From 4dd4d219e4759796794517de418af70358eaaa08 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 22:16:51 -0600
Subject: [PATCH 01/74] chore: open 0.19.0-alpha.1 on develop

---
 plugins/dw/.claude-plugin/plugin.json | 2 +-
 pyproject.toml                        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/plugins/dw/.claude-plugin/plugin.json b/plugins/dw/.claude-plugin/plugin.json
index 8fee25ec..b822ae7d 100644
--- a/plugins/dw/.claude-plugin/plugin.json
+++ b/plugins/dw/.claude-plugin/plugin.json
@@ -1,7 +1,7 @@
 {
     "name": "dw",
     "description": "Compose MiniMax H3 video, MiniMax Music 3, LTX-2.5, Kandinsky 6.0 and FLUX 3 Klein workflows over a dw MCP server, cut a multi-episode series from them, direct camera and look in each family's caption format, and storyboard a scene before it is filmed: which template fits which shape, the hard rules, cost, and how to judge the output. Prompt format comes from the vendors' own guides.",
-    "version": "0.18.0",
+    "version": "0.19.0-alpha.1",
     "author": {
         "name": "Don Kackman"
     },
diff --git a/pyproject.toml b/pyproject.toml
index 61e72373..85f7ce8e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ name = "diffusers-workflow"
 # runtime (static in TOML because importing dw at build time would drag
 # torch into the build environment). Release tags must match it - see
 # docs/RELEASING.md
-version = "0.18.0"
+version = "0.19.0-alpha.1"
 description = "Declarative workflow engine and web UI for Hugging Face Diffusers"
 readme = "README.md"
 requires-python = ">=3.12"

From 1f1cc32f24ca6397c3b9c7d1278f7aff3a363161 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 22:20:04 -0600
Subject: [PATCH 02/74] fix(skill): #1154 - series-episodes keeps cast
 portraits with an extensionless asset_name

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 plugins/dw/skills/series-episodes/SKILL.md | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/plugins/dw/skills/series-episodes/SKILL.md b/plugins/dw/skills/series-episodes/SKILL.md
index 619b30f0..3548c500 100644
--- a/plugins/dw/skills/series-episodes/SKILL.md
+++ b/plugins/dw/skills/series-episodes/SKILL.md
@@ -32,12 +32,14 @@ and the one step (0 below) that keeps the cast fixed; everything past it is
 Draw each character's portrait (and, for a voice that must hold, its
 reference clip) exactly once, in whichever workflow is convenient - a bare
 `templates/minimax/dialogue-short` run works, or any Z-Image step. Then
-`keep_output(name, asset_name="cast/<character>.png", shared=True)` for
-each portrait, and `upload_asset(..., shared=True)` for a voice clip that
+`keep_output(name, asset_name="cast/<character>", shared=True)` for
+each portrait (leave the extension off: the engine appends the file's own, and
+a portrait may be a JPEG or a PNG; cite the `asset:cast/<character>.<ext>`
+that `keep_output` replies with), and `upload_asset(..., shared=True)` for a voice clip that
 did not come from a run. `shared=True` is the whole point: it puts the
 portrait in the asset library every workspace shares
 (`docs/WORKSPACES.md`), so an episode generated in a different workspace,
-weeks later, still resolves `asset:cast/<character>.png` to the same file.
+weeks later, still resolves `asset:cast/<character>.<ext>` to the same file.
 
 Every later episode's `shots` entries reference that cast with `from_file`,
 never `from_previous_result`:

From d185ef33811112efaa6f5048918f9e5216ee5a4c Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 22:29:16 -0600
Subject: [PATCH 03/74] feat: update SKILL documentation and add reference
 files for ltx-2.5, minimax-h3, and series-episodes skills

---
 plugins/dw/skills/ltx-2.5/SKILL.md            | 36 +-----------
 .../dw/skills/ltx-2.5/references/repair.md    | 55 +++++++++++++++++++
 plugins/dw/skills/minimax-h3/SKILL.md         | 28 ++++------
 .../dw/skills/minimax-h3/references/chains.md | 22 ++++++++
 .../minimax-h3/references/watching-a-run.md   | 21 +++++++
 plugins/dw/skills/series-episodes/SKILL.md    | 31 ++---------
 .../references/check-script.md                | 15 +++++
 .../references/dialogue-mix.md                | 28 ++++++++++
 8 files changed, 158 insertions(+), 78 deletions(-)
 create mode 100644 plugins/dw/skills/ltx-2.5/references/repair.md
 create mode 100644 plugins/dw/skills/minimax-h3/references/chains.md
 create mode 100644 plugins/dw/skills/minimax-h3/references/watching-a-run.md
 create mode 100644 plugins/dw/skills/series-episodes/references/check-script.md
 create mode 100644 plugins/dw/skills/series-episodes/references/dialogue-mix.md

diff --git a/plugins/dw/skills/ltx-2.5/SKILL.md b/plugins/dw/skills/ltx-2.5/SKILL.md
index 2ec73019..a4579dae 100644
--- a/plugins/dw/skills/ltx-2.5/SKILL.md
+++ b/plugins/dw/skills/ltx-2.5/SKILL.md
@@ -53,39 +53,9 @@ schedule that is not a knob (except refine-in-place's `strength`, 0 preserve
   a prompt doesn't steer it. `num_frames` caps at 145 (RGB leaks past it).
   A hard cut leaks source texture into the frames after it: matte one shot
   at a time.
-- **Repairing the user's footage**: `templates/ltx2/restore-deblur` for
-  spatial defocus, `templates/ltx2/restore-decompression` for low-bitrate
-  artefacts, `templates/ltx2/restore-colorize` for black-and-white or
-  washed-out footage. All three come back at the source's size and frame
-  rate (`frame_rate` overrides the rate), with no soundtrack. Colorize
-  colours only what the prompt names: look at a source frame
-  (`get_output_frames`) and write both prompt panels for that clip, naming
-  its colours - the default is an example and leaves an unlike clip grey.
-  Reliable on 24 fps 16:9; a low-rate or portrait source may stay grey even
-  so: check a frame of the result; if it is grey, this route doesn't take
-  that source. Each inverts one defect and no other - neither upscales or
-  removes motion blur or grain - so name the defect and let the user
-  correct you. To upscale or sharpen: `templates/ltx2/upscale-clip`
-  (IC-LoRA re-render), `width`/`height` the render size, or `refine-clip`
-  (its own latents, no LoRA), `width`/`height` the working size. Both fit
-  the source first and restore after: output exactly 2x the source (2x the kept region under crop) at its
-  length, any aspect ratio letterboxed and cut back (`fit`), a short source
-  held and trimmed. Soundtrack kept, silent source refused. Same size and length, no 2x:
-  `templates/ltx2/refine-in-place` (`strength` 0-4, default 2). All three
-  write at the source's frame rate (`frame_rate` overrides the rate, never
-  the frame count). A small,
-  smeared face in a wide shot, the rest left alone:
-  `templates/ltx2/face-repair` (`strength` 0-4, default 2; describe the
-  person in `prompt` to hold identity at 3-4). A near face comes back
-  untouched; the source's soundtrack, size and frame rate are kept
-  (`frame_rate` overrides the rate, never the frame count). Footage
-  longer than `num_frames`: `templates/ltx2/restore-long` restores it window
-  by window. Its `windows` list needs `ceil(source_frames / (num_frames -
-  overlap))` entries, `index` 0 up - read `source_frames` from
-  `get_gallery_metadata`, keep `num_frames` 121 and `overlap` 16 (105 new
-  frames per window), and let `validate_workflow` name any entry to add or
-  drop. At most 32 windows. Written at the source's frame rate with its
-  soundtrack (`frame_rate` overrides the rate, never the frame count).
+- **Repairing the user's footage** (deblur, decompression, colorize, a
+  2x upscale or refine of their clip, a small smeared face, a long clip
+  window by window): read `references/repair.md` first.
 - **Longer**: `templates/ltx2/extend-clip` continues an opening conditioned
   on all of it, not one frame; `clip` extends an existing clip (`width`/
   `height` matched, shorter than `num_frames`; clip_frames unused) instead
diff --git a/plugins/dw/skills/ltx-2.5/references/repair.md b/plugins/dw/skills/ltx-2.5/references/repair.md
new file mode 100644
index 00000000..35d5b150
--- /dev/null
+++ b/plugins/dw/skills/ltx-2.5/references/repair.md
@@ -0,0 +1,55 @@
+# Repairing and upscaling the user's footage
+
+Part of the `ltx-2.5` skill: the restore, upscale, refine and face-repair
+templates that take a clip the user supplies.
+
+## One defect each
+
+`templates/ltx2/restore-deblur` for spatial defocus,
+`templates/ltx2/restore-decompression` for low-bitrate artefacts,
+`templates/ltx2/restore-colorize` for black-and-white or washed-out footage.
+All three come back at the source's size and frame rate (`frame_rate`
+overrides the rate), with no soundtrack.
+
+Colorize colours only what the prompt names: look at a source frame
+(`get_output_frames`) and write both prompt panels for that clip, naming its
+colours - the default is an example and leaves an unlike clip grey. Reliable
+on 24 fps 16:9; a low-rate or portrait source may stay grey even so: check a
+frame of the result; if it is grey, this route doesn't take that source.
+
+Each inverts one defect and no other - neither upscales or removes motion
+blur or grain - so name the defect and let the user correct you.
+
+## Upscale or sharpen
+
+`templates/ltx2/upscale-clip` (IC-LoRA re-render), `width`/`height` the
+render size, or `refine-clip` (its own latents, no LoRA), `width`/`height`
+the working size. Both fit the source first and restore after: output
+exactly 2x the source (2x the kept region under crop) at its length, any
+aspect ratio letterboxed and cut back (`fit`), a short source held and
+trimmed. Soundtrack kept, silent source refused.
+
+Same size and length, no 2x: `templates/ltx2/refine-in-place` (`strength`
+0-4, default 2).
+
+All three write at the source's frame rate (`frame_rate` overrides the rate,
+never the frame count).
+
+## A small face
+
+A small, smeared face in a wide shot, the rest left alone:
+`templates/ltx2/face-repair` (`strength` 0-4, default 2; describe the person
+in `prompt` to hold identity at 3-4). A near face comes back untouched; the
+source's soundtrack, size and frame rate are kept (`frame_rate` overrides the
+rate, never the frame count).
+
+## Longer than one window
+
+Footage longer than `num_frames`: `templates/ltx2/restore-long` restores it
+window by window. Its `windows` list needs
+`ceil(source_frames / (num_frames - overlap))` entries, `index` 0 up - read
+`source_frames` from `get_gallery_metadata`, keep `num_frames` 121 and
+`overlap` 16 (105 new frames per window), and let `validate_workflow` name
+any entry to add or drop. At most 32 windows. Written at the source's frame
+rate with its soundtrack (`frame_rate` overrides the rate, never the frame
+count).
diff --git a/plugins/dw/skills/minimax-h3/SKILL.md b/plugins/dw/skills/minimax-h3/SKILL.md
index 44060236..69d80a25 100644
--- a/plugins/dw/skills/minimax-h3/SKILL.md
+++ b/plugins/dw/skills/minimax-h3/SKILL.md
@@ -56,18 +56,14 @@ H3 generates video and audio together. Every template fits a 24 GB card.
   crosses the seam, cut when the scene changes.
 - **As one take (a chain)**: `templates/minimax/chained-segments`
   (default `continuity: "last_frame"`; `continuity: "guide"`, `guide_frames`
-  22 or 39 - default 22 - holds the last frames and voice as a guide instead.
-  The guide prefix is trimmed off the deliverable (`trim_frames` and the
-  crossfade do not apply). `carry_audio` (default true, a chain-block setting)
-  holds the previous tail's audio. Guide only here: the reference chains
-  are ref2va, and guides stay off ref2va),
+  22 or 39 - default 22 - holds the last frames and voice as a guide instead;
+  guide only here),
   `templates/minimax/chain-video-continuity`
   (previous segment's tail rides as a video reference - motion, camera,
   voice carry the seam), `templates/minimax/chain-matched-to-audio` (a track
   sets the length), `templates/minimax/chain-matched-and-aligned`
-  (per-segment prompts). Drift compounds per seam: reference the subject
-  picture each segment, prefer `last_segment`, use the longest segments
-  memory allows.
+  (per-segment prompts). Before composing a chain, read `references/chains.md`:
+  what a guide seam trims and carries, and how to hold drift down.
 - **A piece with cuts**: fresh shots from shared portraits, then a concat.
   `templates/minimax/dialogue-short` (Z-Image draws the cast, one shot per
   `shots` entry, `concat_videos` splices) and `templates/minimax/music-video`
@@ -168,12 +164,10 @@ A wanted look (clay, paper): `references/style.md`. Film-term camera or look: th
    plan's `{fingerprint, minutes, downloads}`.
 3. `wait_for_job` with `timeout_seconds` = the estimate plus a margin
    (`timeout_capped` says the server's cap cut it; call again while
-   `still_running`; on a client "timed out", retry at `timeout_seconds` 60), then `get_job`. A cancelled H3 job runs
-   to its next step boundary. Silence is no hang: `denoise_step` is null
-   at first (encode ~6 s img, ~50 s video; 1st pass
-   dominates: 165-508 s) and the block cache makes later steps uneven -
-   two-minute gaps are healthy; so is a slow first step at a new size (autotune, once a box). `phase_stall` in `get_job_events` narrates
-   it, not a fault; judge by `denoise_step`. Each entry carries `subfolder`:
+   `still_running`; on a client "timed out", retry at `timeout_seconds` 60),
+   then `get_job`. Silence is no hang (encode ~6 s img, ~50 s video; 1st
+   pass 165-508 s), nor are two-minute gaps; judge by `denoise_step`
+   (`references/watching-a-run.md`). Each entry carries `subfolder`:
    `final` is the deliverable (`episode`, `music_video`, `voyage`),
    `intermediate` the scratch.
 4. Judge: `get_output_frames(count=12)` for a clip's shape,
@@ -183,10 +177,8 @@ A wanted look (clay, paper): `references/style.md`. Film-term camera or look: th
    a chain for drift sharpening to noise, and `get_output_audio` for a
    voice-over without affect. It returns sound, not text; to confirm a
    take's lines, run `templates/check-script` with them as `lines` (markup
-   and all) and read its `findings` with `get_output_text`. Dialogue gaps (`shot_dead_air`) need room tone:
-   `find_loop_bed` the `output:` cut, then `slice_audio`->`loop_audio`->`mix_audio`
-   its pick at its `gain`.
-   Then `get_gallery_metadata` for duration/audio and hand the
+   and all) and read its `findings` with `get_output_text`. Dialogue gaps (`shot_dead_air`) need room tone,
+   in that reference. Then `get_gallery_metadata` for duration/audio and hand the
    user the gallery `url`.
 5. After a run worth keeping, `get_job_workflow` and `save_workflow` it;
    `export_job` bundles it on the server. `auth_required: false` - fetch
diff --git a/plugins/dw/skills/minimax-h3/references/chains.md b/plugins/dw/skills/minimax-h3/references/chains.md
new file mode 100644
index 00000000..ceed2090
--- /dev/null
+++ b/plugins/dw/skills/minimax-h3/references/chains.md
@@ -0,0 +1,22 @@
+# H3 chains
+
+Part of the `minimax-h3` skill: what a chain carries across a seam, and how to
+keep it from drifting.
+
+## A guide seam
+
+`templates/minimax/chained-segments` with `continuity: "guide"` holds the
+previous segment's last `guide_frames` and its voice as a guide.
+
+- The guide prefix is trimmed off the deliverable, so `trim_frames` and the
+  crossfade do not apply.
+- `carry_audio` (default true, a chain-block setting) holds the previous
+  tail's audio.
+- Guide continuity exists only on this template. The reference chains
+  (`chain-video-continuity`, `chain-matched-to-audio`,
+  `chain-matched-and-aligned`) are ref2va, and guides stay off ref2va.
+
+## Drift
+
+Drift compounds per seam. Reference the subject picture in each segment,
+prefer `last_segment`, and use the longest segments memory allows.
diff --git a/plugins/dw/skills/minimax-h3/references/watching-a-run.md b/plugins/dw/skills/minimax-h3/references/watching-a-run.md
new file mode 100644
index 00000000..55844e5a
--- /dev/null
+++ b/plugins/dw/skills/minimax-h3/references/watching-a-run.md
@@ -0,0 +1,21 @@
+# Watching an H3 run
+
+Part of the `minimax-h3` skill: what a healthy run looks like while it is
+running, and the dead-air fix after it finishes.
+
+## Silence is no hang
+
+- `denoise_step` is null at first, through the encode and into the first
+  pass, which takes the most time (the measured figures are in the skill).
+- The block cache makes later steps uneven, so two-minute gaps are healthy.
+  A slow first step at a new size is healthy too: autotune runs once per
+  box.
+- `phase_stall` in `get_job_events` describes the wait; it does not mean a
+  fault. Judge progress by `denoise_step`.
+- A cancelled H3 job runs on to its next step boundary before it stops.
+
+## Dialogue gaps need room tone
+
+A `shot_dead_air` finding means a gap in a voiced shot is digital silence.
+Run `find_loop_bed` on the `output:` cut, then
+`slice_audio`->`loop_audio`->`mix_audio` its pick at its `gain`.
diff --git a/plugins/dw/skills/series-episodes/SKILL.md b/plugins/dw/skills/series-episodes/SKILL.md
index 903b2af2..72155109 100644
--- a/plugins/dw/skills/series-episodes/SKILL.md
+++ b/plugins/dw/skills/series-episodes/SKILL.md
@@ -115,23 +115,9 @@ composes into, not something to re-derive:
 - **match_levels**: shots generated independently drift in loudness -
   `assemble-and-score`'s `match_levels` (`"rms"` or `"peak"`) evens them
   before the cut; leaving it null only warns on a wide spread instead of
-  fixing it. A shot whose voice-over the score buries is a different
-  problem, not fixed by `match_levels` or `world_gain` (which lifts the
-  whole world track, action sound included). Either duck the score -
-  `minimax-h3`'s ducking recipe: `gain_audio` regions on the score, one per
-  voice-over shot, applied before the score is passed in - or give dialogue
-  its own gain. For that, in a saved copy of `assemble-and-score`, add a
-  `separate_stems` step on `world_faded`; `previous_result:<step>.vocals` is
-  the dialogue, and a `mix_audio` of its `drums`, `bass` and `other` is the
-  rest (room tone, foley, action sound). Then have `mixed` layer
-  `[soundtrack_resampled, dialogue, rest]`, one gain each, and keep
-  `balanced` after it: a dialogue gain above 1 on a stem peaking near full
-  scale sums past 0 dBFS, which `balanced` pulls back to -3. htdemucs keeps
-  H3 speech intact this way (#1091). A shot with little foley leaves a
-  near-silent rest, and a quiet unpitched bed (room tone, ambience) lands
-  mostly in `vocals`, not the rest. Separating an already-scored mix works
-  too, but its rest is the score plus the shots' foley, not a clean score:
-  keep the original score file to re-mix or replace it.
+  fixing it. A voice-over the score buries is a different problem, not
+  fixed by `match_levels` or `world_gain`: duck the score or give dialogue
+  its own gain, per `references/dialogue-mix.md`.
 - **sound effects**: `video-to-sound-effects` (or `text-to-sound-effect`) makes
   effects for the `rest` bed; MMAudio runs 7-25 dB hot, so `match_levels` them first.
 - **normalize**: the mixed world sound and score are normalized together
@@ -185,16 +171,7 @@ people - the failure mode step 0 exists to prevent. Measure it per shot with
 `integrated_lufs` (`get_gallery_metadata`), not `peak_dbfs`; the second
 half of `references/loudness.md` reads the gap. To confirm every
 line rendered rather than judging it by ear, run `templates/check-script`
-against the episode's script: `validate_workflow(name="templates/check-script",
-arguments={"input_audio": "output:<name>", "lines": [...]})` first (free; it
-takes the episode's muxed soundtrack directly, and `lines` is the script in
-order, `{text, shot}` to name each line's shot), then `run_workflow(...,
-acknowledged_cost=<that plan's {fingerprint, minutes, downloads}>,
-wait_seconds=60)`, then `get_output_text` for its `findings` - a dropped or
-changed line, speech in a silent shot, a clipped last word - and
-`delete_output(job_id=...)` the scratch run afterward. This workflow's
-plan is `basis: "unknown"` with `minutes: null` - quote seconds, not
-minutes.
+against the episode's script, per `references/check-script.md`.
 
 To take the project home, call `export_job` once per job of the project (each
 episode, and the cast run), then fetch each zip's `open_url` and unpack it into
diff --git a/plugins/dw/skills/series-episodes/references/check-script.md b/plugins/dw/skills/series-episodes/references/check-script.md
new file mode 100644
index 00000000..e6ba3c05
--- /dev/null
+++ b/plugins/dw/skills/series-episodes/references/check-script.md
@@ -0,0 +1,15 @@
+# Checking an episode's lines
+
+Part of the `series-episodes` skill: confirming every line rendered with
+`templates/check-script` rather than judging it by ear.
+
+1. `validate_workflow(name="templates/check-script",
+   arguments={"input_audio": "output:<name>", "lines": [...]})` first. It is
+   free, and it takes the episode's muxed soundtrack directly. `lines` is the
+   script in order, `{text, shot}` to name each line's shot.
+2. `run_workflow(..., acknowledged_cost=<that plan's {fingerprint, minutes,
+   downloads}>, wait_seconds=60)`. This workflow's plan is
+   `basis: "unknown"` with `minutes: null`, so quote seconds, not minutes.
+3. `get_output_text` for its `findings`: a dropped or changed line, speech in
+   a silent shot, a clipped last word.
+4. `delete_output(job_id=...)` the scratch run afterward.
diff --git a/plugins/dw/skills/series-episodes/references/dialogue-mix.md b/plugins/dw/skills/series-episodes/references/dialogue-mix.md
new file mode 100644
index 00000000..a0524599
--- /dev/null
+++ b/plugins/dw/skills/series-episodes/references/dialogue-mix.md
@@ -0,0 +1,28 @@
+# A voice-over the score buries
+
+Part of the `series-episodes` skill. A shot whose voice-over the score buries
+is not fixed by `match_levels` or `world_gain` (which lifts the whole world
+track, action sound included). Do one of these two things instead.
+
+## Duck the score
+
+Use `minimax-h3`'s ducking recipe: `gain_audio` regions on the score, one per
+voice-over shot, applied before the score is passed in.
+
+## Give dialogue its own gain
+
+In a saved copy of `assemble-and-score`, add a `separate_stems` step on
+`world_faded`. `previous_result:<step>.vocals` is the dialogue, and a
+`mix_audio` of its `drums`, `bass` and `other` is the rest (room tone, foley,
+action sound). Then have `mixed` layer
+`[soundtrack_resampled, dialogue, rest]`, one gain each, and keep `balanced`
+after it: a dialogue gain above 1 on a stem peaking near full scale sums past
+0 dBFS, which `balanced` pulls back to -3.
+
+htdemucs keeps H3 speech intact this way (#1091). A shot with little foley
+leaves a near-silent rest, and a quiet unpitched bed (room tone, ambience)
+lands mostly in `vocals`, not the rest.
+
+Separating an already-scored mix works too, but its rest is the score plus
+the shots' foley, not a clean score: keep the original score file to re-mix
+or replace it.

From c7d00dcec9b0dfa55ce79f333297ec014fd97e2a Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 23:35:01 -0600
Subject: [PATCH 04/74] fix(plugin): #1157 - sitcom-multicam master sets
 camera_move: static

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 .../dw/skills/series-episodes/references/sitcom-multicam.md    | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/plugins/dw/skills/series-episodes/references/sitcom-multicam.md b/plugins/dw/skills/series-episodes/references/sitcom-multicam.md
index a722d28a..f6940c7a 100644
--- a/plugins/dw/skills/series-episodes/references/sitcom-multicam.md
+++ b/plugins/dw/skills/series-episodes/references/sitcom-multicam.md
@@ -8,7 +8,8 @@ below are this repo's.
 ## The grammar
 
 - **Master**: one wide take of the whole scene, held start to finish. Its shot
-  fields are `shot_size: wide`. It carries the room, the blocking and the full
+  fields are `shot_size: wide` and `camera_move: static` (no push, dolly or
+  pan, so each single cuts back to an unmoved master). It carries the room, the blocking and the full
   dialogue audio, and is the fallback for anything below.
 - **Singles**: one `templates/minimax/dialogue-short` clip per speaker line or
   run of lines, `shot_size: medium-close` or `close-up`, the speaker facing

From 285b537525fc86daebe69679273c3f8aa2ddbb85 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 23:38:56 -0600
Subject: [PATCH 05/74] feat(plugin): #1142 - one light phrase per value in the
 LTX-2.5, H3 and Klein compile tables

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../cinematography/references/compile-flux3-klein.md      | 8 +++++++-
 plugins/dw/skills/cinematography/references/compile-h3.md | 7 ++++++-
 .../skills/cinematography/references/compile-ltx-2.5.md   | 8 ++++++--
 3 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/plugins/dw/skills/cinematography/references/compile-flux3-klein.md b/plugins/dw/skills/cinematography/references/compile-flux3-klein.md
index 1529bae6..050cedcb 100644
--- a/plugins/dw/skills/cinematography/references/compile-flux3-klein.md
+++ b/plugins/dw/skills/cinematography/references/compile-flux3-klein.md
@@ -60,7 +60,13 @@ In the caption, as visual detail:
   compression with creamy background blur", "anamorphic widescreen with
   oval bokeh and horizontal flares", "soft vintage-lens glow". Name the
   effect, not a lens product.
-- **light**: the `vocabulary.md` meaning in observable words.
+- **light**: one phrase per value, in observable words - `high-key`
+  "bright, even high-key light with almost no shadows", `low-key` "a single
+  hard light source, most of the frame in deep shadow", `golden-hour` "low
+  warm golden-hour sun, long shadows, a glowing rim light on <subject>",
+  `practical` "lit only by the lamps, screens and neon signs in the scene",
+  `noir` "film noir lighting, hard contrast, venetian-blind shadows across
+  <subject>, a haze of smoke".
 - **era**: "a grainy 8mm film frame", "a 16mm film still", "a 35mm film
   still", "a VHS video frame", "a clean digital photograph".
 - **tempo**: only through motion blur - `still` and `measured` none,
diff --git a/plugins/dw/skills/cinematography/references/compile-h3.md b/plugins/dw/skills/cinematography/references/compile-h3.md
index 3668f5cb..c3e718c8 100644
--- a/plugins/dw/skills/cinematography/references/compile-h3.md
+++ b/plugins/dw/skills/cinematography/references/compile-h3.md
@@ -64,7 +64,12 @@ In the opening of the shot, with the style:
 - **lens**: the effect - "wide-angle", "long-lens compression with a soft
   background", "anamorphic widescreen, horizontal flares", "soft vintage
   glass".
-- **light**: the `vocabulary.md` meaning, in the visual description.
+- **light**: one phrase per value, in the visual description - `high-key`
+  "bright, even high-key lighting, few shadows", `low-key` "low-key
+  lighting from one hard source, deep shadows", `golden-hour` "warm
+  golden-hour sunlight, long shadows, glowing rim light", `practical` "lit
+  only by the lamps, screens and neon in the scene", `noir` "film noir
+  lighting, hard contrast, venetian-blind shadows, smoke, night".
 - **era**: "shot on grainy 8mm home-movie film", "16mm documentary film",
   "35mm film", "VHS camcorder footage", "clean digital video". For a medium
   (clay, paper) the `minimax-h3` skill's `style.md` reference applies too.
diff --git a/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md b/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md
index 1953976f..74919b5a 100644
--- a/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md
+++ b/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md
@@ -69,8 +69,12 @@ and texture, and in the spec's aesthetic-quality descriptors:
   stretches the edges", "compressed depth and a shallow focus", "a
   widescreen frame with oval bokeh and horizontal flares", "soft, glowing
   vintage glass". Say the effect, not a lens name.
-- **light**: the `vocabulary.md` meaning in observable words ("a single hard
-  lamp leaves most of the room in deep shadow").
+- **light**: one phrase per value, in observable words - `high-key` "bright,
+  even light with almost no shadows", `low-key` "a single hard lamp leaves
+  most of the room in deep shadow", `golden-hour` "low, warm sunlight casts
+  long shadows and a glowing rim around <subject>", `practical` "lit only by
+  the lamps, screens and signs in the scene", `noir` "hard-contrast night
+  light, venetian-blind shadows striping the wall, a haze of smoke".
 - **era**: "grainy 8mm home-movie footage", "16mm documentary film grain",
   "35mm film with fine grain", "soft VHS camcorder footage", "clean digital
   video".

From b96ccf1dd50a98069289d2ddb70090281ac85a5b Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 23:52:43 -0600
Subject: [PATCH 06/74] fix(plugin): #1157 - sitcom-multicam master uses the
 vocabulary's move key, not camera_move

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 plugins/dw/skills/series-episodes/references/sitcom-multicam.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/plugins/dw/skills/series-episodes/references/sitcom-multicam.md b/plugins/dw/skills/series-episodes/references/sitcom-multicam.md
index f6940c7a..99a12126 100644
--- a/plugins/dw/skills/series-episodes/references/sitcom-multicam.md
+++ b/plugins/dw/skills/series-episodes/references/sitcom-multicam.md
@@ -8,7 +8,7 @@ below are this repo's.
 ## The grammar
 
 - **Master**: one wide take of the whole scene, held start to finish. Its shot
-  fields are `shot_size: wide` and `camera_move: static` (no push, dolly or
+  fields are `shot_size: wide` and `move: static` (no push, dolly or
   pan, so each single cuts back to an unmoved master). It carries the room, the blocking and the full
   dialogue audio, and is the fallback for anything below.
 - **Singles**: one `templates/minimax/dialogue-short` clip per speaker line or

From 6f8e12eb9c4a8209ae4ef9e4c3b23de7cdb454c1 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Sun, 1 Nov 2026 23:59:11 -0600
Subject: [PATCH 07/74] fix(tasks): #1144 - stills_to_video: silent track so
 the clip scores, images confined at validate

- The clip carries a silent mono 48 kHz track of its own length instead of
  none: assemble-and-score's world step (resample_audio on the joined cut)
  refused a video with no track (C-F645)
- 'images' is a media argument, so each entry that names a file is confined
  at validate_workflow like concat_videos' 'videos' (SE-F060)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/TASKS.md                 |  8 ++++--
 dw/tasks/video_handlers.py    |  1 +
 dw/tasks/video_utils.py       | 29 +++++++++++++++-----
 tests/test_locations.py       | 51 +++++++++++++++++++++++++++++++++++
 tests/test_stills_to_video.py | 35 ++++++++++++++++++++----
 5 files changed, 111 insertions(+), 13 deletions(-)

diff --git a/docs/TASKS.md b/docs/TASKS.md
index 37dd665a..e3de2605 100644
--- a/docs/TASKS.md
+++ b/docs/TASKS.md
@@ -573,8 +573,12 @@ frames in total; those cap its length, not its memory. The frames are held as
 uint8 pixels, so the command also refuses, before allocating, a clip over
 4 GiB (frames x width x height x 3), naming `durations`, `fps` and the image
 size.
-Stills of different sizes are refused, naming both sizes. The video has no
-audio track; its `shots` record where each still starts.
+Stills of different sizes are refused, naming both sizes. The video carries a
+silent mono track (48 kHz) of its own length, so it joins and scores like a
+generated shot - `assemble-and-score` reads the shots' track for its world
+audio and refuses a video with none; its `shots` record where each still
+starts. Each entry of `images` that names a file is confined at
+`validate_workflow` like any media location.
 `validate_workflow` catches a non-positive duration, an `fps` out of range, a
 duration count that does not match a literal image list, and a total over 600
 seconds or 36000 frames (the 4 GiB limit depends on the image size, so it is
diff --git a/dw/tasks/video_handlers.py b/dw/tasks/video_handlers.py
index 70aa7313..6bb157fa 100644
--- a/dw/tasks/video_handlers.py
+++ b/dw/tasks/video_handlers.py
@@ -131,6 +131,7 @@ def _handle_loop_frames(task, arguments, previous_pipelines):
     implementation="dw.tasks.video_utils.stills_to_video",
     domains={"fps": POSITIVE},
     static_check=stills_to_video_errors,
+    media_arguments=("images",),
 )
 def _handle_stills_to_video(task, arguments, previous_pipelines):
     """Hold each still for a duration, as one silent clip"""
diff --git a/dw/tasks/video_utils.py b/dw/tasks/video_utils.py
index 95713f4c..c1276f42 100644
--- a/dw/tasks/video_utils.py
+++ b/dw/tasks/video_utils.py
@@ -32,7 +32,12 @@
     fit_codec_padding,
 )
 from ..shots import shot_record
-from ..task_domains import frame_size_error, real_number, whole_number
+from ..task_domains import (
+    frame_size_error,
+    frames_to_samples,
+    real_number,
+    whole_number,
+)
 from ..task_problems import (  # noqa: F401
     MAX_STILLS_FPS,
     MAX_STILLS_SECONDS,
@@ -42,6 +47,13 @@
 
 logger = logging.getLogger("dw")
 
+# The rate of stills_to_video's silent track. A track of zeros rather than
+# none, so the clip scores like a generated shot: the world-audio path of
+# assemble-and-score (resample_audio on the joined cut) refuses a video that
+# carries no track at all. Mono, which concat_videos tiles up to a stereo
+# neighbour's channels
+STILLS_SAMPLE_RATE = 48000
+
 # A location that names a scheme is a URL, whatever the scheme
 _URL_SCHEME = re.compile(r"^[a-zA-Z][a-zA-Z0-9+.\-]*://")
 
@@ -259,9 +271,10 @@ def stills_to_video(images, durations, fps=24):
     """Task command: storyboard stills held on screen for a duration each, as
     one silent clip - an animatic.
 
-    The clip has no audio track (`audio=None`); `concat_videos` fills a
-    silent input with silence and `pair_audio` lays a score under it, so it
-    joins and scores like any generated shot.
+    The clip carries a silent mono track at STILLS_SAMPLE_RATE, its own
+    length, so it joins and scores like any generated shot: `concat_videos`
+    and the audio commands that read a video's track (`resample_audio` in
+    assemble-and-score) take it, and `pair_audio` lays a score in its place.
 
     Args:
         images: The stills, in order - PIL images or arrays. As a task
@@ -277,7 +290,7 @@ def stills_to_video(images, durations, fps=24):
 
     Returns:
         An AudioVideo of uint8 (frames, height, width, 3) frames at `fps`,
-        with one shot record per still
+        with a silent track and one shot record per still
     """
     if isinstance(images, numpy.ndarray) and images.ndim == 4:
         images = list(images)
@@ -325,7 +338,11 @@ def stills_to_video(images, durations, fps=24):
         frames[start : start + count] = numpy.asarray(still.convert("RGB"))
         shots.append(shot_record(f"still_{index}", start, count))
         start += count
-    return AudioVideo(frames, None, None, fps=fps, shots=shots)
+    silence = numpy.zeros(
+        (1, frames_to_samples(len(frames), fps, STILLS_SAMPLE_RATE)),
+        dtype=numpy.float32,
+    )
+    return AudioVideo(frames, silence, STILLS_SAMPLE_RATE, fps=fps, shots=shots)
 
 
 def frame_grid(video, count=12, columns=None, tile_width=320, label=True):
diff --git a/tests/test_locations.py b/tests/test_locations.py
index 95665e1a..6e93957d 100644
--- a/tests/test_locations.py
+++ b/tests/test_locations.py
@@ -932,6 +932,57 @@ def test_references_are_left_alone(self, untrusted, workflow_dir, command):
         assert location_errors(definition, base_dir=workflow_dir) == []
 
 
+class TestStillsImagesArgument:
+    """stills_to_video's `images` list - a plural no media key matches - is
+    confined at validation entry by entry, as the joining commands' `videos`
+    is (#1144)."""
+
+    @staticmethod
+    def _step(images):
+        return {
+            "steps": [
+                {
+                    "name": "probe",
+                    "task": {
+                        "command": "stills_to_video",
+                        "arguments": {"images": images, "durations": 1},
+                    },
+                }
+            ]
+        }
+
+    @pytest.mark.parametrize(
+        "value",
+        [
+            "/etc/hostname",
+            "../../../../etc/hostname",
+            "file:///etc/hostname",
+        ],
+    )
+    def test_an_unreadable_entry_is_an_error_at_its_index(
+        self, untrusted, workflow_dir, value
+    ):
+        errors = location_errors(
+            self._step(["asset:a.png", value]), base_dir=workflow_dir
+        )
+
+        assert [error["path"] for error in errors] == [
+            "steps[0].task.arguments.images[1]"
+        ]
+        assert "'images'" in errors[0]["message"]
+
+    def test_a_scalar_is_checked_too(self, untrusted, workflow_dir):
+        errors = location_errors(self._step("/etc/hostname"), base_dir=workflow_dir)
+
+        assert [error["path"] for error in errors] == ["steps[0].task.arguments.images"]
+
+    def test_references_are_left_alone(self, untrusted, workflow_dir):
+        definition = self._step(
+            ["asset:a.png", "output:wf/run/b.png", "previous_result:c"]
+        )
+        assert location_errors(definition, base_dir=workflow_dir) == []
+
+
 class TestOwnOutputHint:
     """#951: a bare manifest file name is refused with `output:` as the fix."""
 
diff --git a/tests/test_stills_to_video.py b/tests/test_stills_to_video.py
index e6f23a72..26db7ebc 100644
--- a/tests/test_stills_to_video.py
+++ b/tests/test_stills_to_video.py
@@ -5,7 +5,12 @@
 from PIL import Image
 
 from dw.media_types import AudioVideo
-from dw.tasks.video_utils import MAX_STILLS_FPS, MAX_STILLS_SECONDS, stills_to_video
+from dw.tasks.video_utils import (
+    MAX_STILLS_FPS,
+    MAX_STILLS_SECONDS,
+    STILLS_SAMPLE_RATE,
+    stills_to_video,
+)
 
 
 def still(width=512, height=512, color=(255, 0, 0)):
@@ -42,10 +47,11 @@ def test_arrays_are_accepted(self):
         video = stills_to_video([array], 1, fps=4)
         assert video.frames.shape == (4, 64, 64, 3)
 
-    def test_silent_clip_carries_no_audio(self):
-        video = stills_to_video([still()], 1, fps=4)
-        assert video.audio is None
-        assert video.sample_rate is None
+    def test_clip_carries_a_silent_track_of_its_own_length(self):
+        video = stills_to_video([still(), still()], [1, 0.5], fps=24)
+        assert video.sample_rate == STILLS_SAMPLE_RATE
+        assert video.audio.shape == (1, round(36 / 24 * STILLS_SAMPLE_RATE))
+        assert not video.audio.any()
 
 
 class TestRefusals:
@@ -92,6 +98,25 @@ def test_result_concats_with_concat_videos(self):
         joined = concat_videos([video, video], fps=4)
         assert len(joined.frames) == 2 * len(video.frames)
 
+    def test_clip_scores_through_the_assemble_and_score_chain(self):
+        """assemble-and-score's own steps, in order: the joined cut's track is
+        resampled as the world audio, mixed under the score and paired back
+        onto the cut - a stills clip must not stop at resample_audio."""
+        from dw.tasks.audio_utils import mix_audio, resample_audio
+        from dw.tasks.concat_videos import concat_videos
+        from dw.tasks.pair_audio import pair_audio
+
+        video = stills_to_video([still(64, 64)] * 3, [1, 2, 0.5], fps=24)
+        cut = concat_videos([video], fps=24, sample_rate=44100)
+        world = resample_audio(cut, 44100)
+        score = numpy.full((2, round(3.5 * 44100)), 0.1, dtype=numpy.float32)
+        mixed = mix_audio([score, world], gains=[1.0, 1.8], sample_rate=44100)
+        film = pair_audio(cut, mixed, sample_rate=44100, fit="video")
+
+        assert len(film.frames) == 84
+        assert film.audio.shape[-1] == round(3.5 * 44100)
+        assert film.audio.any()
+
     def test_silent_clip_saves_as_a_video(self, tmp_path):
         from dw.result import Result
 

From 0bf7d99da0fbdd51a5cbc09b2c9cfb862c31064e Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 00:12:49 -0600
Subject: [PATCH 08/74] fix(tasks): #1161 - stills_to_video's silent track
 raises no near_silent or mono-duplication warning

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 dw/audio_qc.py                |  6 +++-
 dw/result.py                  |  9 ++++--
 dw/server/assess.py           | 19 ++++++++++--
 dw/server/routes/media.py     |  2 +-
 dw/tasks/video_utils.py       |  8 +++--
 dw/writers.py                 | 17 ++++++-----
 tests/test_stills_to_video.py | 55 +++++++++++++++++++++++++++++++++++
 7 files changed, 100 insertions(+), 16 deletions(-)

diff --git a/dw/audio_qc.py b/dw/audio_qc.py
index 9631434c..062ce9ca 100644
--- a/dw/audio_qc.py
+++ b/dw/audio_qc.py
@@ -436,6 +436,10 @@ def check_written_media(
         output_path,
         info=probed_info,
         source_already_quiet=(
-            source_mean_dbfs is not None and source_mean_dbfs < NEAR_SILENT_WARN_DBFS
+            getattr(artifact, "silent_by_design", False) is True
+            or (
+                source_mean_dbfs is not None
+                and source_mean_dbfs < NEAR_SILENT_WARN_DBFS
+            )
         ),
     )
diff --git a/dw/result.py b/dw/result.py
index 94fdd0e9..5b0bfd77 100644
--- a/dw/result.py
+++ b/dw/result.py
@@ -822,7 +822,10 @@ def save_audio_video(self, artifact, output_path, content_type):
 
             audio = None
             if artifact.audio is not None and sample_rate is not None:
-                audio = as_audio_track(artifact.audio)
+                audio = as_audio_track(
+                    artifact.audio,
+                    quiet=getattr(artifact, "silent_by_design", False) is True,
+                )
                 self._predicted_peak_dbfs = warn_without_headroom(
                     artifact.audio, os.path.basename(output_path), emit=False
                 )
@@ -882,7 +885,9 @@ def save_audio_video(self, artifact, output_path, content_type):
             frames_for_encoding(artifact.frames),
             fps=fps,
             output_path=output_path,
-            audio=as_audio_track(audio),
+            audio=as_audio_track(
+                audio, quiet=getattr(artifact, "silent_by_design", False) is True
+            ),
             audio_sample_rate=sample_rate,
         )
 
diff --git a/dw/server/assess.py b/dw/server/assess.py
index 844e6b30..8f10e19e 100644
--- a/dw/server/assess.py
+++ b/dw/server/assess.py
@@ -126,7 +126,7 @@ def assess(path, kind, shots, probe=None, detail=False):
     # The whole-file level rules (full_scale, near_silent) are the gallery's
     # too (#807); a file with no soundtrack has no level to read
     level = media is not None and media.audio is not None
-    level_found = level_findings(_file_level(media)) if level else []
+    level_found = level_findings(_file_level(media), records) if level else []
     body = {
         "shots_source": source,
         "findings": sort_findings(
@@ -152,7 +152,16 @@ def assess(path, kind, shots, probe=None, detail=False):
 __all__ = ["PROBES", "assess", "unknown_probe"]
 
 
-def level_findings(media):
+def silent_by_design(shots):
+    """Whether every recorded shot says its track is silent on purpose
+    (stills_to_video's zeros, #1161)."""
+    return bool(shots) and all(
+        isinstance(shot, dict) and shot.get("silent_by_design") is True
+        for shot in shots
+    )
+
+
+def level_findings(media, shots=None):
     """The level problems a probed file's soundtrack shows, as findings:
     a peak at full scale (an encode may clip it) and a near-silent mean
     (info when real peaks say quiet-not-empty, as an ambience-only shot
@@ -176,7 +185,11 @@ def level_findings(media):
             "re-render",
         }
         found.append(finding(rule, peak, None))
-    if mean is not None and mean < audio_qc.NEAR_SILENT_WARN_DBFS:
+    if (
+        mean is not None
+        and mean < audio_qc.NEAR_SILENT_WARN_DBFS
+        and not silent_by_design(shots)
+    ):
         quiet_not_empty = (
             peak is not None and peak >= audio_qc.NEAR_SILENT_QUIET_NOT_EMPTY_DBFS
         )
diff --git a/dw/server/routes/media.py b/dw/server/routes/media.py
index 677c521b..79efdfb3 100644
--- a/dw/server/routes/media.py
+++ b/dw/server/routes/media.py
@@ -250,7 +250,7 @@ def gallery_metadata(
         "run_id": run_id,
         "version": version,
         "media": media,
-        "findings": level_findings(media),
+        "findings": level_findings(media, (media or {}).get("shots")),
         "role": role,
         "inputs": inputs,
         **notes,
diff --git a/dw/tasks/video_utils.py b/dw/tasks/video_utils.py
index c1276f42..8b547fca 100644
--- a/dw/tasks/video_utils.py
+++ b/dw/tasks/video_utils.py
@@ -336,13 +336,17 @@ def stills_to_video(images, durations, fps=24):
     shots, start = [], 0
     for index, (still, count) in enumerate(zip(stills, counts), start=1):
         frames[start : start + count] = numpy.asarray(still.convert("RGB"))
-        shots.append(shot_record(f"still_{index}", start, count))
+        shots.append(shot_record(f"still_{index}", start, count, silent_by_design=True))
         start += count
     silence = numpy.zeros(
         (1, frames_to_samples(len(frames), fps, STILLS_SAMPLE_RATE)),
         dtype=numpy.float32,
     )
-    return AudioVideo(frames, silence, STILLS_SAMPLE_RATE, fps=fps, shots=shots)
+    clip = AudioVideo(frames, silence, STILLS_SAMPLE_RATE, fps=fps, shots=shots)
+    # The zeros are the design (#1161): no near-silent or mono-duplication
+    # warning at save time, and the shot records say so for the gallery
+    clip.silent_by_design = True
+    return clip
 
 
 def frame_grid(video, count=12, columns=None, tile_width=320, label=True):
diff --git a/dw/writers.py b/dw/writers.py
index 17bfb10d..bbfd8ad6 100644
--- a/dw/writers.py
+++ b/dw/writers.py
@@ -307,7 +307,7 @@ def normalize_audio(artifact):
     raise ValueError(f"Cannot save audio with shape {waveform.shape}")
 
 
-def as_audio_track(audio):
+def as_audio_track(audio, quiet=False):
     """Convert a generated waveform into the tensor encode_video expects.
 
     encode_video wants a float torch tensor on the CPU shaped (channels, samples) -
@@ -321,6 +321,8 @@ def as_audio_track(audio):
 
     Args:
         audio: Waveform as a torch tensor or numpy array
+        quiet: Skip the duplication warning, for a track the engine itself
+            made mono (stills_to_video's silent one, #1161)
 
     Returns:
         Float CPU torch tensor holding the waveform, with at least 2 channels
@@ -329,10 +331,10 @@ def as_audio_track(audio):
         audio = torch.from_numpy(numpy.asarray(audio))
 
     audio = audio.detach().float().cpu()
-    return _as_stereo(audio)
+    return _as_stereo(audio, quiet)
 
 
-def _as_stereo(audio):
+def _as_stereo(audio, quiet=False):
     """Duplicate a mono waveform into two channels, leaving anything else alone.
 
     Orientation is read the way encode_video reads it: (samples,) and (1, samples)
@@ -353,10 +355,11 @@ def _as_stereo(audio):
     elif audio.ndim != 1:
         return audio
 
-    emit_warning(
-        f"Duplicating a mono audio track of {audio.shape[0]} samples into two "
-        f"channels - an mp4 audio stream takes stereo and nothing else"
-    )
+    if not quiet:
+        emit_warning(
+            f"Duplicating a mono audio track of {audio.shape[0]} samples into two "
+            f"channels - an mp4 audio stream takes stereo and nothing else"
+        )
     return audio.unsqueeze(0).repeat(2, 1)
 
 
diff --git a/tests/test_stills_to_video.py b/tests/test_stills_to_video.py
index 26db7ebc..40ddaba7 100644
--- a/tests/test_stills_to_video.py
+++ b/tests/test_stills_to_video.py
@@ -255,3 +255,58 @@ def test_saved_shots_start_frames(self, tmp_path):
         assert list(tmp_path.glob("animatic.0*.mp4"))
         shots = step_shots(result.saved_shots, saved)
         assert [shot["start_frame"] for shot in shots] == [0, 24, 72]
+
+
+class TestSilentByDesign:
+    """The silent track raises no near-silent or mono-duplication warning (#1161)."""
+
+    def test_clip_and_shots_are_marked(self):
+        video = stills_to_video([still(64, 64)], [1], fps=24)
+        assert video.silent_by_design is True
+        assert all(shot["silent_by_design"] for shot in video.shots)
+
+    def test_gallery_level_skips_near_silent_for_marked_shots(self):
+        from dw.server.assess import level_findings
+
+        media = {"peak_dbfs": -120.0, "mean_dbfs": -120.0}
+        video = stills_to_video([still(64, 64)], [1], fps=24)
+        assert level_findings(media, video.shots) == []
+        names = [f["rule"] for f in level_findings(media)]
+        assert "near_silent" in names
+
+    def test_mono_duplication_quiet_only_when_asked(self, monkeypatch):
+        import dw.writers as writers
+
+        seen = []
+        monkeypatch.setattr(writers, "emit_warning", lambda *a, **k: seen.append(a))
+        mono = numpy.zeros((1, 100), dtype=numpy.float32)
+        writers.as_audio_track(mono, quiet=True)
+        assert seen == []
+        writers.as_audio_track(mono)
+        assert len(seen) == 1
+
+    def test_write_time_near_silent_skipped(self, monkeypatch):
+        import dw.audio_qc as qc
+
+        calls = []
+        monkeypatch.setattr(
+            qc,
+            "_probe_written_media",
+            lambda p: {"mean_dbfs": -120.0, "peak_dbfs": -120.0},
+        )
+        monkeypatch.setattr(qc, "emit_warning", lambda *a, **k: calls.append(a))
+        monkeypatch.setattr(qc, "remeasure_shots_after_mux", lambda *a, **k: None)
+        monkeypatch.setattr(
+            qc, "warn_if_written_above_full_scale", lambda *a, **k: None
+        )
+        video = stills_to_video([still(64, 64)], [1], fps=24)
+        qc.check_written_media(
+            "x.mp4",
+            video,
+            "video/mp4",
+            video_fps=None,
+            consumed_by_normalizer=False,
+            headroom_warned=False,
+            predicted_peak_dbfs=None,
+        )
+        assert calls == []

From 18dd9390e4466d9cfa4f44de2e218b2e46906369 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 00:54:55 -0600
Subject: [PATCH 09/74] feat(plugin): #1148 - measure six named moves per video
 family

Ran static, dolly in, dolly out, truck left, crane up and orbit on H3,
LTX-2.5 and Kandinsky 6 at seed 42 on one fixed subject, judged each clip
by its frames, and recorded the 18 verdicts: the cinematography skill's
table, a measured column in each compile-*.md move table, and the job ids
and frame evidence in docs/proposals/audits/2026-11-higgsfield/move-trial.md.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../audits/2026-11-higgsfield/move-trial.md   | 121 ++++++++++++++++++
 plugins/dw/skills/cinematography/SKILL.md     |  26 +++-
 .../cinematography/references/compile-h3.md   |  30 ++---
 .../references/compile-kandinsky-6.md         |  30 ++---
 .../references/compile-ltx-2.5.md             |  30 ++---
 5 files changed, 189 insertions(+), 48 deletions(-)
 create mode 100644 docs/proposals/audits/2026-11-higgsfield/move-trial.md

diff --git a/docs/proposals/audits/2026-11-higgsfield/move-trial.md b/docs/proposals/audits/2026-11-higgsfield/move-trial.md
new file mode 100644
index 00000000..f9ab36e5
--- /dev/null
+++ b/docs/proposals/audits/2026-11-higgsfield/move-trial.md
@@ -0,0 +1,121 @@
+# Move trial: which named moves each video family honours
+
+Stage G of #1141 (#1148), run on lem (2x RTX 3090, develop @ 2c371a1d) on
+2026-11-02. Each of H3, LTX-2.5 and Kandinsky 6 got the same six moves from
+the `cinematography` vocabulary, worded by that family's compile table, at
+one seed on one fixed subject. Each clip was judged from `get_output_frames`
+(a 4-frame contact sheet, then frame 0 against the last frame at 480 px where
+the sheet was not conclusive). The verdicts are in the skill's table
+(`plugins/dw/skills/cinematography/SKILL.md`, *A hint, not a promise*) and in
+the `measured (#1148)` column of each `references/compile-*.md` move table.
+
+## Method
+
+- **Subject:** an old red cast-iron telephone box on a wet cobbled square at
+  dusk, a black iron lamp post beside it, a stone fountain to its left, brick
+  shopfronts with lit windows behind, low mist. Nothing in the frame moves on
+  its own, so any change in framing is the camera.
+- **Shot size:** `wide` for every clip, so a dolly-in or crane has room to
+  show.
+- **Seed:** 42 on every clip. Everything else is the template's default.
+- **Templates:**
+  - LTX-2.5: `templates/ltx2/text-to-video`, 960x544, 121 frames at 24 fps.
+  - Kandinsky 6: `templates/kandinsky6/text-to-video`, 864x480, 121 frames.
+  - H3: `templates/minimax/video-with-audio`, 960x544, 124 frames, the
+    template's 5-step turbo LoRA.
+- **Prompt per family:** that family's own format, with the move sentence
+  taken verbatim from its compile table and only the placeholders filled.
+  - LTX-2.5: one sentence, "A wide shot frames an old red cast-iron telephone
+    box and the cobbled square around it at dusk, <move>." plus the scene
+    description. Viewpoint "a front-facing angle". Reveals: dolly-in "the
+    black handset behind its glass panes"; dolly-out "the whole square and
+    its shopfronts"; truck-left "the stone fountain"; crane-up "the rooftops
+    and the whole cobbled square"; orbit "the narrow side street behind it".
+  - Kandinsky 6: "A wide shot of an old red cast-iron telephone box standing
+    alone on a cobbled square at dusk." then exactly one camera sentence,
+    then the scene, then `<AUDCAP>A quiet town square at dusk, distant
+    traffic hum and the soft trickle of the fountain.<ENDAUDCAP>`.
+  - H3: Context-IR, `integrated_multimodal_description: [Shot 1] Cinematic,
+    live-action, a wide shot frames ...` with the move sentence in the shot,
+    then `overall_soundscape:`.
+- **Verdicts:** *honoured* - the move landed in the right direction at a
+  visible size; *partial* - motion of the right kind, but small or mixed with
+  another move; *ignored* - no motion of that kind (the skill would mark it
+  "hint only, rarely honoured").
+
+## Results
+
+| move | H3 | LTX-2.5 | Kandinsky 6 |
+| --- | --- | --- | --- |
+| `static` | honoured (`8261c82d7308`) | honoured (`417ce2c24c44`) | honoured (`80b30f24fba1`) |
+| `dolly-in` | honoured (`8d6786c0b6a2`) | honoured (`9223e8eb9161`) | honoured (`c52734282a38`) |
+| `dolly-out` | honoured (`db5271519874`) | honoured (`0602c1400c3e`) | honoured (`26843efb6392`) |
+| `truck-left` | partial (`5cf0b0af6608`) | honoured (`6d46ed86d897`) | partial (`2192d6ca5dcf`) |
+| `crane-up` | honoured (`a9644e35f0fb`) | honoured (`7dda520e6667`) | partial (`99b30c30371e`) |
+| `orbit` | honoured (`28d0f4b327e8`) | honoured (`5134c962d4aa`) | partial (`3361d11be518`) |
+
+No cell was ignored. LTX-2.5 honoured all six; H3 missed only the truck;
+Kandinsky 6 honoured the axial moves (static, dolly in, dolly out) and turned
+each lateral or vertical one into a forward drift.
+
+### What the frames showed
+
+**LTX-2.5**
+- static: the box, lamp and fountain hold their place over all 5 s.
+- dolly-in: the box grows to fill most of the height; the fountain leaves the
+  left edge.
+- dolly-out: opens on the box's glass panes filling the frame and ends on the
+  whole square with the fountain and shopfronts.
+- truck-left: the box slides from centre to the right third; the fountain
+  comes in from the left, with parallax against the shopfronts.
+- crane-up: rises to a high angle looking down on the box, its paved circle
+  and the whole square.
+- orbit: the box stays central while the buildings behind it swing past; the
+  last frame looks down a side street the first did not show.
+
+**Kandinsky 6**
+- static: unchanged framing over 5 s.
+- dolly-in: a steady push from wide to the box filling the frame.
+- dolly-out: opens close on the box's panes and ends wide on the square.
+- truck-left (partial): the box moves right in frame but also grows; the
+  fountain and lamp hardly move against the background, so it reads as a
+  forward-right drift more than a truck.
+- crane-up (partial): the camera lifts slightly (more of the fountain rim and
+  paving shows, the box's base leaves the bottom edge) but never rises above
+  the box or looks down.
+- orbit (partial): mostly a push-in; the box's right side panel turns a
+  little into view, the background does not swing.
+
+**H3**
+- static: unchanged framing; only the mist moves.
+- dolly-in: from wide to the box's door filling the frame.
+- dolly-out: from the box close at frame left to a very wide view of the
+  square and the terrace.
+- truck-left (partial): the box, lamp and fountain shift right by about a
+  tenth of the frame width together, with little parallax against the
+  buildings - a small drift, not a truck.
+- crane-up: rises above the square until the box is at the bottom edge and
+  the rooftops and street fill the frame.
+- orbit: the fountain moves from beside the box to behind it, the lamp from
+  its left to its right, and a different row of buildings is behind it.
+
+## Limits
+
+One seed per cell is one sample, not a rate: a cell marked honoured can miss
+on another seed, and a partial can land. The subject was chosen to show
+motion clearly (a fixed object with depth cues); a moving subject or a
+close-up gives a move less to show. The seven moves not run (`truck-right`,
+`crane-down`, `tracking`, `whip-pan`, `push-in`, `handheld`, `crash-zoom`)
+stay unmeasured, and FLUX 3 Klein is a still with no move to measure.
+
+## Notes on the run
+
+- The Kandinsky 6 weights (`kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers`,
+  24.9 GB) were not on lem and were downloaded by the first Kandinsky job.
+  It is the catalog model already in use (run on lem 2026-10-06), and the
+  approved K6 clips could not run without it, so it was taken as part of the
+  approved spend.
+- Every clip carried a quiet-audio warning; the prompts asked only for
+  ambience. Audio was not part of this trial.
+- GPU time: about 40 minutes of generation across both cards, plus the
+  Kandinsky download.
diff --git a/plugins/dw/skills/cinematography/SKILL.md b/plugins/dw/skills/cinematography/SKILL.md
index 0e544249..9fbb1f9b 100644
--- a/plugins/dw/skills/cinematography/SKILL.md
+++ b/plugins/dw/skills/cinematography/SKILL.md
@@ -68,10 +68,30 @@ each family's format with a camera already in it - `ltx2/`, `kandinsky6/`,
 ## A hint, not a promise
 
 Every value here is a hint, not a promise. The models were trained on
-captions that describe camera work; none was trained on this list, and dw
-has not measured which named moves each family honours (one 241-frame LTX
+captions that describe camera work; none was trained on this list. dw has
+measured six named moves per video family once: one seed (42), one wide shot
+of a fixed subject, the catalog text-to-video template's defaults, each clip
+judged by its first and last frames (job ids and what the frames showed are
+in `docs/proposals/audits/2026-11-higgsfield/move-trial.md`):
+
+| move | `minimax-h3` | `ltx-2.5` | `kandinsky-6` |
+| --- | --- | --- | --- |
+| `static` | honoured | honoured | honoured |
+| `dolly-in` | honoured | honoured | honoured |
+| `dolly-out` | honoured | honoured | honoured |
+| `truck-left` | partial | honoured | partial |
+| `crane-up` | honoured | honoured | partial |
+| `orbit` | honoured | honoured | partial |
+
+*Honoured*: the move landed in the right direction at a visible size.
+*Partial*: some motion of the right kind, too small or mixed with another
+move (Kandinsky 6's truck, crane and orbit each drifted forward more than
+they moved as named). *Ignored*: no motion of that kind; a move measured so
+would read "hint only, rarely honoured" - none did in this trial. The
+other seven moves, and `flux3-klein` (a still), are not measured. One seed is
+one sample, not a rate: a longer clip can still drift (one 241-frame LTX
 image-to-video clip told "the camera remains static" pushed or arced from
-about five seconds, while six clips in the L2 cells showed no clear move; see
+about five seconds; see
 `docs/proposals/audits/2026-11-story-forge-lessons.md`). So:
 
 1. Describe the move in the caption in the family's own words (the table
diff --git a/plugins/dw/skills/cinematography/references/compile-h3.md b/plugins/dw/skills/cinematography/references/compile-h3.md
index c3e718c8..2a3cf4b4 100644
--- a/plugins/dw/skills/cinematography/references/compile-h3.md
+++ b/plugins/dw/skills/cinematography/references/compile-h3.md
@@ -41,21 +41,21 @@ The guide sets no shot-size words; its own examples use the plain ones.
 
 ## `move`
 
-| id | The move, in MiniMax's motion types |
-| --- | --- |
-| `static` | "The camera holds a static shot as <action>." |
-| `dolly-in` | "The camera pushes in with large amplitude toward <subject>." |
-| `dolly-out` | "The camera pulls out with large amplitude, revealing <the setting>." |
-| `truck-left` | "The camera trucks left alongside <subject>." |
-| `truck-right` | "The camera trucks right alongside <subject>." |
-| `crane-up` | "The camera pedestals up with large amplitude, rising above <subject>." |
-| `crane-down` | "The camera pedestals down to <subject>'s eye level." |
-| `orbit` | "The camera moves in an arc shot around <subject>." |
-| `tracking` | "The camera holds a tracking shot, matching <subject>'s pace." |
-| `whip-pan` | "The camera pans <left or right> with large amplitude at fast speed, revealing <the new view>." |
-| `push-in` | "The camera pushes in with small amplitude at slow speed toward <subject's face>." |
-| `handheld` | "The camera shakes slightly as it follows <subject>." |
-| `crash-zoom` | "The camera zooms in with large amplitude at fast speed on <subject>." |
+| id | The move, in MiniMax's motion types | measured (#1148) |
+| --- | --- | --- |
+| `static` | "The camera holds a static shot as <action>." | honoured |
+| `dolly-in` | "The camera pushes in with large amplitude toward <subject>." | honoured |
+| `dolly-out` | "The camera pulls out with large amplitude, revealing <the setting>." | honoured |
+| `truck-left` | "The camera trucks left alongside <subject>." | partial |
+| `truck-right` | "The camera trucks right alongside <subject>." | not measured |
+| `crane-up` | "The camera pedestals up with large amplitude, rising above <subject>." | honoured |
+| `crane-down` | "The camera pedestals down to <subject>'s eye level." | not measured |
+| `orbit` | "The camera moves in an arc shot around <subject>." | honoured |
+| `tracking` | "The camera holds a tracking shot, matching <subject>'s pace." | not measured |
+| `whip-pan` | "The camera pans <left or right> with large amplitude at fast speed, revealing <the new view>." | not measured |
+| `push-in` | "The camera pushes in with small amplitude at slow speed toward <subject's face>." | not measured |
+| `handheld` | "The camera shakes slightly as it follows <subject>." | not measured |
+| `crash-zoom` | "The camera zooms in with large amplitude at fast speed on <subject>." | not measured |
 
 ## The other axes
 
diff --git a/plugins/dw/skills/cinematography/references/compile-kandinsky-6.md b/plugins/dw/skills/cinematography/references/compile-kandinsky-6.md
index dfd80ede..5e4f85ed 100644
--- a/plugins/dw/skills/cinematography/references/compile-kandinsky-6.md
+++ b/plugins/dw/skills/cinematography/references/compile-kandinsky-6.md
@@ -33,21 +33,21 @@ A clip is five seconds: one move, once. `<subject>` is the shot's own noun.
 
 ## `move`
 
-| id | The one camera sentence |
-| --- | --- |
-| `static` | "The camera is static." |
-| `dolly-in` | "The camera slowly dollies in toward <subject>." |
-| `dolly-out` | "The camera slowly dollies back, widening to show <the setting>." |
-| `truck-left` | "The camera trucks left alongside <subject>." |
-| `truck-right` | "The camera trucks right alongside <subject>." |
-| `crane-up` | "The camera cranes up and looks down over <the setting>." |
-| `crane-down` | "The camera cranes down to <subject>'s eye level." |
-| `orbit` | "The camera circles slowly around <subject>." |
-| `tracking` | "The camera tracks alongside <subject>, keeping pace." |
-| `whip-pan` | "The camera whip-pans quickly to <the new view>." |
-| `push-in` | "The camera pushes in slightly toward <subject's face>." |
-| `handheld` | "The camera is handheld and sways gently with the action." |
-| `crash-zoom` | "The camera crash-zooms in on <subject>." |
+| id | The one camera sentence | measured (#1148) |
+| --- | --- | --- |
+| `static` | "The camera is static." | honoured |
+| `dolly-in` | "The camera slowly dollies in toward <subject>." | honoured |
+| `dolly-out` | "The camera slowly dollies back, widening to show <the setting>." | honoured |
+| `truck-left` | "The camera trucks left alongside <subject>." | partial |
+| `truck-right` | "The camera trucks right alongside <subject>." | not measured |
+| `crane-up` | "The camera cranes up and looks down over <the setting>." | partial |
+| `crane-down` | "The camera cranes down to <subject>'s eye level." | not measured |
+| `orbit` | "The camera circles slowly around <subject>." | partial |
+| `tracking` | "The camera tracks alongside <subject>, keeping pace." | not measured |
+| `whip-pan` | "The camera whip-pans quickly to <the new view>." | not measured |
+| `push-in` | "The camera pushes in slightly toward <subject's face>." | not measured |
+| `handheld` | "The camera is handheld and sways gently with the action." | not measured |
+| `crash-zoom` | "The camera crash-zooms in on <subject>." | not measured |
 
 Example opening: "A medium close-up of an old fisherman on a wet pier,
 from the chest up. The camera slowly dollies in toward his face. ..."
diff --git a/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md b/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md
index 74919b5a..ded38b9e 100644
--- a/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md
+++ b/plugins/dw/skills/cinematography/references/compile-ltx-2.5.md
@@ -40,21 +40,21 @@ the spec's seven (for example "a low angle" or "a front-facing angle").
 
 ## `move`
 
-| id | Camera motion to write (every row carries the viewpoint, as the spec requires) |
-| --- | --- |
-| `static` | "captured from <viewpoint>, the camera remains static" |
-| `dolly-in` | "seen from <viewpoint>, as the camera dollies in toward <subject>, <reveal> comes into view" |
-| `dolly-out` | "seen from <viewpoint>, as the camera dollies back from <subject>, <reveal> comes into view around them" |
-| `truck-left` | "seen from <viewpoint>, as the camera trucks left alongside <subject>, <reveal> slides into view from the left" |
-| `truck-right` | "seen from <viewpoint>, as the camera trucks right alongside <subject>, <reveal> slides into view from the right" |
-| `crane-up` | "seen from <viewpoint>, as the camera cranes upward above <subject>, <reveal> comes into view below" |
-| `crane-down` | "seen from <viewpoint>, as the camera cranes down to <subject>'s level, <reveal> comes into view behind them" |
-| `orbit` | "seen from <viewpoint>, as the camera arcs around <subject>, <reveal> comes into view behind them" |
-| `tracking` | "seen from <viewpoint>, the camera tracks alongside <subject> at their pace, <reveal> passing through the frame" |
-| `whip-pan` | "seen from <viewpoint>, the camera whip-pans <left or right> in a blur, landing on <reveal>" |
-| `push-in` | "seen from <viewpoint>, the camera pushes in slowly toward <subject's face>, until <reveal> fills the frame" |
-| `handheld` | "seen from <viewpoint>, the handheld camera sways slightly as it follows <subject>, <reveal> drifting into view" |
-| `crash-zoom` | "seen from <viewpoint>, the camera crash-zooms in on <subject>, until <reveal> fills the frame" |
+| id | Camera motion to write (every row carries the viewpoint, as the spec requires) | measured (#1148) |
+| --- | --- | --- |
+| `static` | "captured from <viewpoint>, the camera remains static" | honoured |
+| `dolly-in` | "seen from <viewpoint>, as the camera dollies in toward <subject>, <reveal> comes into view" | honoured |
+| `dolly-out` | "seen from <viewpoint>, as the camera dollies back from <subject>, <reveal> comes into view around them" | honoured |
+| `truck-left` | "seen from <viewpoint>, as the camera trucks left alongside <subject>, <reveal> slides into view from the left" | honoured |
+| `truck-right` | "seen from <viewpoint>, as the camera trucks right alongside <subject>, <reveal> slides into view from the right" | not measured |
+| `crane-up` | "seen from <viewpoint>, as the camera cranes upward above <subject>, <reveal> comes into view below" | honoured |
+| `crane-down` | "seen from <viewpoint>, as the camera cranes down to <subject>'s level, <reveal> comes into view behind them" | not measured |
+| `orbit` | "seen from <viewpoint>, as the camera arcs around <subject>, <reveal> comes into view behind them" | honoured |
+| `tracking` | "seen from <viewpoint>, the camera tracks alongside <subject> at their pace, <reveal> passing through the frame" | not measured |
+| `whip-pan` | "seen from <viewpoint>, the camera whip-pans <left or right> in a blur, landing on <reveal>" | not measured |
+| `push-in` | "seen from <viewpoint>, the camera pushes in slowly toward <subject's face>, until <reveal> fills the frame" | not measured |
+| `handheld` | "seen from <viewpoint>, the handheld camera sways slightly as it follows <subject>, <reveal> drifting into view" | not measured |
+| `crash-zoom` | "seen from <viewpoint>, the camera crash-zooms in on <subject>, until <reveal> fills the frame" | not measured |
 
 Example: "A medium shot frames a woman in a grey wool coat from the waist up,
 captured from a low angle as the camera dollies in toward her, the lit

From bd90252f8a5057ad759cb5054734ac67b8d05599 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 01:14:35 -0600
Subject: [PATCH 10/74] fix(assess): #1162 - shot_dead_air skips
 silent_by_design shots

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 dw/tasks/assess.py   |  9 ++++++---
 tests/test_assess.py | 10 ++++++++++
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/dw/tasks/assess.py b/dw/tasks/assess.py
index 970255c2..3a60fb70 100644
--- a/dw/tasks/assess.py
+++ b/dw/tasks/assess.py
@@ -608,10 +608,13 @@ def shots_answer(media, records, source):
         rms_range = 0.0
     answer = {"shots": measured, "rms_range_db": rms_range, "has_audio": True}
     dead_air_findings = []
-    for index, shot in enumerate(measured):
+    for index, (shot, record) in enumerate(zip(measured, records)):
         # A shot that is quiet throughout, on purpose, holds no gap to find -
-        # the same guard seam_hole applies to a seam's two sides (#465)
-        quiet = shot["rms_dbfs"] is not None and shot["rms_dbfs"] <= HOLE_VOICED_DBFS
+        # the same guard seam_hole applies to a seam's two sides (#465); a
+        # shot recorded silent_by_design is silent on purpose (#1162)
+        quiet = (
+            shot["rms_dbfs"] is not None and shot["rms_dbfs"] <= HOLE_VOICED_DBFS
+        ) or (isinstance(record, dict) and record.get("silent_by_design") is True)
         dead_air_findings.extend(
             _findings(
                 "analyze_shots",
diff --git a/tests/test_assess.py b/tests/test_assess.py
index a6e5f2c6..96dd9ace 100644
--- a/tests/test_assess.py
+++ b/tests/test_assess.py
@@ -255,6 +255,16 @@ def test_a_shot_quiet_throughout_does_not_fire(self):
         assert shot["dead_air_seconds"] > 0.4
         assert not [f for f in answer["findings"] if f["rule"] == "shot_dead_air"]
 
+    def test_a_silent_by_design_shot_does_not_fire_but_a_plain_one_does(self):
+        video = self._shot(gap_dbfs=-80.0)
+        flagged = [
+            f for f in analyze_shots(video)["findings"] if f["rule"] == "shot_dead_air"
+        ]
+        assert len(flagged) == 1
+        video.shots[0]["silent_by_design"] = True
+        answer = analyze_shots(video)
+        assert not [f for f in answer["findings"] if f["rule"] == "shot_dead_air"]
+
 
 # ---------------------------------------------------------------------------
 # 2. A dissolve does not flag a level step, click, hole or frame jump

From 44cc7e00b0324a9647695bd8e3fc4f04b3a941c6 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 01:22:31 -0600
Subject: [PATCH 11/74] feat(media): #1158 - pcm_sha256 in audio
 get_gallery_metadata media

Opt-in SHA-256 over the decoded samples (native dtype, interleaved,
little-endian, trimmed to the container duration) in probe_media and
_AudioAccumulator, with sample_format and frames; the gallery_metadata
route always asks for it, list_gallery and the save-time check never do.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/MCP.md                        |   2 +-
 dw/media.py                        |  40 ++++-
 dw/server/routes/media.py          |  12 +-
 dw_mcp/tools_catalog.py            |   4 +
 tests/test_media_info.py           | 244 ++++++++++++++++++++++++++++-
 tests/test_server.py               |  55 +++++++
 ui/src/lib/generated/api-schema.ts |   7 +
 ui/src/lib/generated/openapi.json  |   2 +-
 8 files changed, 361 insertions(+), 5 deletions(-)

diff --git a/docs/MCP.md b/docs/MCP.md
index 2466bca7..7aa2a007 100644
--- a/docs/MCP.md
+++ b/docs/MCP.md
@@ -234,7 +234,7 @@ when no single workflow covers it.
 | `get_server_info()` | — | What this installation can do and where it keeps things: `device` (the accelerator a run will use), `version`, the `workspace` this session is working in and the workflow/asset/output/prompt `directories` of *that* workspace, the bind address and port, whether a token is required, and whether MCP is mounted. Check the device before authoring - a CUDA-only choice (bitsandbytes, `torch.compile`, flash attention) is not available on an `mps` or `cpu` server. `runtime` (#222) reports the Python version, torch version and the CUDA version torch was built against, the NVIDIA driver version (when `nvidia-smi` is reachable), and the installed versions of diffusers, transformers, accelerate, bitsandbytes, peft, safetensors and sentencepiece (`null` for one not installed) - for diagnosing an environment mismatch between boxes without shelling in. `caches` lists dw's disk caches (`inductor`, `triton`, `quantized`), each with `name`, `bytes`, `files`, `enabled`, `clearable` and `disk_free`, plus `entry_count` for a cache with entries, and `cache_entries=true` lists them (`id`, `model`, `subfolder`, `component`, `bytes`, `files`, `unreadable`, `created`, `last_hit`; the quantizer and dtype are not in `/api/caches`); it is omitted when the server has no `/api/caches`. There is no MCP tool to clear one - that is `DELETE /api/caches/{name}` or `.../entries/{entry}` ([SERVER.md](SERVER.md)) |
 | `list_jobs(limit=20, status=None, workspace=None)` | optional `limit` (newest N), `status` (one state or a comma-separated set of `queued`, `running`, `succeeded`, `failed`, `cancelled`), `workspace` | List queued, running and recent jobs, **newest first** (a queued job held for host RAM carries `waiting_for`). Bounded by default: the unbounded listing was over a client's tool-result limit on a server with a few months of history, which made it a tool that could not be called at all. `total` says how many matched and `truncated`/`next` say so when the answer was cut - raise `limit` or narrow with `status`. Without `workspace`, a named workspace lists its own jobs and the default one lists every job the server holds |
 | `list_gallery(limit=50, subfolder=None, only_orphans=False, workspace=None, folder=None, version=None, media=False, role=None)` | `limit`, `subfolder`, `only_orphans`, `workspace`, `folder`, `version`, `media`, `role` | List generated output files, newest first. A name is `<workflow>/<run id>/<file>`, where `<file>` may sit in the subfolder the step chose (`final/episode.mp4`); each entry carries `folder` (the workflow) and `subfolder` (by convention `final` or `intermediate`, `''` when the step chose none, any path the workflow wrote otherwise), and `subfolder="final"` lists only deliverables. Each entry also carries `role`, `final` or `intermediate` (null for a flat-layout file with no run, or an older run's file that neither its subfolder nor a later run's reads place; `intermediate` once a later step or a later run read the file, unless `result.role` declared it); `role=["final"]` lists only those, and the `role` filter defaults to every file. See `dw/roles.py` for how a role is decided. A file starred in the web UI's gallery (its keep mark) carries `starred: true`; an unstarred one carries no `starred`. Each entry carries `run_id` and `version` - that run's ordinal among the workflow's runs, which is how one of several runs that wrote the same basename is named to a person: the web UI labels the same file `v5`. The number is assigned when the run opens and never renumbered, so deleting a run leaves a gap rather than sliding the rest down (a failed run, or a rerun that reused every step, leaves one too - it took a number and may have nothing to list), and it is `null` under the flat output layout, which has no runs. `folder` with `version` lists that one run's files, and `output:<folder>/v5/<file>` names one in a workflow; every other tool takes `name`. Each entry also carries a ready-made `url`, already scoped to the workspace that made it - a hand-built `/outputs/<name>` URL 404s for anything but the default workspace. `only_orphans=True` inverts the call: instead of files, it returns run directories with no media anywhere under them (`runs`, each `{name, mtime}`) - a run whose output was deleted before `delete_output` could remove it by name, or one that failed before writing anything; `subfolder` does not apply in this mode, and `name` is exactly what `delete_output` accepts (#170). `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
-| `get_gallery_metadata(name, envelope=False, workspace=None, interval_seconds=None)` | `name`, `workspace` | Get the metadata embedded in a generated file — or, when `name` is an `asset:` reference, what an *input* asset holds (`source` says which; `job` is null for an asset). Reading an input's duration, frame count, fps and sample rate before a run is how a caller learns the `total_frames`, `fps` and `sample_rate` a workflow expects it to supply: the exact workflow and arguments that produced it, and, for audio/video, a `media` block (duration, rate, channels, fps, size, peak/mean dBFS) and `findings` - each level problem the server measured against `dw/audio_qc.py`'s thresholds (`full_scale`, `near_silent`), with the fix. Only an image (PNG/JPEG/WebP) embeds `metadata` this way — it is always null for audio and video, and `next` then points at `get_job_workflow(job_id)` when `job` is known, or says a kept asset carries no provenance at all when it isn't. `envelope=true` adds `media.envelope` — `rms_dbfs` and `peak_dbfs` one entry per second, or per `interval_seconds` (default 1.0, 0.05 to 3600; 0.1 places a vocal onset; a finer-than-default width that would need over 2400 bins is refused with the interval that fits) — which is what locates something in a track rather than measuring the whole of it. `media.shots` is set on an output joined from shots: one `{name, start_frame, num_frames, start_sample, num_samples}` per shot, as the join measured them (see the run manifest in docs/WORKFLOW_GUIDE.md), else null. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
+| `get_gallery_metadata(name, envelope=False, workspace=None, interval_seconds=None)` | `name`, `workspace` | Get the metadata embedded in a generated file — or, when `name` is an `asset:` reference, what an *input* asset holds (`source` says which; `job` is null for an asset). Reading an input's duration, frame count, fps and sample rate before a run is how a caller learns the `total_frames`, `fps` and `sample_rate` a workflow expects it to supply: the exact workflow and arguments that produced it, and, for audio/video, a `media` block (duration, rate, channels, fps, size, peak/mean dBFS) and `findings` - each level problem the server measured against `dw/audio_qc.py`'s thresholds (`full_scale`, `near_silent`), with the fix. Only an image (PNG/JPEG/WebP) embeds `metadata` this way — it is always null for audio and video, and `next` then points at `get_job_workflow(job_id)` when `job` is known, or says a kept asset carries no provenance at all when it isn't. `envelope=true` adds `media.envelope` — `rms_dbfs` and `peak_dbfs` one entry per second, or per `interval_seconds` (default 1.0, 0.05 to 3600; 0.1 places a vocal onset; a finer-than-default width that would need over 2400 bins is refused with the interval that fits) — which is what locates something in a track rather than measuring the whole of it. A file with a soundtrack also carries `media.pcm_sha256`, the SHA-256 of its decoded samples (native dtype, interleaved, little-endian, trimmed to the container duration) with the `sample_format` and `frames` hashed: two tracks are sample-identical when the hashes match, and two hashes compare only when `sample_format`, `frames`, `sample_rate` and `channels` all agree. `media.shots` is set on an output joined from shots: one `{name, start_frame, num_frames, start_sample, num_samples}` per shot, as the join measured them (see the run manifest in docs/WORKFLOW_GUIDE.md), else null. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
 
 ### Media
 
diff --git a/dw/media.py b/dw/media.py
index 2481a430..e6703cb4 100644
--- a/dw/media.py
+++ b/dw/media.py
@@ -21,6 +21,7 @@
 torch or materialise frames.
 """
 
+import hashlib
 import io
 import logging
 import math
@@ -418,6 +419,7 @@ def __init__(
         duration_seconds=None,
         envelope=False,
         interval_seconds=1.0,
+        pcm_hash=False,
     ):
         self.rate = rate
         self.bin_samples = _bin_samples(rate, interval_seconds)
@@ -438,6 +440,12 @@ def __init__(
         # The whole (trimmed) soundtrack: integrated loudness and true peak
         # are measured over the full track, not per frame
         self.chunks = []
+        # The opt-in identity of the decoded samples (#1139): SHA-256 over
+        # the native dtype, interleaved (frames, channels), little-endian,
+        # over exactly the trimmed samples the loudness is measured on
+        self.hasher = hashlib.sha256() if pcm_hash else None
+        self.hash_frames = 0
+        self.sample_format = None
 
     def add(self, decoded):
         samples = to_float32(decoded)
@@ -455,10 +463,26 @@ def add(self, decoded):
             _fill_envelope(self.bins, frame[:length], self.elapsed, self.bin_samples)
         if length > 0:
             self.chunks.append(frame[:length])
+        if self.hasher is not None:
+            self._hash(decoded, length)
         # the frame's *full* length, so a later frame's position is measured
         # against the real track rather than the clipped one
         self.elapsed += full_length
 
+    def _hash(self, decoded, length):
+        native = as_frame_samples(decoded, self.channels)[:length]
+        if self.sample_format is None:
+            self.sample_format = native.dtype.name
+        elif native.dtype.name != self.sample_format:
+            # A stream whose sample format changes mid-decode has no one
+            # format its hash could be compared under: report none
+            self.hasher = None
+            return
+        self.hasher.update(
+            native.astype(native.dtype.newbyteorder("<"), copy=False).tobytes()
+        )
+        self.hash_frames += native.shape[0]
+
     def finish(self):
         if self.bins is not None and self.max_samples is None:
             _merge_trailing_fragment(self.bins, self.bin_samples)
@@ -472,10 +496,14 @@ def finish(self):
         }
         if self.bins is not None:
             levels["envelope"] = _as_envelope(self.bins, self.bin_samples / self.rate)
+        if self.hasher is not None and self.sample_format is not None:
+            levels["pcm_sha256"] = self.hasher.hexdigest()
+            levels["sample_format"] = self.sample_format
+            levels["frames"] = self.hash_frames
         return levels
 
 
-def probe_media(path, envelope=False, interval_seconds=1.0):
+def probe_media(path, envelope=False, interval_seconds=1.0, pcm_hash=False):
     """Duration, format and level of an audio or video file, or None.
 
     Video answers fps, frame_count, width and height, plus the soundtrack's
@@ -524,6 +552,15 @@ def probe_media(path, envelope=False, interval_seconds=1.0):
             (#277). A track whose *real* length isn't a whole number of
             seconds still gets a genuine final bin shorter than the rest -
             `len(rms_dbfs)` is `ceil(duration_seconds)`, not `floor` (#278)
+        pcm_hash: Also report `pcm_sha256`, the SHA-256 of the decoded
+            samples in their native dtype (`sample_format`, a numpy dtype
+            name), interleaved (frames, channels) and little-endian, trimmed
+            to the container duration as the loudness is, and `frames`, the
+            sample frames hashed. Two hashes compare only when
+            `sample_format`, `frames`, `sample_rate` and `channels` agree.
+            Off by default so `list_gallery` and the save-time check pay
+            nothing; absent, like the levels, when decode fails partway
+            (#1139)
     """
     try:
         container = av.open(path)
@@ -553,6 +590,7 @@ def probe_media(path, envelope=False, interval_seconds=1.0):
                 info.get("duration_seconds"),
                 envelope,
                 interval_seconds,
+                pcm_hash,
             )
             if audio is not None
             else None
diff --git a/dw/server/routes/media.py b/dw/server/routes/media.py
index 79efdfb3..898aac02 100644
--- a/dw/server/routes/media.py
+++ b/dw/server/routes/media.py
@@ -135,6 +135,13 @@ def gallery_metadata(
     refused (422) with the interval that fits; the default width is
     never refused for length (#823).
 
+    A file with a soundtrack also carries `pcm_sha256`, the SHA-256 of
+    its decoded samples (native dtype, interleaved, little-endian,
+    trimmed to the container duration), with the `sample_format` and
+    `frames` hashed: proof two tracks are sample-identical, comparable
+    only when `sample_format`, `frames`, `sample_rate` and `channels`
+    all agree (#1139). Only this route computes it.
+
     An image's `media` is its measured `{kind, width, height, mode}`,
     read from the file header (#922) - not the width/height arguments
     the pipeline was asked for. `include_workflow=false` drops the
@@ -223,7 +230,10 @@ def gallery_metadata(
     try:
         if kind in ("audio", "video"):
             media = probe_media(
-                path, envelope=envelope, interval_seconds=interval_seconds
+                path,
+                envelope=envelope,
+                interval_seconds=interval_seconds,
+                pcm_hash=True,
             )
         elif kind == "image":
             media = _image_media(path)
diff --git a/dw_mcp/tools_catalog.py b/dw_mcp/tools_catalog.py
index affbe598..7f012547 100644
--- a/dw_mcp/tools_catalog.py
+++ b/dw_mcp/tools_catalog.py
@@ -328,6 +328,10 @@ def get_gallery_metadata(
         `findings` lists each level problem measured (full scale, near
         silence) with its fix.
 
+        `media.pcm_sha256` hashes the decoded audio samples; two hashes
+        compare only when `sample_format`, `frames`, `sample_rate` and
+        `channels` all agree.
+
         `name` may be an `asset:` reference, and then it describes that
         input asset - a shot's frame count, whether two shots share an fps,
         whether a score reaches the cut's length. Check before running: a
diff --git a/tests/test_media_info.py b/tests/test_media_info.py
index ff5c17d2..ec82299c 100644
--- a/tests/test_media_info.py
+++ b/tests/test_media_info.py
@@ -432,7 +432,15 @@ def test_it_carries_no_loudness_keys(self, tmp_path):
 
         info = probe_metadata(str(tmp_path / "score.wav"))
 
-        for key in ("peak_dbfs", "mean_dbfs", "integrated_lufs", "true_peak_dbfs"):
+        for key in (
+            "peak_dbfs",
+            "mean_dbfs",
+            "integrated_lufs",
+            "true_peak_dbfs",
+            "pcm_sha256",
+            "sample_format",
+            "frames",
+        ):
             assert key not in info
 
     def test_a_video_soundtrack_is_never_decoded_for_its_level(self, tmp_path):
@@ -536,3 +544,237 @@ def _boom_once(self, *args, **kwargs):
 
         assert state["failed_once"]
         assert info["frame_count"] == 12
+
+
+def _write_s16_wav(path, samples, sample_rate=8000, list_chunk=False):
+    """An s16 WAV of `samples` (frames, channels), built by hand so an
+    extra LIST chunk can sit between fmt and data - the container
+    difference a file-byte hash would trip on."""
+    import struct
+
+    samples = numpy.asarray(samples, dtype="<i2")
+    channels = samples.shape[1]
+    fmt = struct.pack(
+        "<HHIIHH",
+        1,
+        channels,
+        sample_rate,
+        sample_rate * channels * 2,
+        channels * 2,
+        16,
+    )
+    chunks = b"fmt " + struct.pack("<I", len(fmt)) + fmt
+    if list_chunk:
+        info = b"INFO" + b"ISFT" + struct.pack("<I", 4) + b"dw\x00\x00"
+        chunks += b"LIST" + struct.pack("<I", len(info)) + info
+    data = samples.tobytes()
+    chunks += b"data" + struct.pack("<I", len(data)) + data
+    path.write_bytes(b"RIFF" + struct.pack("<I", 4 + len(chunks)) + b"WAVE" + chunks)
+
+
+def _write_flac(path, samples, sample_rate=8000):
+    import av
+
+    samples = numpy.asarray(samples, dtype="<i2")
+    with av.open(str(path), "w") as container:
+        stream = container.add_stream("flac", rate=sample_rate)
+        stream.layout = "stereo"
+        stream.format = "s16"
+        for start in range(0, samples.shape[0], 1024):
+            piece = numpy.ascontiguousarray(samples[start : start + 1024]).reshape(
+                1, -1
+            )
+            frame = av.AudioFrame.from_ndarray(piece, format="s16", layout="stereo")
+            frame.sample_rate = sample_rate
+            frame.pts = start
+            for packet in stream.encode(frame):
+                container.mux(packet)
+        for packet in stream.encode():
+            container.mux(packet)
+
+
+def _tone(seconds=1.0, sample_rate=8000):
+    t = numpy.arange(int(seconds * sample_rate)) / sample_rate
+    left = (numpy.sin(2 * numpy.pi * 220 * t) * 0.5 * 32767).astype("<i2")
+    right = (numpy.sin(2 * numpy.pi * 330 * t) * 0.3 * 32767).astype("<i2")
+    return numpy.stack([left, right], 1)
+
+
+HASH_KEYS = ("pcm_sha256", "sample_format", "frames")
+
+
+class TestPcmHash:
+    """`pcm_sha256`: proof over MCP that two tracks are sample-identical
+    (#1139) - of the decoded samples, not the file's bytes."""
+
+    def _hash(self, path, **kwargs):
+        return probe_media(str(path), pcm_hash=True, **kwargs)
+
+    def test_a_wav_carries_the_hash_and_its_descriptors(self, tmp_path):
+        _write_s16_wav(tmp_path / "a.wav", _tone())
+
+        info = self._hash(tmp_path / "a.wav")
+
+        assert len(info["pcm_sha256"]) == 64
+        int(info["pcm_sha256"], 16)
+        assert info["sample_format"] == "int16"
+        assert info["frames"] == 8000
+        assert info["sample_rate"] == 8000
+        assert info["channels"] == 2
+        assert info["frames"] / info["sample_rate"] == pytest.approx(
+            info["duration_seconds"], abs=1 / 8000
+        )
+
+    def test_it_hashes_the_interleaved_little_endian_samples(self, tmp_path):
+        import hashlib
+
+        tone = _tone()
+        _write_s16_wav(tmp_path / "a.wav", tone)
+
+        expected = hashlib.sha256(tone.astype("<i2").tobytes()).hexdigest()
+        assert self._hash(tmp_path / "a.wav")["pcm_sha256"] == expected
+
+    def test_off_by_default(self, tmp_path):
+        _write_s16_wav(tmp_path / "a.wav", _tone())
+
+        info = probe_media(str(tmp_path / "a.wav"))
+
+        for key in HASH_KEYS:
+            assert key not in info
+
+    def test_an_extra_list_chunk_does_not_change_it(self, tmp_path):
+        tone = _tone()
+        _write_s16_wav(tmp_path / "plain.wav", tone)
+        _write_s16_wav(tmp_path / "tagged.wav", tone, list_chunk=True)
+        assert (tmp_path / "plain.wav").read_bytes() != (
+            tmp_path / "tagged.wav"
+        ).read_bytes()
+
+        plain = self._hash(tmp_path / "plain.wav")
+        tagged = self._hash(tmp_path / "tagged.wav")
+
+        assert plain["pcm_sha256"] == tagged["pcm_sha256"]
+        assert plain["frames"] == tagged["frames"]
+
+    def test_a_wav_and_a_flac_of_the_same_samples_hash_equal(self, tmp_path):
+        tone = _tone()
+        _write_s16_wav(tmp_path / "a.wav", tone)
+        _write_flac(tmp_path / "a.flac", tone)
+
+        wav = self._hash(tmp_path / "a.wav")
+        flac = self._hash(tmp_path / "a.flac")
+
+        for key in ("pcm_sha256", "sample_format", "frames"):
+            assert wav[key] == flac[key]
+
+    def test_packed_and_planar_decodes_hash_equal(self):
+        # The same samples as a packed frame (1, frames * channels) and a
+        # planar one (channels, frames), fed to the accumulator as
+        # frame.to_ndarray() hands them over
+        from dw.media import _AudioAccumulator
+
+        tone = _tone(seconds=0.25)
+        packed = _AudioAccumulator(8000, 2, 0.25, pcm_hash=True)
+        packed.add(numpy.ascontiguousarray(tone).reshape(1, -1))
+        planar = _AudioAccumulator(8000, 2, 0.25, pcm_hash=True)
+        planar.add(numpy.ascontiguousarray(tone.T))
+
+        assert packed.finish()["pcm_sha256"] == planar.finish()["pcm_sha256"]
+
+    def test_a_polarity_flip_hashes_unequal(self, tmp_path):
+        tone = _tone()
+        _write_s16_wav(tmp_path / "a.wav", tone)
+        _write_s16_wav(tmp_path / "flipped.wav", -tone)
+
+        assert (
+            self._hash(tmp_path / "a.wav")["pcm_sha256"]
+            != self._hash(tmp_path / "flipped.wav")["pcm_sha256"]
+        )
+
+    def test_a_one_lsb_change_hashes_unequal(self, tmp_path):
+        tone = _tone()
+        nudged = tone.copy()
+        nudged[4000, 1] += 1
+        _write_s16_wav(tmp_path / "a.wav", tone)
+        _write_s16_wav(tmp_path / "nudged.wav", nudged)
+
+        a = self._hash(tmp_path / "a.wav")
+        b = self._hash(tmp_path / "nudged.wav")
+
+        assert a["pcm_sha256"] != b["pcm_sha256"]
+        assert a["peak_dbfs"] == pytest.approx(b["peak_dbfs"])
+
+    def test_a_video_soundtrack_is_hashed_trimmed_to_the_container(self, tmp_path):
+        write_mp4(tmp_path / "shot.mp4", frames=12, fps=6, with_audio=True)
+
+        info = self._hash(tmp_path / "shot.mp4")
+
+        assert len(info["pcm_sha256"]) == 64
+        assert info["sample_format"] == "float32"
+        assert info["frames"] == int(round(info["duration_seconds"] * 8000))
+        again = self._hash(tmp_path / "shot.mp4")
+        assert again["pcm_sha256"] == info["pcm_sha256"]
+
+    def test_a_mute_video_carries_none_of_the_keys(self, tmp_path):
+        write_mp4(tmp_path / "mute.mp4", frames=12, fps=6, with_audio=False)
+
+        info = self._hash(tmp_path / "mute.mp4")
+
+        for key in HASH_KEYS + ("sample_rate", "channels"):
+            assert key not in info
+
+    def test_a_damaged_track_carries_no_hash(self, tmp_path):
+        path = tmp_path / "shot.mkv"
+        write_mp4(path, frames=12, fps=6)
+        raw = bytearray(path.read_bytes())
+        for i in range(len(raw) // 2, len(raw), 64):
+            raw[i] = (raw[i] + 137) % 256
+        path.write_bytes(bytes(raw))
+
+        info = self._hash(path)
+
+        assert "peak_dbfs" not in info
+        for key in HASH_KEYS:
+            assert key not in info
+
+    @pytest.mark.parametrize("interval", [1.0, 0.05])
+    def test_the_envelope_is_unchanged_with_the_hash_on(self, tmp_path, interval):
+        write_mp4(tmp_path / "shot.mp4", frames=12, fps=6, with_audio=True)
+
+        without = probe_media(
+            str(tmp_path / "shot.mp4"), envelope=True, interval_seconds=interval
+        )
+        with_hash = self._hash(
+            tmp_path / "shot.mp4", envelope=True, interval_seconds=interval
+        )
+
+        assert with_hash["envelope"] == without["envelope"]
+        for key in ("peak_dbfs", "mean_dbfs", "integrated_lufs", "true_peak_dbfs"):
+            assert with_hash[key] == without[key]
+
+    def test_probe_metadata_never_carries_it(self, tmp_path):
+        _write_s16_wav(tmp_path / "a.wav", _tone())
+
+        info = probe_metadata(str(tmp_path / "a.wav"))
+
+        for key in HASH_KEYS:
+            assert key not in info
+
+    def test_the_save_time_check_does_not_compute_it(self, tmp_path, monkeypatch):
+        import dw.media
+        from dw import audio_qc
+
+        calls = []
+        real = dw.media.probe_media
+
+        def spy(*args, **kwargs):
+            calls.append(kwargs)
+            return real(*args, **kwargs)
+
+        monkeypatch.setattr(dw.media, "probe_media", spy)
+        _write_s16_wav(tmp_path / "a.wav", _tone())
+
+        info = audio_qc._probe_written_media(str(tmp_path / "a.wav"))
+
+        assert calls and not any(c.get("pcm_hash") for c in calls)
+        assert "pcm_sha256" not in info
diff --git a/tests/test_server.py b/tests/test_server.py
index bdcb7277..0f97a528 100644
--- a/tests/test_server.py
+++ b/tests/test_server.py
@@ -2515,6 +2515,61 @@ def test_gallery_metadata_survives_a_damaged_track(server, tmp_path):
         assert "job" in body
         assert body["media"]["kind"] == "video"
         assert "peak_dbfs" not in body["media"]
+        for key in ("pcm_sha256", "sample_format", "frames"):
+            assert key not in body["media"]
+
+
+def test_gallery_metadata_hashes_the_decoded_audio(server, tmp_path):
+    """get_gallery_metadata's media carries pcm_sha256 and its descriptors
+    for anything with a soundtrack - an audio output, a video's track, the
+    same file kept as an asset - and none of them for a mute video (#1139)."""
+    from tests.test_media_info import write_mp4, write_wav
+
+    with server(success_script) as client:
+        outputs = tmp_path / "outputs"
+        write_wav(outputs / "score-gen.0-0.0.wav", seconds=2.0)
+        write_mp4(outputs / "shot-gen.0-0.0.mp4", frames=12, fps=6)
+        write_mp4(outputs / "mute-gen.0-0.0.mp4", frames=12, fps=6, with_audio=False)
+
+        score = client.get("/api/gallery/score-gen.0-0.0.wav/metadata").json()["media"]
+        assert len(score["pcm_sha256"]) == 64
+        assert score["sample_format"] == "int16"
+        assert score["frames"] == 16000
+        assert score["sample_rate"] == 8000 and score["channels"] == 2
+        again = client.get("/api/gallery/score-gen.0-0.0.wav/metadata").json()
+        assert again["media"]["pcm_sha256"] == score["pcm_sha256"]
+
+        shot = client.get("/api/gallery/shot-gen.0-0.0.mp4/metadata").json()["media"]
+        assert len(shot["pcm_sha256"]) == 64
+        assert shot["pcm_sha256"] != score["pcm_sha256"]
+
+        mute = client.get("/api/gallery/mute-gen.0-0.0.mp4/metadata").json()["media"]
+        for key in ("pcm_sha256", "sample_format", "frames", "sample_rate", "channels"):
+            assert key not in mute
+
+
+def test_gallery_listing_never_computes_the_pcm_hash(server, tmp_path, monkeypatch):
+    """list_gallery media=true probes every file on the page: it must not
+    pay for the hash only get_gallery_metadata reports (#1139)."""
+    from dw.server.routes import gallery
+    from tests.test_media_info import write_wav
+
+    calls = []
+    real = gallery.probe_media
+
+    def spy(*args, **kwargs):
+        calls.append(kwargs)
+        return real(*args, **kwargs)
+
+    monkeypatch.setattr(gallery, "probe_media", spy)
+    with server(success_script) as client:
+        write_wav(tmp_path / "outputs" / "score-gen.0-0.0.wav", seconds=1.0)
+
+        listing = client.get("/api/gallery", params={"media": "true"}).json()
+
+        assert calls and not any(c.get("pcm_hash") for c in calls)
+        for entry in listing["files"]:
+            assert "pcm_sha256" not in entry
 
 
 def test_gallery_paginates_and_groups_by_workflow_folder(server, tmp_path):
diff --git a/ui/src/lib/generated/api-schema.ts b/ui/src/lib/generated/api-schema.ts
index a544f312..2a569285 100644
--- a/ui/src/lib/generated/api-schema.ts
+++ b/ui/src/lib/generated/api-schema.ts
@@ -618,6 +618,13 @@ export interface paths {
          *     refused (422) with the interval that fits; the default width is
          *     never refused for length (#823).
          *
+         *     A file with a soundtrack also carries `pcm_sha256`, the SHA-256 of
+         *     its decoded samples (native dtype, interleaved, little-endian,
+         *     trimmed to the container duration), with the `sample_format` and
+         *     `frames` hashed: proof two tracks are sample-identical, comparable
+         *     only when `sample_format`, `frames`, `sample_rate` and `channels`
+         *     all agree (#1139). Only this route computes it.
+         *
          *     An image's `media` is its measured `{kind, width, height, mode}`,
          *     read from the file header (#922) - not the width/height arguments
          *     the pipeline was asked for. `include_workflow=false` drops the
diff --git a/ui/src/lib/generated/openapi.json b/ui/src/lib/generated/openapi.json
index d2801ffe..7df63da7 100644
--- a/ui/src/lib/generated/openapi.json
+++ b/ui/src/lib/generated/openapi.json
@@ -6841,7 +6841,7 @@
     },
     "/api/gallery/{name}/metadata": {
       "get": {
-        "description": "Generation metadata embedded in a saved image ('workflow' inside\nit is the full definition the editor can reopen), plus the job that\nproduced the file when history remembers one, plus - for audio and\nvideo - what the file itself holds: duration, format and level,\nwhich is how an agent that cannot listen checks a track. Only an\nimage embeds 'metadata' this way - it is always null for audio and\nvideo, since neither format has a slot this writer uses; recover\nthe recipe from 'job' (GET /api/jobs/{id}/workflow) when one is\nknown, or from nothing when it isn't (a kept asset has no job).\n\n`envelope=true` adds the soundtrack's level second by second, which\nis what says *where* in a track something is - whether a shot is\nstill voiced at its last frame, how deep the hole at a seam goes.\nOpt-in: a ten-minute track is 600 numbers, and the default call has\nto stay small. `interval_seconds` (default 1.0, 0.05 to 3600) is\nthe bin width: 0.1 places a cut on a vocal onset. A track that\nwould need more than 2400 bins at a finer-than-default width is\nrefused (422) with the interval that fits; the default width is\nnever refused for length (#823).\n\nAn image's `media` is its measured `{kind, width, height, mode}`,\nread from the file header (#922) - not the width/height arguments\nthe pipeline was asked for. `include_workflow=false` drops the\nembedded `metadata.workflow` (the whole definition, often 20 KB) and\nleaves the rest of `metadata` with `workflow_omitted: true`.\n\n`name` may also be an 'asset:' reference, and then it is the input\nasset of that name that is described rather than an output (#127).\nThe numbers here - duration, frame count, fps, sample rate - are\nwhat decide whether a call will work at all, and for a file the\ncaller is about to *consume* they were previously unobtainable:\nthe only way to read a wav's length was to run a job that copied it\ninto the output directory. `job` is null for an asset with no\nrecorded provenance, and `source` says which of the two roots\nanswered.\n\nA kept asset (`keep_output`) is not always provenance-blind: when\nthe file it was kept from still had a job in history, `keep_output`\nrecorded `{job, run_id, version}` beside it, and this route reads it\nback the same way it reads a kept file's shots (#556). `run_id`/\n`version` are that source run's own - `job.id` is still the one to\npass `get_job_workflow`, since a pruned run leaves no `workflow.json`\nof its own to reopen. `inputs` is the source run's too (#902).",
+        "description": "Generation metadata embedded in a saved image ('workflow' inside\nit is the full definition the editor can reopen), plus the job that\nproduced the file when history remembers one, plus - for audio and\nvideo - what the file itself holds: duration, format and level,\nwhich is how an agent that cannot listen checks a track. Only an\nimage embeds 'metadata' this way - it is always null for audio and\nvideo, since neither format has a slot this writer uses; recover\nthe recipe from 'job' (GET /api/jobs/{id}/workflow) when one is\nknown, or from nothing when it isn't (a kept asset has no job).\n\n`envelope=true` adds the soundtrack's level second by second, which\nis what says *where* in a track something is - whether a shot is\nstill voiced at its last frame, how deep the hole at a seam goes.\nOpt-in: a ten-minute track is 600 numbers, and the default call has\nto stay small. `interval_seconds` (default 1.0, 0.05 to 3600) is\nthe bin width: 0.1 places a cut on a vocal onset. A track that\nwould need more than 2400 bins at a finer-than-default width is\nrefused (422) with the interval that fits; the default width is\nnever refused for length (#823).\n\nA file with a soundtrack also carries `pcm_sha256`, the SHA-256 of\nits decoded samples (native dtype, interleaved, little-endian,\ntrimmed to the container duration), with the `sample_format` and\n`frames` hashed: proof two tracks are sample-identical, comparable\nonly when `sample_format`, `frames`, `sample_rate` and `channels`\nall agree (#1139). Only this route computes it.\n\nAn image's `media` is its measured `{kind, width, height, mode}`,\nread from the file header (#922) - not the width/height arguments\nthe pipeline was asked for. `include_workflow=false` drops the\nembedded `metadata.workflow` (the whole definition, often 20 KB) and\nleaves the rest of `metadata` with `workflow_omitted: true`.\n\n`name` may also be an 'asset:' reference, and then it is the input\nasset of that name that is described rather than an output (#127).\nThe numbers here - duration, frame count, fps, sample rate - are\nwhat decide whether a call will work at all, and for a file the\ncaller is about to *consume* they were previously unobtainable:\nthe only way to read a wav's length was to run a job that copied it\ninto the output directory. `job` is null for an asset with no\nrecorded provenance, and `source` says which of the two roots\nanswered.\n\nA kept asset (`keep_output`) is not always provenance-blind: when\nthe file it was kept from still had a job in history, `keep_output`\nrecorded `{job, run_id, version}` beside it, and this route reads it\nback the same way it reads a kept file's shots (#556). `run_id`/\n`version` are that source run's own - `job.id` is still the one to\npass `get_job_workflow`, since a pruned run leaves no `workflow.json`\nof its own to reopen. `inputs` is the source run's too (#902).",
         "operationId": "gallery_metadata_api_gallery__name__metadata_get",
         "parameters": [
           {

From 94a0d1806ee2fb29301f6bd994de05f5450b79bc Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 01:27:55 -0600
Subject: [PATCH 12/74] docs(proposals): #1141 - close-out record for cinematic
 controls

Design record in docs/proposals/complete/cinematic-controls-complete.md
(stages #1142-#1148: what was built, deviations, bounces, the move-trial
verdicts, what was deferred) and a todo.md "Shipped" entry.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../complete/cinematic-controls-complete.md   | 193 ++++++++++++++++++
 docs/proposals/todo.md                        |  17 ++
 2 files changed, 210 insertions(+)
 create mode 100644 docs/proposals/complete/cinematic-controls-complete.md

diff --git a/docs/proposals/complete/cinematic-controls-complete.md b/docs/proposals/complete/cinematic-controls-complete.md
new file mode 100644
index 00000000..c709c15e
--- /dev/null
+++ b/docs/proposals/complete/cinematic-controls-complete.md
@@ -0,0 +1,193 @@
+# Cinematic controls: camera/look vocabulary, shot document, storyboard/animatic, face-drift check (#1141)
+
+Written by model `claude-opus-5-5` via provider `anthropic` (close-out,
+2026-11-02). Plan v1 was approved by Don on 2026-11-01 with every default
+(no later version). There were seven stages, #1142–#1148, and all were
+verified on 2026-11-02. Stages A–F were built in Don's interactive session.
+The loop built stage G and the two bounce fixes.
+
+Sources:
+- The audit: `docs/proposals/audits/2026-11-higgsfield/README.md`. It covers
+  Higgsfield's catalog, the #OpenSourceHiggsfield clones and their licences,
+  and dw's gaps.
+- The full plan with implementation tasks:
+  `docs/superpowers/plans/2026-11-01-cinematic-controls.md`. It stays in the
+  tree as the task-level record.
+- Tier 2 (re-shoot, 9 angles, speed LoRAs, relight, dubbing, swap) is a
+  separate feature, `docs/proposals/cinematic-addons.md`. It is still open.
+
+Concepts only, no code: Popcorn (Higgsfield) and the coverage set from
+Shot Board (MIT). The face-drift probe takes its approach from CreatorOS
+(YuNet + SFace) and was re-implemented.
+
+## The idea
+
+Higgsfield is a preset layer over other people's models. The audit
+concluded that **dw's gaps are mostly vocabulary and presentation, not
+capability**. So tier 1 adopts Higgsfield's director vocabulary and its
+plan-board-shoot loop on the families dw already runs: H3, LTX-2.5,
+Kandinsky 6 and FLUX 3 Klein. It adds no new generator.
+
+## Decisions (Don, 2026-11-01)
+
+- **Named moves are hints, not promises.** The skill tells the agent to
+  describe the move and check it with `get_output_frames`. Stage G measures
+  how often each family honours it.
+- **Q1 [scope], yes:** two task commands (`stills_to_video`,
+  `analyze_face_drift`) and two skills (`cinematography`, `storyboard`).
+  No MCP, REST or workflow-syntax surface.
+- **Q2 [none], yes:** the module ratchet goes 236 → 237 for
+  `dw/tasks/face_drift.py`, rather than growing `face_track.py` past its
+  ceiling.
+- **Q3 [trust], yes:** SFace weights (Apache-2.0, ~37 MB) at a pinned
+  revision.
+- **Q4 [spend], yes, after A–F verify:** about 1 h of lem GPU for stage G.
+
+## What was built
+
+### A: `cinematography` skill (#1142)
+- `plugins/dw/skills/cinematography/` holds a closed vocabulary of six
+  axes (`references/vocabulary.md`): shot size, move, lens, light,
+  era/stock and tempo.
+- It has one compile table per family: `compile-h3.md`,
+  `compile-ltx-2.5.md`, `compile-kandinsky-6.md` and
+  `compile-flux3-klein.md`. On Klein, moves compile to framing.
+- `references/grades.md` holds five named grades: noir, teal-orange,
+  70s stock, VHS and bleach-bypass. Each is an `apply_lut` palette plus a
+  `film_grain` setting.
+- *A hint, not a promise* gives the `get_output_frames(count=12)` check.
+  *Not in scope* covers Music 3 (no camera) and any other family (stop).
+- Each of the four family skills has a pointer line.
+- The H3 sentences come from MiniMax's prompt guide §4.3 and the stored
+  `minimax/fox_dawn_context_ir` prompt, because the H3 references had no
+  camera wording.
+
+### B: shot document, fix ladder, H3 lint (#1143)
+- `script-to-video/references/shot-document.md` defines:
+  - the shot list, saved as `prompt:<project>/shots` (a JSON array);
+  - each caption, saved as `prompt:<project>/shot-NN` and referenced by name;
+  - a revision, which re-saves and re-runs only the changed shots.
+- Every shot carries `shot_size`, `move`, `lens`, `light` (ids from
+  `cinematography`) and `duration`. A `null` means the model chooses.
+- The fix ladder: regional edit or face repair, then re-prompt, then
+  reseed, then ask the user. It allows 3 tries per shot and a per-project
+  GPU-minute cap quoted from `plan.estimate`.
+- The H3 prompt lint flags an overfull beat, a cut with no new information
+  and a missing `overall_soundscape`.
+
+### C: `stills_to_video` task (#1144)
+- `stills_to_video(images, durations, fps=24)` holds each still for its
+  duration. It returns a clip with one shot record per still, documented in
+  `docs/TASKS.md`.
+- Its caps:
+  - fps in (0, 120];
+  - 600 s in total;
+  - 36000 frames;
+  - a 4 GiB frame budget, checked at run time because image size isn't
+    known at validate.
+- Validate refuses bad literal durations or fps, a count mismatch and
+  either cap. Mixed sizes are refused naming `images`.
+- `images` is a declared media argument, so validate path-gates each entry
+  and names the index (`images[N]`). This came from the bounce.
+
+### D: `storyboard` skill + `animatic` template (#1145)
+- The skill covers:
+  - the Popcorn shape on Klein: up to 8 boards, with references cited as
+    "image one";
+  - the six-cut coverage set of one moment, redrawn from a hero frame;
+  - a board as the start frame of an H3, LTX-2.5 or K6 shot;
+  - the animatic recipe: boards, a TTS scratch track, a Music 3 bed, then
+    the template.
+- Boards are kept as `asset:boards/<scene>-NN.jpg`.
+- `workflows/templates/animatic.json` (shape `utility`) runs
+  `stills_to_video` and then `pair_audio(fit="video")`. The audio is
+  trimmed or padded to the clip, with a warning. The manifest carries shots
+  `still_1..N`.
+
+### E: `analyze_face_drift` + `face-drift-check` (#1146)
+- `analyze_face_drift(video, reference, shots=None, threshold=0.363,
+  samples_per_shot=8)` runs YuNet detection and SFace embedding, on CPU or
+  CUDA. Both weights are at pinned revisions and listed in the plan's
+  weights. SFace is `opencv/face_recognition_sface` @ `3d708243…`.
+- Findings come in the `assess.py` answer shape: `face_drift` (median
+  below threshold) and `no_face`.
+- Validate refuses `threshold` outside [-1, 1] and `samples_per_shot`
+  outside 1–64.
+- `workflows/templates/face-drift-check.json` is the template. The
+  `series-episodes` and `script-to-video` skills point at it.
+
+### F: sitcom multicam grammar (#1147)
+- `series-episodes/references/sitcom-multicam.md` lays out:
+  - a wide master held throughout;
+  - per-speaker singles from `templates/minimax/dialogue-short`;
+  - `transcribe_audio` (`timestamps: "segment"`), then `plan_cuts`
+    (`segment_by: "line"`) on the H3 grid for cuts on line boundaries.
+- `SKILL.md` has one pointer line to it.
+
+### G: named-move trial (#1148)
+- 18 clips: 6 moves × H3, LTX-2.5 and K6, at seed 42, on one fixed subject
+  at `wide`. Each was judged by its frames. The job ids, prompts and
+  observations are in `docs/proposals/audits/2026-11-higgsfield/move-trial.md`.
+- The verdicts are in the skill's table. Each family compile table also
+  has a `measured (#1148)` column, and the 7 moves not in the trial read
+  `not measured`.
+
+| move | H3 | LTX-2.5 | K6 |
+|---|---|---|---|
+| static | honoured | honoured | honoured |
+| dolly-in | honoured | honoured | honoured |
+| dolly-out | honoured | honoured | honoured |
+| truck-left | partial | honoured | partial |
+| crane-up | honoured | honoured | partial |
+| orbit | honoured | honoured | partial |
+
+No cell was ignored, so no move carries "hint only, rarely honoured". The
+skill defines that marking for a future trial.
+
+## Deviations from the plan
+
+- **D's estimate.** The plan said `validate_workflow` "quotes a near-zero
+  GPU estimate". The animatic loads no model, so the estimate is reported
+  as `unknown` with the reason "task-only workflow". Don noted this before
+  the spec, and C-F647 tests it.
+- **C's caps.** Beyond the plan's 600 s cap, C adds a 36000-frame cap and a
+  4 GiB run-time byte budget. So 600 s at 120 fps is refused.
+- **C's silence.** The plan said "an AudioVideo that is silent". After the
+  bounce, it is a full-length silent mono 48 kHz track, not a clip with no
+  audio stream, so the clip goes through `assemble-and-score`'s
+  `resample_audio` path. `resample_audio`'s refusal of a video with no
+  track was deliberately left alone, because many commands share it.
+- **G's download.** The K6 weights
+  (`kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers`, 24.9 GB) were
+  not on lem, so the first K6 trial job downloaded them. This is the
+  catalog model already in use, and the download was counted as part of
+  Q4's approved spend.
+
+## Bounces per stage
+
+| Stage | Bounces | What |
+|---|---|---|
+| A #1142 | 1 | C-F629: the `light` axis had no per-value sentence in the LTX-2.5, H3 and Klein compile tables (only K6 had one), which also blocked part of C-F633. Fixed with a five-phrase `light` table in each (`285b5375`, merged `d4256478`). |
+| B #1143 | 0 | |
+| C #1144 | 1 | C-F645: `assemble-and-score` failed at `resample_audio` on a stills clip with no audio track. SE-F060: some `images` path probes were not refused at validate. The details were filed privately. Fixed with a silent track and `images` as a declared media argument (`6f8e12eb`, merged `2c371a1d`, arch review pass). |
+| D #1145 | 0 | |
+| E #1146 | 0 | |
+| F #1147 | 0 | |
+| G #1148 | 0 | |
+
+Cost: no stage comment recorded `usage:` figures, so none is given here.
+G used the approved ~1 h of lem GPU (18 clips) plus the K6 download above.
+
+## Deferred and left open
+
+- **Tier 2 add-ons** (re-shoot, angles, speed LoRAs, relight, dubbing,
+  swap): `docs/proposals/cinematic-addons.md`, a separate feature.
+- **Moves not in the trial** (7 of the vocabulary's moves) and **Klein**
+  (stills: moves compile to framing) read `not measured`. Back if a user
+  reports one being ignored, or when a new trial is approved.
+- **The trial's single seed and subject.** The verdicts are one sample per
+  cell. A partial on truck-left (H3, K6) and crane-up/orbit (K6) may move
+  with seed or subject.
+- **`resample_audio` on a trackless video** still refuses. Back if another
+  task needs to produce trackless clips for the scoring path.
+- Pre-existing, found in the A–F review and filed separately: #1154.
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index 39bb0b9c..23bff5ac 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -506,6 +506,23 @@ remaining deferred fix is recorded in
   clone a voice in English, Russian and more, with a 20 s reference cap and
   a context-fit refusal. Template `voice-clone`. Record, including the
   known edges: `complete/audio8-voice-clone-complete.md`.
+- **Cinematic controls, tier 1** (#1141, stages #1142-#1148), shipped
+  2026-11-02. It adds:
+  - the `cinematography` skill: a closed camera-and-look vocabulary
+    compiled per family for H3, LTX-2.5, K6 and Klein, plus five named
+    grades;
+  - a saved shot document, a fix ladder and an H3 lint in
+    `script-to-video`;
+  - `stills_to_video` and the `storyboard` skill with an `animatic`
+    template;
+  - `analyze_face_drift` (YuNet + SFace, pinned) with a
+    `face-drift-check` template;
+  - a sitcom multicam grammar in `series-episodes`.
+
+  A measured trial put six moves × three families at honoured or partial,
+  none ignored. Record, including what was deferred (the 7 unmeasured
+  moves, tier 2 in `cinematic-addons.md`):
+  `complete/cinematic-controls-complete.md`.
 
 ## Declined
 

From c4c74bbcbd60261a9c9f14aac7fa556d4cadc863 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 01:41:29 -0600
Subject: [PATCH 13/74] fix(audio): #1163 - no near-silent job warning on a cut
 whose shots are all silent_by_design

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 dw/audio_qc.py                      | 12 +++++++
 tests/test_audio_qc_silent_shots.py | 55 +++++++++++++++++++++++++++++
 2 files changed, 67 insertions(+)
 create mode 100644 tests/test_audio_qc_silent_shots.py

diff --git a/dw/audio_qc.py b/dw/audio_qc.py
index 062ce9ca..fec2724f 100644
--- a/dw/audio_qc.py
+++ b/dw/audio_qc.py
@@ -197,6 +197,17 @@ def warn_if_written_above_full_scale(
 NEAR_SILENT_QUIET_NOT_EMPTY_DBFS = -30.0
 
 
+def all_shots_silent_by_design(shots):
+    """True when a file's recorded shots all say they are silent on purpose
+    (#1163): a cut of stills is silent by design, so the near-silent warning's
+    advice (a malformed prompt, a near-zero gain) would chase nothing. One
+    shot that is not exempt keeps the check on the whole file."""
+    return bool(shots) and all(
+        isinstance(shot, dict) and shot.get("silent_by_design") is True
+        for shot in shots
+    )
+
+
 def warn_if_written_near_silent(
     output_path, already_warned=False, info=_UNPROBED, source_already_quiet=False
 ):
@@ -437,6 +448,7 @@ def check_written_media(
         info=probed_info,
         source_already_quiet=(
             getattr(artifact, "silent_by_design", False) is True
+            or all_shots_silent_by_design(getattr(artifact, "shots", None))
             or (
                 source_mean_dbfs is not None
                 and source_mean_dbfs < NEAR_SILENT_WARN_DBFS
diff --git a/tests/test_audio_qc_silent_shots.py b/tests/test_audio_qc_silent_shots.py
new file mode 100644
index 00000000..5a65bd92
--- /dev/null
+++ b/tests/test_audio_qc_silent_shots.py
@@ -0,0 +1,55 @@
+"""#1163: a cut whose shots are all silent_by_design gets no near-silent warning."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from dw import audio_qc
+
+
+@pytest.mark.parametrize(
+    "shots, expected",
+    [
+        (None, False),
+        ([], False),
+        ([{"silent_by_design": True}, {"silent_by_design": True}], True),
+        ([{"silent_by_design": True}, {"name": "x"}], False),
+        ([{"silent_by_design": True}, {"silent_by_design": False}], False),
+    ],
+)
+def test_all_shots_silent_by_design(shots, expected):
+    assert audio_qc.all_shots_silent_by_design(shots) is expected
+
+
+def _run(shots, monkeypatch):
+    monkeypatch.setattr(
+        audio_qc,
+        "_probe_written_media",
+        lambda path: {"mean_dbfs": -120.0, "peak_dbfs": -120.0},
+    )
+    monkeypatch.setattr(audio_qc, "remeasure_shots_after_mux", lambda *a, **k: None)
+    monkeypatch.setattr(
+        audio_qc, "warn_if_written_above_full_scale", lambda *a, **k: None
+    )
+    seen = []
+    monkeypatch.setattr(audio_qc, "emit_warning", lambda msg, **kw: seen.append(msg))
+    audio_qc.check_written_media(
+        "/tmp/cut.mp4",
+        SimpleNamespace(shots=shots),
+        "video/mp4",
+        video_fps=lambda: 24,
+        consumed_by_normalizer=False,
+        headroom_warned=False,
+        predicted_peak_dbfs=None,
+    )
+    return seen
+
+
+def test_all_silent_shots_emit_no_near_silent_warning(monkeypatch):
+    shots = [{"silent_by_design": True}, {"silent_by_design": True}]
+    assert _run(shots, monkeypatch) == []
+
+
+def test_a_mixed_cut_still_warns(monkeypatch):
+    shots = [{"silent_by_design": True}, {"name": "b"}]
+    assert any("near-silent" in m for m in _run(shots, monkeypatch))

From dece403de57f3e27cde0977f8ca1867e3cca7c85 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:18:45 -0600
Subject: [PATCH 14/74] feat(loras): #1159 - h3-antiburn catalog entry (trial)
 from the A/B/C chain probe

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 loras/minimax-h3/h3-antiburn.json | 53 +++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 loras/minimax-h3/h3-antiburn.json

diff --git a/loras/minimax-h3/h3-antiburn.json b/loras/minimax-h3/h3-antiburn.json
new file mode 100644
index 00000000..fc439888
--- /dev/null
+++ b/loras/minimax-h3/h3-antiburn.json
@@ -0,0 +1,53 @@
+{
+  "model_name": "Imagineer13/H3-Anti-Burn-LoRA",
+  "weight_name": "h3_antiburn_universal_v1.safetensors",
+  "revision": "b98eb1d3f5574d1b8f57d6b0e720d2f047cfadb3",
+  "base_models": [
+    "MiniMaxAI/MiniMax-H3"
+  ],
+  "workflow": [
+    "fl2va"
+  ],
+  "description": "Imagineer13's rank-32 anti-burn LoRA (310 MB, ComfyUI diffusion_model.* keys), trained to stop the contrast build-up, over-sharpened skin and crunchy texture that compound across H3 continuation segments. Loads through load_lora_weights alongside the turbo adapter (lem, 2026-11-02, #1159). The file name names neither fl2v nor ref2v, so validate warns adapter_unrecognized; only fl2va has been tried. On a seed-42 five-segment guide chain (39 guide frames, 960x544, turbo-4step-keyframe at 5 steps) the unaided chain had burnt skin, knit and wall by segment 5; 0.6 on segments 2-5 held segment 1's look with a slight softening of fine texture; 0.4 let the burn partly back in by segment 5",
+  "use_when": "continuation segments only, never segment 1; needs a hand-built chain until a chain option exists",
+  "scale": {
+    "default": 0.6,
+    "range": [
+      0.4,
+      0.8
+    ]
+  },
+  "stacks_with": [
+    "minimax-h3/turbo-4step-keyframe"
+  ],
+  "status": "trial",
+  "evidence": [
+    {
+      "job": "3dc3f60fb217",
+      "issue": 1151,
+      "note": "Load check: fl2va with turbo plus anti-burn 0.6 ran clean, no unmatched-key warning; differs from turbo-only job e82ea2430448 (PSNR 22.75 dB, SSIM 0.78)"
+    },
+    {
+      "job": "e0c2ff0dd654",
+      "issue": 1151,
+      "note": "Arm A: five-segment guide chain without anti-burn - skin, knit and flat wall burnt by segment 5 (frame 421)"
+    },
+    {
+      "job": "bf477d3089dd",
+      "issue": 1151,
+      "note": "Arm B: the same chain with 0.6 on segments 2-5 - segment 1's look held through segment 5, fine knit slightly softened"
+    },
+    {
+      "job": "f8287f133e1c",
+      "issue": 1151,
+      "note": "Arm C: 0.4 on segments 2-5 - better than none, but the burn partly returns by segment 5"
+    }
+  ],
+  "license": "minimax-h3-community",
+  "tags": [
+    "anti-burn",
+    "continuation",
+    "chain",
+    "quality"
+  ]
+}

From 416d32bf2fe3b53d25db0497071134dbdeba58f7 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:23:20 -0600
Subject: [PATCH 15/74] docs(proposals): #1139 - close-out record for
 pcm_sha256

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../proposals/complete/pcm-sha256-complete.md | 108 ++++++++++++++++++
 docs/proposals/todo.md                        |   5 +
 2 files changed, 113 insertions(+)
 create mode 100644 docs/proposals/complete/pcm-sha256-complete.md

diff --git a/docs/proposals/complete/pcm-sha256-complete.md b/docs/proposals/complete/pcm-sha256-complete.md
new file mode 100644
index 00000000..4737e95c
--- /dev/null
+++ b/docs/proposals/complete/pcm-sha256-complete.md
@@ -0,0 +1,108 @@
+# `pcm_sha256`: a decoded-sample identity check for audio over MCP (#1139)
+
+Written by model `claude-opus-5-5` via provider `anthropic` (close-out session,
+2026-11-02). Don filed the idea on 2026-11-01 from harnest#170. Plan v1 was
+designed on #1139, and Don approved it unchanged (Q1 cut, Q2 yes). It was built
+as one stage, #1158. The plan comment on #1139 is still the full design. This
+file records what shipped.
+
+## The problem
+
+The MCP surface had no way to check whether two audio files are the same.
+`compare_outputs` refuses `.wav`. `get_gallery_metadata` returned only derived
+levels (duration, peak/mean dBFS, LUFS, true peak, an optional envelope). Two
+files with equal levels are not proven identical: a polarity flip, a reordering
+inside one envelope bin, or a one-LSB change all give the same levels. This
+caused two problems in the suite:
+- harnest#170: C-F619 step 3 ("same seed gives identical audio") fell back to
+  comparing a 0.05 s envelope plus the five levels.
+- C-F080: its sample-level properties had to be checked in pytest instead of
+  over MCP.
+
+## Verdict: build smaller
+
+The audio hash shipped. **Cut: `pixel_sha256` for images and video** (Q1).
+`compare_outputs` already reports "identical" (infinite PSNR) for images and
+video at sampled moments. Image `media` is pinned to an exact key set, and
+hashing every video frame would mean a full decode on every metadata call.
+*What would bring it back:* a verify that needs exact identity across every
+frame of a video, or an image identity check that `compare_outputs` can't
+express. Either would be filed as a new idea.
+
+These non-goals stayed out:
+- a "how different" measure for audio;
+- a hash of the file bytes;
+- a hash in `list_gallery` or computed on save.
+
+## Plan history
+
+- **v1 (approved, 2026-11-01):** audio only, one stage.
+  - Q1 [scope]: cut image and video hashes.
+  - Q2 [none]: hash the native decoded dtype, interleaved `(frames, channels)`,
+    little-endian, and trimmed to the container duration the same way the
+    LUFS and envelope chunks are. `sample_format` is the numpy dtype name.
+  - The plan corrected the proposal. The float32 array behind the levels loses
+    precision for `s32` sources. Peak and mean are measured untrimmed while
+    LUFS is trimmed. So the hash uses its own native-dtype path.
+
+## What was built
+
+### Stage A (#1158): `pcm_sha256` in `get_gallery_metadata` `media`
+
+Commit `44cc7e00` on `feat/1139-A-pcm-sha256`, merged to `develop` as
+`710a0e81` and deployed to lem on 2026-11-02.
+
+- `dw/media.py`: `_AudioAccumulator(..., pcm_hash=False)` and
+  `probe_media(..., pcm_hash=False)`. The hash is fed one decoded frame at a
+  time over the native `to_ndarray()` dtype and trimmed by the same `length`
+  the LUFS/envelope chunks use. It adds three new keys: `pcm_sha256`,
+  `sample_format` and `frames`. `sample_rate` and `channels` were already
+  reported.
+- `dw/server/routes/media.py`: the `gallery_metadata` route always passes
+  `pcm_hash=True`. There is no new REST or MCP parameter. `list_gallery
+  media=true`, `check_written_media` and `probe_metadata` don't compute the
+  hash.
+- `dw_mcp/tools_catalog.py` and `docs/MCP.md` each say what is hashed and the
+  rule for comparing: two hashes compare only when `sample_format`, `frames`,
+  `sample_rate` and `channels` all agree. The OpenAPI snapshot and UI types
+  were regenerated for the docstring change. The schema didn't change.
+- Tests: `TestPcmHash` in `tests/test_media_info.py` and route cases in
+  `tests/test_server.py`. They cover the byte layout, the hash being off by
+  default, an extra `LIST` chunk, WAV vs FLAC, packed vs planar, polarity,
+  one-LSB, an AAC mp4 soundtrack, a mute video, a damaged track, the envelope
+  and levels being unchanged, and `list_gallery` not carrying the hash.
+
+**Deviations recorded at build:**
+- On a damaged track, `pcm_sha256`, `sample_format` and `frames` are absent.
+  `sample_rate` and `channels` stay, as they did before the stage, because
+  they are header fields.
+- If the decoded dtype changes mid-stream, the hash is omitted, because no
+  single `sample_format` describes it.
+- This libav decodes FLAC to packed `s16`. So WAV vs FLAC pins container
+  invariance, and packed vs planar is pinned by feeding both ndarray layouts
+  to the accumulator.
+
+**Review and verify:** the architecture review passed. The tester verified it
+over MCP with no bounces. C-F658 to C-F661 all passed as written:
+- wav and mp3 assets: the mp3's `frames` shows the priming trim;
+- mp4 soundtrack vs mute mp4 vs jpg;
+- envelope bins unchanged at 1.0 s and 0.05 s;
+- two seed-1124 MMAudio runs gave equal hashes, seed 1125 gave a different
+  one, and the kept asset hashed the same as its source.
+
+| Stage | Bounces | Cost |
+|---|---|---|
+| A (#1158) | 0 | not recorded |
+
+## Known edges and follow-ups
+
+- **Lossy codecs:** the hash is of this server's decode. It is stable run to
+  run on one box but may differ across libav versions. Suite cases compare
+  within one server.
+- **C-F080's "bit-identical outside a region"** needs a hash of a time range.
+  That would be a follow-up with a new parameter and needs its own approval.
+- **C-F619 step 3** can now be tightened to compare `pcm_sha256`. That
+  amendment is the curator's.
+- Found by the tester, outside this feature: `validate_workflow`'s plan
+  fingerprint doesn't cover a step-level `seed` (seed 1124 and 1125 inline
+  workflows got the same fingerprint). It was noted on #1158 but not filed.
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index 23bff5ac..beaa0739 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -37,6 +37,11 @@ remaining deferred fix is recorded in
 
 ## Shipped since the ranking
 
+- **`pcm_sha256`, an audio identity check over MCP** (#1139, stage #1158),
+  shipped 2026-11-02. `get_gallery_metadata`'s `media` now carries a
+  SHA-256 of the decoded samples, with `sample_format` and `frames`, for any
+  file with a soundtrack. The image/video `pixel_sha256` was cut, with its
+  trigger: `complete/pcm-sha256-complete.md`.
 - **MMAudio picture-synced sound effects** (#1124, stages #1131-#1133),
   shipped 2026-11-01. It followed a listening gate in which Don heard a
   clear win on replacing a shot's own sound, on silent clips and on

From a27968a2e6b5afe21ae02dcc947a8f43c37a8f13 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:34:47 -0600
Subject: [PATCH 16/74] refactor(audio_qc): #1163 - one silent-by-design
 predicate, assess.py uses audio_qc's

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md |  4 ++--
 dw/server/assess.py  | 11 +----------
 2 files changed, 3 insertions(+), 12 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 15023b2d..3cf25cce 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -86,7 +86,7 @@ one to open.
 | A `join_windows` list of the wrong length, before the run | `dw/window_count_errors.py`: `window_count_errors`, wired into `validation_errors` | Owned by the task, never a template: any `join_windows` step whose `source` is probeable header-only (`asset:`/`output:` or a readable literal path), with literal `num_frames`/`overlap` and an expanded `videos` list, is checked against `window_count_problem` - the same rule the run refuses with. Anything not knowable stays silent for the run. | `tests/test_window_count_errors.py` |
 | Fit to a model and restore | `dw/tasks/fit.py`: `fit_to_model`, `restore_to_source` | fit_to_model's record is the whole of what restore_to_source needs: the scale is inferred from the output's size and must be uniform, and restore returns the source's size times that scale and at most its frame count. | `tests/test_fit_to_model.py` |
 | Shared image ops | `dw/tasks/image_ops.py`: `LUMA_WEIGHTS`, `luma`, `split_alpha`, `join_alpha`, `per_frame` | `grade`, `finish` and `lut` take luma (float64 Rec. 709 weights, cast to the input's dtype) and alpha handling from `image_ops`, never from each other, so an RGBA input comes back RGBA with its alpha untouched; `lut.LUMA_WEIGHTS` is a re-export. An image command runs over a video or frame list only through `per_frame`. A media file is read by `task._load_media` with `fetch_image(keep_alpha=True)`, so a file with transparency arrives RGBA; a pipeline input still flattens to RGB. | `tests/test_image_ops.py::TestLuma::test_lut_re_exports_the_one_float64_weights`, `tests/test_image_ops.py::TestAlpha::test_rgba_round_trip_keeps_the_alpha_bytes`, `tests/test_image_ops.py::TestOutputsAreByteIdentical::test_every_command_matches_the_pre_refactor_bytes`, `tests/test_image_ops.py::TestPerFrame::test_a_video_comes_back_as_a_video_with_the_same_frames_and_audio`, `tests/test_finish.py::TestRgbaFileKeepsAlpha::test_a_file_path_comes_back_rgba_with_its_alpha` |
-| Audio level of a deliverable | `dw/audio_qc.py`: `warn_without_headroom`, `warn_if_written_above_full_scale` | A muxed video keeps the pre-encode headroom prediction and falls back to it only when the post-write probe cannot measure the file. A result a later step resets the level of (`consumed_by_normalizer`, `dw/step_cache.py`) draws neither the post-write warning nor the fallback. | `tests/test_result.py::TestNoHeadroom::test_a_clean_video_mux_drops_the_stale_prediction`, `tests/test_result.py::TestNoHeadroom::test_an_unprobeable_video_mux_falls_back_to_the_prediction`, `tests/test_result.py::TestNoHeadroom::test_a_video_a_join_level_matches_draws_no_headroom_warning`, `tests/test_result.py::TestTheWrittenLevel::test_a_file_that_decodes_above_full_scale_warns` |
+| Audio level of a deliverable | `dw/audio_qc.py`: `warn_without_headroom`, `warn_if_written_above_full_scale`, `all_shots_silent_by_design` | A muxed video keeps the pre-encode headroom prediction and falls back to it only when the post-write probe cannot measure the file. A result a later step resets the level of (`consumed_by_normalizer`, `dw/step_cache.py`) draws neither the post-write warning nor the fallback. | `tests/test_result.py::TestNoHeadroom::test_a_clean_video_mux_drops_the_stale_prediction`, `tests/test_result.py::TestNoHeadroom::test_an_unprobeable_video_mux_falls_back_to_the_prediction`, `tests/test_result.py::TestNoHeadroom::test_a_video_a_join_level_matches_draws_no_headroom_warning`, `tests/test_result.py::TestTheWrittenLevel::test_a_file_that_decodes_above_full_scale_warns` |
 | Level-spread warning on a replaced track | `dw/step_cache.py`: `audio_replaced_downstream`, `dw/tasks/joins.py`: `warn_on_level_spread` | A join whose track a later `pair_audio` takes as its `video` draws no `level_spread` warning; being that step's `audio` argument doesn't count. | `tests/test_step_cache.py::test_level_spread_warning_suppressed_when_audio_replaced`, `tests/test_step_cache.py::test_audio_replaced_downstream_false_when_it_is_the_audio_or_unread` |
 | Template audio-level placement | `workflows/templates/minimax/music-video.json`, `workflows/templates/minimax/music.json` | `normalize_audio` (-3 dBFS) acts only on the track that goes into the final file, so the slices that condition the shots are untouched. `music-video` slices each shot `lead_frames` early and `trim_video` drops the run-up before the join. | — |
 
@@ -193,7 +193,7 @@ one to open.
 | Inline images | `dw/server/inline_media.py` | Fitting an image to a longest side and halving it under a base64 budget happens on the server, for `/image` and `/frames` alike; `dw_mcp` forwards the size and budget and imports no Pillow. | `tests/test_server_gallery_image.py`, `tests/test_mcp_server.py::TestStartupWeight::test_the_server_starts_without_importing_the_engine` |
 | Workflow patch | `dw/library.py`: `merge_patch`; `dw/server/routes/library.py`: `patch_workflow` | A merge patch is applied on the server under the lock `PUT` takes; no client reads, merges and writes back. | `tests/test_server_library_path.py::TestPatchWorkflow::test_patch_merges_onto_the_stored_definition` |
 | Deleting a job's run | `dw/server/jobs.py`: `JobManager.run_location`; `dw/server/outputs.py`: `delete_run_directory` | The server resolves a job's run directory against the root the job ran in, and refuses a job still queued or running. | `tests/test_server_jobs.py::TestDeleteAJobsRun::test_a_running_job_is_409` |
-| Level findings | `dw/server/assess.py`: `level_findings` | Gallery metadata reports level problems from `dw/audio_qc.py`'s thresholds; no client restates a threshold. | `tests/test_server_assess.py::TestMetadataLevelFindings::test_level_findings_read_audio_qc_thresholds` |
+| Level findings | `dw/server/assess.py`: `level_findings` | Gallery metadata reports level problems from `dw/audio_qc.py`'s thresholds; no client restates a threshold. The silent-by-design exemption to the near-silent rule is `dw/audio_qc.py`: `all_shots_silent_by_design`, used by this row and by the write-time warning. | `tests/test_server_assess.py::TestMetadataLevelFindings::test_level_findings_read_audio_qc_thresholds` |
 | API errors | `dw_mcp/client.py` | An API failure becomes a message a person can act on, here and nowhere else. | `tests/test_mcp_client.py::test_a_400_surfaces_the_servers_detail_verbatim` |
 
 ## UI
diff --git a/dw/server/assess.py b/dw/server/assess.py
index 8f10e19e..f6aa3bc6 100644
--- a/dw/server/assess.py
+++ b/dw/server/assess.py
@@ -152,15 +152,6 @@ def assess(path, kind, shots, probe=None, detail=False):
 __all__ = ["PROBES", "assess", "unknown_probe"]
 
 
-def silent_by_design(shots):
-    """Whether every recorded shot says its track is silent on purpose
-    (stills_to_video's zeros, #1161)."""
-    return bool(shots) and all(
-        isinstance(shot, dict) and shot.get("silent_by_design") is True
-        for shot in shots
-    )
-
-
 def level_findings(media, shots=None):
     """The level problems a probed file's soundtrack shows, as findings:
     a peak at full scale (an encode may clip it) and a near-silent mean
@@ -188,7 +179,7 @@ def level_findings(media, shots=None):
     if (
         mean is not None
         and mean < audio_qc.NEAR_SILENT_WARN_DBFS
-        and not silent_by_design(shots)
+        and not audio_qc.all_shots_silent_by_design(shots)
     ):
         quiet_not_empty = (
             peak is not None and peak >= audio_qc.NEAR_SILENT_QUIET_NOT_EMPTY_DBFS

From 35985dd217f67385839a803ba776b73be84800ec Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:40:22 -0600
Subject: [PATCH 17/74] feat(media_compare): #1160 - texture bands on
 compare_outputs

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/media_compare.py       | 120 ++++++++++++++++++++++++++++++++++----
 dw/server/routes/media.py |  25 ++++++--
 dw/texture_bands.py       | 120 ++++++++++++++++++++++++++++++++++++++
 dw_mcp/media.py           |  61 ++++++++++++++++++-
 dw_mcp/tools_media.py     |   7 ++-
 5 files changed, 315 insertions(+), 18 deletions(-)
 create mode 100644 dw/texture_bands.py

diff --git a/dw/media_compare.py b/dw/media_compare.py
index c051fcb1..05f88c0a 100644
--- a/dw/media_compare.py
+++ b/dw/media_compare.py
@@ -23,6 +23,7 @@
 from PIL import Image, ImageDraw, ImageFont
 
 from . import media, references
+from . import texture_bands as bands
 from .media_frames import compose_grid, evenly_spaced_indices, moment_index, parse_crop
 
 MAX_COMPARE_NAMES = 8
@@ -82,14 +83,25 @@ def _luma(rgb):
     return rgb[..., 0] * 0.299 + rgb[..., 1] * 0.587 + rgb[..., 2] * 0.114
 
 
-def check_request(names, at, crop, baseline):
+def check_request(names, at, crop, baseline, texture_bands=False):
     """Refuse a request by its shape before anything is resolved or
     decoded: name and moment counts, the baseline, the crop's form.
-    Returns (the baseline's row, crop mode: None, "shared" or "each")."""
-    if not isinstance(names, list) or not 2 <= len(names) <= MAX_COMPARE_NAMES:
+    Returns (the baseline's row, crop mode: None, "shared" or "each").
+
+    With `texture_bands` one name is enough (one chain's moments against
+    each other), the crop must be one shared box, and that box must hold
+    the fine band (#1152)."""
+    fewest = 1 if texture_bands else 2
+    if not isinstance(names, list) or not fewest <= len(names) <= MAX_COMPARE_NAMES:
         count = len(names) if isinstance(names, list) else 0
+        if count == 1:
+            raise ValueError(
+                "`names` holds 1 output; compare takes 2 to "
+                f"{MAX_COMPARE_NAMES} (or 1 with `texture_bands`)"
+            )
         raise ValueError(
-            f"`names` holds {count} outputs; compare takes 2 to {MAX_COMPARE_NAMES}"
+            f"`names` holds {count} outputs; compare takes {fewest} to "
+            f"{MAX_COMPARE_NAMES}"
         )
     if at is not None and not 1 <= len(at) <= MAX_COMPARE_MOMENTS:
         raise ValueError(
@@ -104,12 +116,24 @@ def check_request(names, at, crop, baseline):
     if crop is None:
         return row, None
     if crop and all(isinstance(box, (list, tuple)) for box in crop):
+        if texture_bands:
+            raise ValueError(
+                "texture bands need one shared box: give `crop` as one "
+                "[x, y, width, height] for every output, not one per name"
+            )
         if len(crop) != len(names):
             raise ValueError(
                 f"`crop` gives {len(crop)} boxes for {len(names)} names - give "
                 "one [x, y, width, height] for every output, or one box for all"
             )
         return row, "each"
+    if texture_bands:
+        _, _, width, height = parse_crop(crop)
+        if min(width, height) < bands.MIN_SIDE:
+            raise ValueError(
+                f"crop {width}x{height} is too small for the 2-8 px band: "
+                f"texture bands need at least {bands.MIN_SIDE} px a side"
+            )
     return row, "shared"
 
 
@@ -143,11 +167,19 @@ def resolve_moments(at, reference, shortest):
     return resolved
 
 
-def compare(sources, at, crop, baseline_row, crop_mode, max_dimension):
+def compare(
+    sources, at, crop, baseline_row, crop_mode, max_dimension, texture_bands=False
+):
     """The grid and metrics for `sources`, one dict per output in the
     caller's order: {name, kind, and for a still `image` (decoded, PIL),
     for a video `path` and `shape` (`video_shape`)}. Returns the answer's
-    body without the encoded grid, plus the grid itself under "grid"."""
+    body without the encoded grid, plus the grid itself under "grid".
+
+    `texture_bands` adds, per output per moment, the band energy of the
+    measured region (`texture_bands.texture_bands`) - the baseline's too -
+    and, off the baseline, each band's ratio to the baseline's: a
+    `texture` list (one entry per output) and the same numbers on each
+    metrics cell, under a `texture_bands` header naming the px ranges."""
     for source in sources:
         source["width"], source["height"] = _size(source)
         source["box"] = None
@@ -203,14 +235,34 @@ def compare(sources, at, crop, baseline_row, crop_mode, max_dimension):
         for frame in base_frames
     ]
 
+    base_texture = (
+        [None if array is None else bands.texture_bands(array) for array in base_arrays]
+        if texture_bands
+        else None
+    )
+
     rows = []
     metrics = []
+    texture = []
     for row, source in enumerate(sources):
         source_frames = base_frames if row == baseline_row else _frames(source, frames)
+        entry = None
         if row != baseline_row:
-            metrics.append(
-                _metrics_for(
-                    row, source, source_frames, baseline, base_arrays, measure_box
+            entry = _metrics_for(
+                row, source, source_frames, baseline, base_arrays, measure_box
+            )
+            metrics.append(entry)
+        if texture_bands:
+            texture.append(
+                _texture_for(
+                    row,
+                    source,
+                    source_frames,
+                    baseline,
+                    base_texture,
+                    measure_box,
+                    row == baseline_row,
+                    entry,
                 )
             )
         rows.append(
@@ -223,7 +275,7 @@ def compare(sources, at, crop, baseline_row, crop_mode, max_dimension):
 
     fps_values = {round(video["shape"]["fps"] or 0.0, 3) for video in videos}
     grid = _grid(rows, sources, baseline_row, frames, bool(videos))
-    return {
+    answer = {
         "grid": grid,
         "baseline": baseline["name"],
         "region": metrics_region,
@@ -236,6 +288,13 @@ def compare(sources, at, crop, baseline_row, crop_mode, max_dimension):
         "metrics": metrics,
         "notes": notes,
     }
+    if texture_bands:
+        answer["texture_bands"] = {
+            band: f"{shortest}-{longest} px"
+            for band, (shortest, longest) in bands.BANDS.items()
+        }
+        answer["texture"] = texture
+    return answer
 
 
 def _size(source):
@@ -303,6 +362,47 @@ def _metrics_for(row, source, source_frames, baseline, base_arrays, measure_box)
     return entry
 
 
+def _texture_for(
+    row, source, source_frames, baseline, base_texture, measure_box, is_baseline, entry
+):
+    """One output's band energy per moment, and off the baseline its ratio
+    to the baseline's at the same moment; copied onto `entry`'s metrics
+    cells too, so a cell holds every number for its moment."""
+    texture = {
+        "name": source["name"],
+        "row": row,
+        "baseline": is_baseline,
+        "cells": [],
+    }
+    for index, frame in enumerate(source_frames):
+        cell = {"texture": None, "reason": None}
+        if not is_baseline:
+            cell["texture_ratio"] = None
+        if frame is None:
+            cell["reason"] = f"past end ({source['shape']['frame_count']} frames)"
+        else:
+            measured = (
+                base_texture[index]
+                if is_baseline
+                else bands.texture_bands(_measured(frame, measure_box))
+            )
+            cell["texture"] = measured
+            if not is_baseline:
+                base = base_texture[index]
+                if base is None:
+                    cell["reason"] = (
+                        f"baseline past end ({baseline['shape']['frame_count']} "
+                        "frames): no ratio"
+                    )
+                else:
+                    cell["texture_ratio"] = bands.ratio(measured, base)
+        texture["cells"].append(cell)
+        if entry is not None:
+            entry["cells"][index]["texture"] = cell["texture"]
+            entry["cells"][index]["texture_ratio"] = cell["texture_ratio"]
+    return texture
+
+
 def _summary(values):
     """mean, min and max of one metric over the measured columns, None when
     none was measured. PSNR's "identical" columns are left out of the
diff --git a/dw/server/routes/media.py b/dw/server/routes/media.py
index 898aac02..deccd1ab 100644
--- a/dw/server/routes/media.py
+++ b/dw/server/routes/media.py
@@ -652,7 +652,8 @@ def _encoded_tile(tile, image):
 
 class CompareRequest(BaseModel):
     names: list[str] = Field(
-        description="2 to 8 outputs (or `asset:` references), one row each"
+        description="2 to 8 outputs (or `asset:` references), one row each; "
+        "1 is enough with `texture_bands`"
     )
     at: Optional[list[Union[float, str]]] = Field(
         None,
@@ -672,6 +673,14 @@ class CompareRequest(BaseModel):
     max_total_bytes: Optional[int] = Field(
         None, description="A budget on the grid's base64 size"
     )
+    texture_bands: bool = Field(
+        False,
+        description="Add per-output RMS energy in the fine (2-8 px), mid "
+        "(8-32) and coarse (32-128) wavelength bands of the measured region, "
+        "on luma, R-G and B-G, plus each band's ratio to the baseline - a "
+        "measurement, not a score. Needs one shared `crop` (or none: the "
+        "full frame) of at least 16 px a side",
+    )
 
 
 def _compare_source(state, ws, name):
@@ -718,10 +727,12 @@ def gallery_compare(
     name is shown only, and the metrics stay on the full frame. Outputs
     of different sizes get no metrics, and say why. Stills join a video
     comparison as the same image in every column. `max_total_bytes`
-    shrinks the whole grid, and `downscaled_to` says to what."""
+    shrinks the whole grid, and `downscaled_to` says to what.
+    `texture_bands` (#1152) adds each output's band energy per moment,
+    the baseline's included, and its ratio to the baseline's."""
     try:
         baseline_row, crop_mode = media_compare.check_request(
-            body.names, body.at, body.crop, body.baseline
+            body.names, body.at, body.crop, body.baseline, body.texture_bands
         )
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
@@ -730,7 +741,13 @@ def gallery_compare(
     limit = max(1, int(body.max_dimension))
     try:
         answer = media_compare.compare(
-            sources, body.at, body.crop, baseline_row, crop_mode, limit
+            sources,
+            body.at,
+            body.crop,
+            baseline_row,
+            crop_mode,
+            limit,
+            texture_bands=body.texture_bands,
         )
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
diff --git a/dw/texture_bands.py b/dw/texture_bands.py
new file mode 100644
index 00000000..8d6f8d4b
--- /dev/null
+++ b/dw/texture_bands.py
@@ -0,0 +1,120 @@
+"""Texture energy by wavelength band - `compare_outputs(texture_bands=true)`
+(#1152). A measurement, not a score: how much of a region's variation sits
+at fine (2-8 px), mid (8-32 px) and coarse (32-128 px) wavelengths, on luma
+and on two chroma differences, so an A/B can tell coarsening or blotching
+(mid up) from blur or over-smoothing (every band down).
+
+Per channel (Y, R-G, B-G, on the 0-255 scale): a least-squares plane is
+removed so a linear lighting gradient reads as nothing, a 2D Hann window
+tapers the edges so the box's border is not a step, and `numpy.fft.rfft2`
+gives the spectrum. Each band is the RMS of the components whose radial
+frequency falls in that band's wavelengths, rescaled for the window's
+energy, so a sine of amplitude A measures about A/sqrt(2) in its band. A
+band whose longest wavelength exceeds the box's short side can't be held
+by the box and is None.
+
+The method is after knoic/ComfyUI-MiniMaxH3-PrefixStream (MIT); see
+docs/ATTRIBUTIONS.md. Pure numpy; the caller decodes and crops.
+"""
+
+import numpy
+
+# Band name -> (shortest, longest) wavelength in pixels
+BANDS = {"fine": (2, 8), "mid": (8, 32), "coarse": (32, 128)}
+
+# The smallest box side that still holds the fine band
+MIN_SIDE = 16
+
+CHANNELS = ("luma", "chroma_rg", "chroma_bg")
+
+
+def texture_bands(rgb):
+    """`{luma, chroma_rg, chroma_bg}`, each `{fine, mid, coarse}` (float or
+    None), for an (h, w, 3) uint8 RGB array."""
+    rgb = numpy.asarray(rgb, dtype=numpy.float64)
+    if rgb.ndim != 3 or rgb.shape[2] < 3:
+        raise ValueError(f"texture_bands needs an (h, w, 3) array, got {rgb.shape}")
+    height, width = rgb.shape[:2]
+    if min(height, width) < MIN_SIDE:
+        raise ValueError(
+            f"a {width}x{height} region is too small for the 2-8 px band "
+            f"(at least {MIN_SIDE} px a side)"
+        )
+    red, green, blue = rgb[..., 0], rgb[..., 1], rgb[..., 2]
+    planes = {
+        "luma": red * 0.299 + green * 0.587 + blue * 0.114,
+        "chroma_rg": red - green,
+        "chroma_bg": blue - green,
+    }
+    masks = _band_masks(height, width)
+    window = numpy.outer(numpy.hanning(height), numpy.hanning(width))
+    window_power = float(numpy.mean(window * window))
+    return {
+        channel: _bands(planes[channel], window, window_power, masks)
+        for channel in CHANNELS
+    }
+
+
+def ratio(value, base):
+    """`value / base` per channel and band, None where either is None or
+    the baseline band is 0."""
+    return {
+        channel: {
+            band: (
+                None
+                if value[channel][band] is None or not base[channel][band]
+                else round(value[channel][band] / base[channel][band], 4)
+            )
+            for band in BANDS
+        }
+        for channel in CHANNELS
+    }
+
+
+def _bands(plane, window, window_power, masks):
+    tapered = _detrend(plane) * window
+    spectrum = numpy.fft.rfft2(tapered)
+    power = (spectrum.real**2 + spectrum.imag**2) * masks["weight"]
+    count = plane.size
+    out = {}
+    for band, mask in masks["bands"].items():
+        if mask is None:
+            out[band] = None
+            continue
+        mean_square = float(power[mask].sum()) / (count * count)
+        out[band] = round(float(numpy.sqrt(mean_square / window_power)), 4)
+    return out
+
+
+def _detrend(plane):
+    """`plane` less its least-squares fit a + b*x + c*y. On a full grid the
+    centred x and y are orthogonal to each other and to the constant, so
+    each coefficient is its own projection - no solve needed."""
+    height, width = plane.shape
+    x = numpy.arange(width, dtype=numpy.float64) - (width - 1) / 2.0
+    y = numpy.arange(height, dtype=numpy.float64) - (height - 1) / 2.0
+    slope_x = float((plane * x[None, :]).sum()) / (height * float((x * x).sum()))
+    slope_y = float((plane * y[:, None]).sum()) / (width * float((y * y).sum()))
+    return plane - plane.mean() - slope_x * x[None, :] - slope_y * y[:, None]
+
+
+def _band_masks(height, width):
+    """Per-band boolean masks over the rfft2 grid, and the weight that
+    counts each rfft column's mirrored twin (Parseval over a half
+    spectrum)."""
+    fy = numpy.fft.fftfreq(height)[:, None]
+    fx = numpy.fft.rfftfreq(width)[None, :]
+    radius = numpy.sqrt(fx * fx + fy * fy)
+    weight = numpy.full((1, fx.shape[1]), 2.0)
+    weight[0, 0] = 1.0
+    if width % 2 == 0:
+        weight[0, -1] = 1.0
+    short_side = min(height, width)
+    bands = {}
+    for band, (shortest, longest) in BANDS.items():
+        if longest > short_side:
+            bands[band] = None
+            continue
+        # wavelength in [shortest, longest) px; the fine band keeps Nyquist
+        bands[band] = (radius > 1.0 / longest) & (radius <= 1.0 / shortest)
+    return {"weight": weight, "bands": bands}
diff --git a/dw_mcp/media.py b/dw_mcp/media.py
index 6e509a7c..a9fa1cfe 100644
--- a/dw_mcp/media.py
+++ b/dw_mcp/media.py
@@ -335,12 +335,21 @@ def assess_output(client, name, probe=None, detail=False, workspace=None):
 
 
 def compare_outputs(
-    client, names, at=None, crop=None, baseline=None, max_dimension=384, workspace=None
+    client,
+    names,
+    at=None,
+    crop=None,
+    baseline=None,
+    max_dimension=384,
+    texture_bands=False,
+    workspace=None,
 ):
     """Two to eight outputs side by side at the same moments, with PSNR,
     SSIM and mean absolute difference against a baseline (#871). The
     server builds the grid and measures at source resolution; the grid is
-    held to MAX_RETURNED_BYTES, shrunk whole when over it."""
+    held to MAX_RETURNED_BYTES, shrunk whole when over it.
+    `texture_bands` (#1152) is sent only when true, so a call without it
+    posts what it always has."""
     payload = {
         "names": names,
         "max_dimension": max_dimension,
@@ -349,6 +358,8 @@ def compare_outputs(
     for key, value in (("at", at), ("crop", crop), ("baseline", baseline)):
         if value is not None:
             payload[key] = value
+    if texture_bands:
+        payload["texture_bands"] = True
     return client.post_json(
         api_path("api", "gallery", "compare"), payload=payload, workspace=workspace
     )
@@ -370,7 +381,10 @@ def compare_summary(result):
     for entry in result["metrics"]:
         line = f"- row {entry['row'] + 1} {entry['name']}:"
         if entry["ssim"] is None:
-            lines.append(f"{line} no metrics - {entry['reason']}")
+            # No SSIM is no *pairwise* metrics: texture bands, when asked
+            # for, still measured this output and are printed below
+            what = "no pairwise metrics" if result.get("texture") else "no metrics"
+            lines.append(f"{line} {what} - {entry['reason']}")
             continue
         for key in ("psnr_db", "ssim", "mean_abs_diff"):
             summary = entry[key]
@@ -384,6 +398,8 @@ def compare_summary(result):
         if skipped:
             line += "  skipped: " + "; ".join(sorted(skipped))
         lines.append(line)
+    if result.get("texture"):
+        lines.extend(_texture_lines(result))
     if result["fps_differs"]:
         lines.append("fps_differs: the videos' frame rates differ")
     if result["downscaled_to"]:
@@ -392,6 +408,45 @@ def compare_summary(result):
     return "\n".join(lines)
 
 
+TEXTURE_CHANNELS = (("luma", "Y"), ("chroma_rg", "R-G"), ("chroma_bg", "B-G"))
+
+
+def _texture_lines(result):
+    """One bands line per output per moment: fine/mid/coarse for luma and
+    both chroma differences, and off the baseline the ratio to it."""
+    header = result.get("texture_bands") or {}
+    lines = [
+        "texture bands (RMS, a measurement not a score): "
+        + ", ".join(f"{band} {span}" for band, span in header.items())
+    ]
+    for entry in result["texture"]:
+        tag = "  (baseline)" if entry["baseline"] else ""
+        for column, cell in zip(result["columns"], entry["cells"]):
+            line = (
+                f"- row {entry['row'] + 1} {entry['name']}{tag} @ {column['moment']}:"
+            )
+            if cell["texture"] is None:
+                lines.append(f"{line} no bands - {cell['reason']}")
+                continue
+            line += "  " + "  ".join(
+                f"{label} {_band_triple(cell['texture'][key])}"
+                for key, label in TEXTURE_CHANNELS
+            )
+            if cell.get("texture_ratio"):
+                line += "  ratio " + "  ".join(
+                    f"{label} {_band_triple(cell['texture_ratio'][key])}"
+                    for key, label in TEXTURE_CHANNELS
+                )
+            elif cell["reason"]:
+                line += f"  ({cell['reason']})"
+            lines.append(line)
+    return lines
+
+
+def _band_triple(bands):
+    return "/".join("-" if value is None else f"{value:g}" for value in bands.values())
+
+
 def delete_output(client, name=None, workspace=None, job_id=None):
     """Remove one file from the output directory. The gallery is the output
     directory read back, so this is where a delete belongs.
diff --git a/dw_mcp/tools_media.py b/dw_mcp/tools_media.py
index 3a983955..7551e641 100644
--- a/dw_mcp/tools_media.py
+++ b/dw_mcp/tools_media.py
@@ -216,12 +216,16 @@ def compare_outputs(
         crop: list[int] | list[list[int]] | None = None,
         baseline: str | None = None,
         max_dimension: int = 384,
+        texture_bands: bool = False,
         workspace: str | None = None,
     ) -> list[ImageContent | TextContent]:
         """2-8 outputs as one grid (a row each, a column per `at` moment,
         the same frame number in all) with PSNR, SSIM and mean abs diff
         against `baseline` (default the first). One `crop` box is shown
-        and measured; one box per name is shown only. Takes `asset:`."""
+        and measured; one box per name is shown only. Takes `asset:`.
+        `texture_bands` adds per-output FFT band energy over `crop`, 1+
+        names (a measurement, not a score; see the workflows guide,
+        Comparing outputs)."""
         result = media.compare_outputs(
             self.client,
             names,
@@ -229,6 +233,7 @@ def compare_outputs(
             crop=crop,
             baseline=baseline,
             max_dimension=max_dimension,
+            texture_bands=texture_bands,
             workspace=workspace,
         )
         image = result["image"]

From 5e8396dea8a3d33f3be6f33aff4fd2a96913e19f Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:41:04 -0600
Subject: [PATCH 18/74] feat(ui): #1160 - regenerate the OpenAPI document and
 UI schema for texture_bands

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 ui/src/lib/generated/api-schema.ts | 10 +++++++++-
 ui/src/lib/generated/openapi.json  | 10 ++++++++--
 2 files changed, 17 insertions(+), 3 deletions(-)

diff --git a/ui/src/lib/generated/api-schema.ts b/ui/src/lib/generated/api-schema.ts
index 2a569285..ab4cfcae 100644
--- a/ui/src/lib/generated/api-schema.ts
+++ b/ui/src/lib/generated/api-schema.ts
@@ -402,6 +402,8 @@ export interface paths {
          *     of different sizes get no metrics, and say why. Stills join a video
          *     comparison as the same image in every column. `max_total_bytes`
          *     shrinks the whole grid, and `downscaled_to` says to what.
+         *     `texture_bands` (#1152) adds each output's band energy per moment,
+         *     the baseline's included, and its ratio to the baseline's.
          */
         post: operations["gallery_compare_api_gallery_compare_post"];
         delete?: never;
@@ -2102,9 +2104,15 @@ export interface components {
             max_total_bytes?: number | null;
             /**
              * Names
-             * @description 2 to 8 outputs (or `asset:` references), one row each
+             * @description 2 to 8 outputs (or `asset:` references), one row each; 1 is enough with `texture_bands`
              */
             names: string[];
+            /**
+             * Texture Bands
+             * @description Add per-output RMS energy in the fine (2-8 px), mid (8-32) and coarse (32-128) wavelength bands of the measured region, on luma, R-G and B-G, plus each band's ratio to the baseline - a measurement, not a score. Needs one shared `crop` (or none: the full frame) of at least 16 px a side
+             * @default false
+             */
+            texture_bands: boolean;
         };
         /** CopiedItems */
         CopiedItems: {
diff --git a/ui/src/lib/generated/openapi.json b/ui/src/lib/generated/openapi.json
index 7df63da7..81317a1a 100644
--- a/ui/src/lib/generated/openapi.json
+++ b/ui/src/lib/generated/openapi.json
@@ -501,12 +501,18 @@
             "title": "Max Total Bytes"
           },
           "names": {
-            "description": "2 to 8 outputs (or `asset:` references), one row each",
+            "description": "2 to 8 outputs (or `asset:` references), one row each; 1 is enough with `texture_bands`",
             "items": {
               "type": "string"
             },
             "title": "Names",
             "type": "array"
+          },
+          "texture_bands": {
+            "default": false,
+            "description": "Add per-output RMS energy in the fine (2-8 px), mid (8-32) and coarse (32-128) wavelength bands of the measured region, on luma, R-G and B-G, plus each band's ratio to the baseline - a measurement, not a score. Needs one shared `crop` (or none: the full frame) of at least 16 px a side",
+            "title": "Texture Bands",
+            "type": "boolean"
           }
         },
         "required": [
@@ -6223,7 +6229,7 @@
     },
     "/api/gallery/compare": {
       "post": {
-        "description": "Two to eight outputs side by side at the same moments, and how far\neach is from a baseline (#871): one grid, a row per output and a\ncolumn per moment, plus PSNR, SSIM and mean absolute difference per\nnon-baseline output at source resolution. Moments resolve to one\nframe index against the baseline (or the first video) and every\noutput is read at that frame; one past an output's end is a blank\ncell. A single `crop` is the region shown and measured; one box per\nname is shown only, and the metrics stay on the full frame. Outputs\nof different sizes get no metrics, and say why. Stills join a video\ncomparison as the same image in every column. `max_total_bytes`\nshrinks the whole grid, and `downscaled_to` says to what.",
+        "description": "Two to eight outputs side by side at the same moments, and how far\neach is from a baseline (#871): one grid, a row per output and a\ncolumn per moment, plus PSNR, SSIM and mean absolute difference per\nnon-baseline output at source resolution. Moments resolve to one\nframe index against the baseline (or the first video) and every\noutput is read at that frame; one past an output's end is a blank\ncell. A single `crop` is the region shown and measured; one box per\nname is shown only, and the metrics stay on the full frame. Outputs\nof different sizes get no metrics, and say why. Stills join a video\ncomparison as the same image in every column. `max_total_bytes`\nshrinks the whole grid, and `downscaled_to` says to what.\n`texture_bands` (#1152) adds each output's band energy per moment,\nthe baseline's included, and its ratio to the baseline's.",
         "operationId": "gallery_compare_api_gallery_compare_post",
         "parameters": [
           {

From f25bc63dcc0126f6fd4fc2ed002f2ff64f50e451 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:45:14 -0600
Subject: [PATCH 19/74] feat(media_compare): #1160 - texture band tests, guide
 subsection and docs

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md                          |   2 +-
 docs/ATTRIBUTIONS.md                          |   7 ++
 docs/MCP.md                                   |   2 +-
 docs/SERVER.md                                |  16 ++-
 docs/WORKFLOW_GUIDE.md                        |  31 ++++-
 plugins/dw/skills/minimax-h3/SKILL.md         |   3 +
 .../dw/skills/minimax-h3/references/cuts.md   |   4 +
 tests/test_compare_route.py                   |  99 +++++++++++++++
 tests/test_mcp_server.py                      |  75 +++++++++++
 tests/test_media_compare.py                   | 118 ++++++++++++++++++
 tests/test_texture_bands.py                   | 102 +++++++++++++++
 11 files changed, 452 insertions(+), 7 deletions(-)
 create mode 100644 tests/test_texture_bands.py

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 3cf25cce..14d40edb 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -139,7 +139,7 @@ one to open.
 | --- | --- | --- | --- |
 | Opening media | `dw/media.py`, `dw/media_readers.py` (the frame readers, re-exported by `dw/media.py`, which imports from it and never the reverse) | These are the only modules that call `av.open`. They import no torch and no step types. | `tests/test_media_layering.py::test_av_open_appears_only_in_media`, `tests/test_media_layering.py::test_media_imports_no_step_types` (no torch: —) |
 | Frame moments and crops | `dw/media_frames.py`: `moment_index`, `parse_crop` | One `at` grammar ("frame:N", or seconds at the clip's fps; negative counts back from the end for `get_output_frames` and is refused by `compare_outputs`) and one `[x, y, width, height]` crop parse serve `get_output_frames` and `compare_outputs`. Only the bounds policy is the caller's: `get_output_frames` refuses a moment past the end and clamps a crop (`_moment_to_index`, `resolve_crop_box`); `compare_outputs` blanks a past-end cell and refuses a crop that doesn't fit an output. | `tests/test_media_compare.py::test_resolve_moments_refuses_what_get_output_frames_refuses`, `tests/test_media_compare.py::test_crop_box_refuses_a_malformed_box_as_resolve_crop_box_does` |
-| Output comparison | `dw/media_compare.py`: `compare`, `psnr`, `ssim`, `mean_abs_diff`, `check_request`, `crop_box` | Metrics always compare the same pixels: one shared crop is the measured region, and per-output crops are display only, leaving metrics on the full frame. An output whose size differs from the baseline's, or a moment past its end, gets null metrics with a reason; nothing is resized to force a number. SSIM is on Rec. 601 luma (SSIM's convention, not `image_ops.luma`). It runs in the server process and decodes through `dw/media.py` and `open_bounded`. | `tests/test_compare_route.py::test_a_shared_crop_is_the_region_measured`, `tests/test_compare_route.py::test_per_output_crops_are_display_only`, `tests/test_compare_route.py::test_a_size_mismatch_gets_no_numbers_and_says_why`, `tests/test_compare_route.py::test_a_moment_past_the_shorter_clip_is_a_blank_cell` |
+| Output comparison | `dw/media_compare.py` (texture bands from `dw/texture_bands.py`): `compare`, `psnr`, `ssim`, `mean_abs_diff`, `check_request`, `crop_box` | Metrics always compare the same pixels: one shared crop is the measured region, and per-output crops are display only, leaving metrics on the full frame. An output whose size differs from the baseline's, or a moment past its end, gets null metrics with a reason; nothing is resized to force a number. SSIM is on Rec. 601 luma (SSIM's convention, not `image_ops.luma`). It runs in the server process and decodes through `dw/media.py` and `open_bounded`. | `tests/test_compare_route.py::test_a_shared_crop_is_the_region_measured`, `tests/test_compare_route.py::test_per_output_crops_are_display_only`, `tests/test_compare_route.py::test_a_size_mismatch_gets_no_numbers_and_says_why`, `tests/test_compare_route.py::test_a_moment_past_the_shorter_clip_is_a_blank_cell` |
 | Signal processing | `dw/dsp.py` | It measures and transforms waveforms, decides nothing, and imports nothing from `dw`. | `tests/test_media_layering.py::test_dsp_imports_nothing_from_dw` |
 | Assessment probes | `dw/tasks/assess.py`, `dw/assessment_rules.py` | Exactly three registered commands are assessment probes. Each measures a finished file, and every rule names a real probe field. | `tests/test_assessment_rules.py::TestRuleProbesAreRealCommands::test_assessment_is_exactly_the_three_probes`, `tests/test_assessment_rules.py::TestRuleProbesAreRealCommands::test_every_rule_names_a_registered_json_command` |
 | Script check | `dw/tasks/script_check.py`: `check`; `dw/tasks/script_findings.py`: the rule constants and the finding builders `check` calls (imported one way, re-exported by `script_check`); `dw/tasks/script_take.py`: `guard_words`, `word_level`, `shot_spans`; `dw/script_lines.py`: `parse_lines`, `parse_shots`, `shot_names_error` | `check_script` aligns a take's heard words to its expected lines and guards out words heard over silence and repetition loops (Whisper's hallucination over music). It builds its findings with `assessment_rules.finding()` and reads the guard's floor and window (`DEAD_AIR_FLOOR_DBFS`, `DEAD_AIR_WINDOW`) rather than copying them; the loudest-window measure is `dsp.loudest_window_dbfs` and the looped-bed self-correlation `dsp.repeat_correlation` (`script_findings.is_looped_bed` only decides on it) (`script_take.word_level` wraps it; the guard, the transcription levelling, the floor-gated `audible_runs` and the shot placement (with `clamp_to_shot`, a line's timing kept inside its shot) are `dw/tasks/script_take.py`, which `script_check` reads one way - nothing there reads the rule tables), which `transcribe_audio`'s `suspect: "silent"` mark reads with the same floor and window; the repetition-loop detector (`script_lines.looped_texts`, `REPEAT_RUN_MIN`, `REPEAT_MAX_PERIOD`) lives in `dw/script_lines.py` and backs both `check_script`'s `repetition` discard and `transcribe_audio`'s `suspect: "repetition"`, and `script_lines.suspect_runs`/`SUSPECT_LEFT_OUT` are what `plan_cuts` groups and leaves out suspect chunks by - so neither task imports the other. It is not an assessment probe and adds nothing to `RULES`. Its literal `lines` is refused at validation through `task_problems` (`script_lines_errors`), which calls the same `parse_lines` the task runs, and a literal `shots` through the same `parse_shots` and `shot_names_error`; the parsers live in `dw/script_lines.py`, not the task, so that `task_problems` reaches them without importing `script_check` (the `import_cycles` ratchet in `scripts/arch_metrics.py`). A line's `shot` may be the short `@name` of the one recorded shot that carries it (`script_lines.matching_shot_names`, `with_full_shot_names`, shared by the task and the literal-`shots` check); which member key a recorded name carries comes from `references.member_keys`, beside `MEMBER_SEPARATOR` and `for_each.member_name`, never a re-derived pattern: a key ends at the first character outside `references.VARIABLE_NAME_CHARACTERS`, the set `security.validate_variable_name` (which checks a for_each entry's name) builds its pattern from. With `audio` an `output:`/`asset:`/path whose run manifest records shots, `dw/shot_span_preflight.py: script_shot_name_errors` (the `script_shot_names` error check) runs `shot_names_error` at validation, reading the map through `runs.shots_beside`. Its shot map comes from `assess.resolve_shots`, the one the probes use, never a copy; it places each shot on the soundtrack with `assess.sample_span`, decides repeated shot names with `shots.duplicate_shot_names`, and asks `locations.is_http_url` whether the take is a URL. | `tests/test_script_check.py::TestRegistration::test_registered_as_json_command_without_assessment_flag`, `tests/test_script_check.py::TestEmptyLines::test_words_only_over_silence_are_discarded_not_found`, `tests/test_script_check.py::TestLinesErrors::test_literal_with_a_colon_is_not_a_reference`, `tests/test_script_check.py::TestShotValidation::test_a_short_member_name_matches_the_one_shot_carrying_it`, `tests/test_script_check.py::TestShotValidation::test_a_valid_variable_name_comes_back_whole_as_a_member_key`, `tests/test_shot_span_preflight.py::TestScriptShotNames::test_a_wrong_shot_name_is_an_error_at_validate_time` |
diff --git a/docs/ATTRIBUTIONS.md b/docs/ATTRIBUTIONS.md
index 02d07b21..c2d28839 100644
--- a/docs/ATTRIBUTIONS.md
+++ b/docs/ATTRIBUTIONS.md
@@ -109,3 +109,10 @@ rendering the odd shots of a long take first and bridging the gaps
 ([#1094](https://github.com/dkackman/diffusers-workflow/issues/1094)), and
 finding phrase starts from the centre of the stereo mix
 ([#1095](https://github.com/dkackman/diffusers-workflow/issues/1095)).
+
+### [knoic/ComfyUI-MiniMaxH3-PrefixStream](https://github.com/knoic/ComfyUI-MiniMaxH3-PrefixStream)
+
+A ComfyUI node pack for MiniMax H3 prefix streaming, under the MIT licence. It
+inspired the texture-band measurement on `compare_outputs`
+(`texture_bands`, [dw/texture_bands.py](../dw/texture_bands.py)): RMS energy by
+wavelength band, to see whether a change in a chain adds or removes texture.
diff --git a/docs/MCP.md b/docs/MCP.md
index 7aa2a007..6e99529d 100644
--- a/docs/MCP.md
+++ b/docs/MCP.md
@@ -245,7 +245,7 @@ when no single workflow covers it.
 | `get_output_frames(name, at=None, seams=None, count=None, boundaries=None, names=None, max_dimension=512, hear=None, workspace=None)` | `name`, `at`, `seams`, `count`, `boundaries`, `names`, `max_dimension`, `hear`, `workspace` | See a generated video as frames, since there is no video content type over MCP (#193). One selector per call: `count` for an evenly spaced contact sheet, `at` for moments (seconds or `"frame:N"`), `seams` (true, or seam numbers from 1) for the last frame before and first frame after each join side by side (each pair carries `difference`, the mean pixel change across the join, 0-255 - rank seams by it and look at the worst). `boundaries` and `names` are modifiers of `seams` only - passing either alongside `at` or `count` is refused, naming the offending argument(s). On an output joined from shots (`concat_videos`, `dissolve_videos`, a chained step) `seams` alone is enough: the seams and their names are the `media.shots` its run recorded. For any other file pass `boundaries` - each later shot's first frame, the running sum of the shots' `frame_count` from `get_gallery_metadata` on their own `intermediate/` files - and `names` to name them; either one given overrides the recorded value. Tiles are fitted to `max_dimension` and, when the set would exceed the 4MB budget, shrunk together rather than dropped; the text part lists each tile and says so. `hear=N` also returns N seconds of soundtrack centred on each `at` moment, after its image - the way to check a hit point or lip-sync without reconciling two clocks; a mute clip keeps its frames and says `no soundtrack` |
 | `get_output_text(name, max_characters=20000, workspace=None)` | `name`, `max_characters`, `workspace` | Read a text output — a prompt enhancement, or any step whose result is `text/plain` or JSON. Reports the file's real length and whether it was truncated. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
 | `assess_output(name, probe=None, detail=False, workspace=None)` | `name` (a gallery name or `asset:`), `probe`, `detail`, `workspace` | Measure a finished cut on the server, without queueing: the assessment probes (`analyze_shots`, `analyze_seams`, `analyze_sync_drift`) run in the server process over one decode of the file, beside whatever job holds the GPU. Shot boundaries are the ones its run recorded (the manifest for an output, the `keep_output` sidecar for an asset). The answer merges every applicable probe's `findings` (`{rule, severity, at, value, threshold, says}`) with `rules_applied`, `rules_skipped` and `not_applicable` (`{probe: why}` - a still, a mute file, a file with no recorded shots); `detail=true` adds each probe's full body under `probes`, and `probe=` returns that one probe's full body. `probe` is checked against the three names before anything else is read, and an unknown one is refused naming them. Findings are places to look, not verdicts: drill in with `get_output_frames(seams=[n])` / `get_output_audio(start, duration)`. See *Assessing a run's output* in docs/WORKFLOW_GUIDE.md |
-| `compare_outputs(names, at=None, crop=None, baseline=None, max_dimension=384, workspace=None)` | `names`, `at`, `crop`, `baseline`, `max_dimension`, `workspace` | Read-only, queues nothing: 2-8 outputs (gallery names or `asset:`) as one grid, a row per output (labelled with its row number and short name, `(baseline)` on the baseline) and a column per moment, each cell stamped `frame N`, with PSNR, SSIM and mean absolute difference against `baseline` (default the first name). `at` is 1-8 moments (seconds or `"frame:N"`, never negative: unlike `get_output_frames`, a negative moment is a 400, not counted back from the end); they resolve to one frame index against the baseline (the first video if the baseline is a still) and every output is read at that same frame number; default 4 evenly spaced frames of the shortest video. A frame past an output's end is a blank cell and its metrics cell says `past end (N frames)`; `fps_differs` flags videos with different frame rates, where the same frame number is not the same second. All stills: one column, `at` ignored with a note; a still among videos repeats in every column. Metrics are per non-baseline output at source resolution: `psnr_db` (`identical` when equal), `ssim` (luma, 11x11 Gaussian window, sigma 1.5) and `mean_abs_diff` (0-255), per column and as mean/min/max. `crop` is `[x,y,w,h]`: one shared box is the region shown and measured (`region` reads `crop [...]`); a list of one box per name is display only and metrics stay on the full frame (no crop is `full frame`). A crop must lie wholly inside every output's frame, else 400 (stricter than `get_output_frames`, which clamps). An output whose size differs from the baseline gets null metrics with the reason `size differs from baseline: WxH vs WxH` - it is never resized. `max_dimension` is the longest side of each cell; the grid is held to 4 MiB and shrunk whole when over (never below 64 px per cell), `downscaled_to` null when nothing shrank. Returns the grid image and a text summary. Errors: 400 bad shape, out-of-bounds crop, every moment past every end, or not an image or video; 404 unknown name; 413 over the decode pixel limit; 422 undecodable file. See *Assessing a run's output* in [the workflow guide](WORKFLOW_GUIDE.md#assessing-a-runs-output) |
+| `compare_outputs(names, at=None, crop=None, baseline=None, max_dimension=384, texture_bands=False, workspace=None)` | `names`, `at`, `crop`, `baseline`, `max_dimension`, `texture_bands`, `workspace` | Read-only, queues nothing: 2-8 outputs (gallery names or `asset:`) as one grid, a row per output (labelled with its row number and short name, `(baseline)` on the baseline) and a column per moment, each cell stamped `frame N`, with PSNR, SSIM and mean absolute difference against `baseline` (default the first name). `at` is 1-8 moments (seconds or `"frame:N"`, never negative: unlike `get_output_frames`, a negative moment is a 400, not counted back from the end); they resolve to one frame index against the baseline (the first video if the baseline is a still) and every output is read at that same frame number; default 4 evenly spaced frames of the shortest video. A frame past an output's end is a blank cell and its metrics cell says `past end (N frames)`; `fps_differs` flags videos with different frame rates, where the same frame number is not the same second. All stills: one column, `at` ignored with a note; a still among videos repeats in every column. Metrics are per non-baseline output at source resolution: `psnr_db` (`identical` when equal), `ssim` (luma, 11x11 Gaussian window, sigma 1.5) and `mean_abs_diff` (0-255), per column and as mean/min/max. `crop` is `[x,y,w,h]`: one shared box is the region shown and measured (`region` reads `crop [...]`); a list of one box per name is display only and metrics stay on the full frame (no crop is `full frame`). A crop must lie wholly inside every output's frame, else 400 (stricter than `get_output_frames`, which clamps). An output whose size differs from the baseline gets null metrics with the reason `size differs from baseline: WxH vs WxH` - it is never resized. `max_dimension` is the longest side of each cell; the grid is held to 4 MiB and shrunk whole when over (never below 64 px per cell), `downscaled_to` null when nothing shrank. `texture_bands=true` adds per-output RMS texture energy by wavelength (`fine` 2-8 px, `mid` 8-32, `coarse` 32-128) for `luma`, `chroma_rg` and `chroma_bg` over the measured region, plus `texture_ratio` against the baseline for non-baseline outputs; it needs one shared `crop` (at least 16 px a side) or none, and allows a single name (the baseline, so no ratios). It is a measurement, not a score, and means something only on a static box. Returns the grid image and a text summary. Errors: 400 bad shape, out-of-bounds crop, every moment past every end, or not an image or video; 404 unknown name; 413 over the decode pixel limit; 422 undecodable file. See *Assessing a run's output* in [the workflow guide](WORKFLOW_GUIDE.md#assessing-a-runs-output) |
 | `download_output(name, destination=None, overwrite=False, workspace=None)` | `name`, `destination`, `overwrite`, `workspace` | Save one output file to local disk, of any content type. `destination` may be a full path or a directory; `~` expands and missing parent directories are created. `overwrite=True` is required to replace a file already at the resolved path. Over the stdio `dw-mcp`, omitting `destination` saves under the output's own name in the current working directory. Over a `dw.serve --mcp` endpoint the file lands on the server confined to that workspace (a relative path is joined onto it), and `destination` is required there - an omitted one is refused rather than dropped loose in the workspace root, where nothing can find or delete it later (#353); use the `url` `list_gallery` reports, `get_output_image`/`get_output_audio`/`get_output_frames` for inline content, or `keep_output` to make it a named asset instead. Returns nothing to the conversation but where the file landed — unlike the other media tools, the point is a file on disk, not a payload in context. Writes on the machine running the MCP server - over `dw.serve --mcp` that is the GPU box. A write that fails there (a path that exists only on the client, for instance) comes back as an error naming the server-side write and the client-side alternatives, not as an anonymous tool failure. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
 | `delete_output(name=None, workspace=None, job_id=None)` | exactly one of `name` / `job_id`, `workspace` | Permanently remove one generated file from the output directory, or - with `name` a `<workflow>/<run id>` run directory, or with `job_id` - a whole run. By `job_id` the server reads the run directory from the job record and the root the job ran in (`DELETE /api/jobs/{id}/run`), and the reply is `job_id`, `run_dir`, `deleted` and `run_swept`; a job that never wrote a run directory, an unknown one, or one still queued or running is an error. `workspace` names the workspace for a `name` delete without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
 
diff --git a/docs/SERVER.md b/docs/SERVER.md
index 1690f839..10d0bddc 100644
--- a/docs/SERVER.md
+++ b/docs/SERVER.md
@@ -644,8 +644,9 @@ The editor's forms come from these; they are just as usable from scripts:
   `asset:` references, one row each), `at` (1-8 moments, seconds or `"frame:N"`;
   default 4 evenly spaced frames of the shortest video), `crop` (`[x, y, w, h]`: the
   region shown and measured; or one box per name, shown only), `baseline` (default
-  the first name), `max_dimension` (default 384, longest side of each cell) and
-  `max_total_bytes`. Moments resolve to one frame index against the baseline (the
+  the first name), `max_dimension` (default 384, longest side of each cell),
+  `texture_bands` (default false: add per-output texture energy by wavelength band,
+  below) and `max_total_bytes`. Moments resolve to one frame index against the baseline (the
   first video if the baseline is a still) and every output is read at that frame
   number; a frame past an output's end is a blank cell. A still among videos repeats
   in every column; all stills give one column and `at` is ignored with a note. A crop
@@ -663,7 +664,16 @@ The editor's forms come from these; they are just as usable from scripts:
   `identical` when equal and SSIM is on luma (11x11 Gaussian window, sigma 1.5). An
   output whose size differs from the baseline gets null metrics and the `reason`
   `size differs from baseline: WxH vs WxH`, never a resize; a cell past an end says
-  `past end (N frames)`. Errors: 400 bad request, out-of-bounds crop, every moment
+  `past end (N frames)`. With `texture_bands`, the reply adds a `texture_bands` header
+  (`fine` "2-8 px", `mid` "8-32 px", `coarse` "32-128 px") and a `texture` list, one
+  entry per output: `name`, `row`, `baseline` and `cells`, each with `texture`
+  (`luma`, `chroma_rg`, `chroma_bg`, each `{fine, mid, coarse}`, RMS on a 0-255 scale
+  after a least-squares plane is removed), `texture_ratio` (non-baseline only: output
+  over baseline, null where the baseline band is 0) and `reason`. Each non-baseline
+  `metrics[].cells[]` entry carries `texture` and `texture_ratio` too. A band longer
+  than the box's short side is null. One name is allowed with bands (it is the
+  baseline: no ratios). `crop` must be one shared box (a per-name list is a 400:
+  "texture bands need one shared box") of at least 16 px a side. Errors: 400 bad request, out-of-bounds crop, every moment
   past every end, or not an image or video; 404 unknown name; 413 over the decode
   pixel limit; 422 undecodable file
 - `POST /api/gallery/archive` — `{"names": [...]}` (1-1000) bundles a
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index e6294d53..694fc3ad 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1117,7 +1117,9 @@ story wanted is the cut working, and a level step into a quieter scene can
 be the scene. Tell the person what you confirmed, not what the probe
 reported.
 
-**Comparing outputs.** `compare_outputs(names)` (`POST /api/gallery/compare`)
+### Comparing outputs
+
+`compare_outputs(names)` (`POST /api/gallery/compare`)
 lays 2-8 outputs (variations, reruns, the same shot with and without a speed
 setting) in one grid, a row per output and a column per moment, every output
 read at the same frame number, and measures each against a `baseline` (default
@@ -1134,7 +1136,32 @@ cache, attention backend or quantization) but the shot is the same; about 19 dB
 means it was re-staged (the composition changed). Look at the grid before
 trusting a number.
 
-**Remediation.** A *recut* reruns only the join over the shots the run
+**Texture bands.** `compare_outputs(names, texture_bands=true)` adds, per output,
+the RMS energy of the picture's texture by wavelength over the measured region:
+`fine` (2-8 px), `mid` (8-32 px) and `coarse` (32-128 px), in three channels,
+`luma`, `chroma_rg` (R-G) and `chroma_bg` (B-G), on a 0-255 scale. A
+least-squares plane (a lighting gradient) is removed, the region is windowed,
+and the energy is summed over rings of the 2D spectrum. A band longer than the
+box's short side is null. Each non-baseline cell also carries `texture_ratio`
+(output over baseline, null where the baseline band is 0). With bands, one name
+is allowed: it is the baseline, so there are no ratios; use several `at`
+moments and read the numbers across. A `crop` must be one shared box of at
+least 16 px a side; no crop is the full frame.
+- `fine` suggests grain, edge crispness and encoder texture. `mid` suggests
+  blotches, stripes and coarsening. `coarse` suggests large patches and shading.
+- It is a measurement, not a score. `mid` rising can be real detail. Every band
+  falling can be blur or over-smoothing, which the eye often reads as "cleaner".
+- It means something only on a box with static framing and lighting. Pick a
+  static region with `crop`; on moving content the numbers say nothing.
+- Everything is measured after encode, and dw chains write only MP4, so there
+  are no pre-encode frames. Encoder texture sits mostly in `fine`.
+- To A/B, branch from one clean first segment, keep the seeds, prompts and
+  references, change one variable at a time, and compare the same moments in
+  the same box.
+
+### Remediation
+
+A *recut* reruns only the join over the shots the run
 already made: each entry in `videos` is `output:` + the run's
 `intermediate/` shot file. That is cheap, and generates nothing new. A
 *regenerate* is a new run, so quote its `plan.estimate` first.
diff --git a/plugins/dw/skills/minimax-h3/SKILL.md b/plugins/dw/skills/minimax-h3/SKILL.md
index 69d80a25..841c4bc4 100644
--- a/plugins/dw/skills/minimax-h3/SKILL.md
+++ b/plugins/dw/skills/minimax-h3/SKILL.md
@@ -64,6 +64,9 @@ H3 generates video and audio together. Every template fits a 24 GB card.
   sets the length), `templates/minimax/chain-matched-and-aligned`
   (per-segment prompts). Before composing a chain, read `references/chains.md`:
   what a guide seam trims and carries, and how to hold drift down.
+  To compare two takes' texture on a static region, use
+  `compare_outputs(texture_bands=true)` (workflows guide, *Comparing outputs*,
+  *Texture bands*).
 - **A piece with cuts**: fresh shots from shared portraits, then a concat.
   `templates/minimax/dialogue-short` (Z-Image draws the cast, one shot per
   `shots` entry, `concat_videos` splices) and `templates/minimax/music-video`
diff --git a/plugins/dw/skills/minimax-h3/references/cuts.md b/plugins/dw/skills/minimax-h3/references/cuts.md
index 258f6dd8..471d6ed4 100644
--- a/plugins/dw/skills/minimax-h3/references/cuts.md
+++ b/plugins/dw/skills/minimax-h3/references/cuts.md
@@ -125,3 +125,7 @@ would skip `seam_frame_jump`).
 - Jobs: shot 1 `71a3c4ee9eba`; arm A `e1d37983f04a`; arm B shot 3
   `d4be72601012`, shot 5 `1ae6a04cb793`, bridge 2 `932c2add5238`, bridge 4
   `305f70b310b9`; joins and measurements `8a8adfbf3c30`.
+
+Drift above is lightness, saturation and contrast. For texture (grain, blotching,
+smoothing) over a static region, `compare_outputs(texture_bands=true)` reports
+energy by band; see the workflows guide, *Comparing outputs*, *Texture bands*.
diff --git a/tests/test_compare_route.py b/tests/test_compare_route.py
index 5908b112..bcf61bf8 100644
--- a/tests/test_compare_route.py
+++ b/tests/test_compare_route.py
@@ -323,3 +323,102 @@ def test_an_asset_reference_compares_with_an_output(asset_server, tmp_path, outp
         body = response.json()
         assert body["baseline"] == "asset:uploads/ref.png"
         assert body["metrics"][0]["ssim"]["mean"] == 1.0
+
+
+# Texture bands (#1152)
+
+
+def write_smooth_png(path, width=64, height=64, seed=0):
+    from scipy.ndimage import gaussian_filter
+
+    plane = gaussian_filter(
+        numpy.random.default_rng(seed).normal(128, 50, (height, width)), 1.0
+    )
+    pixels = numpy.stack([numpy.clip(plane, 0, 255).astype(numpy.uint8)] * 3, axis=-1)
+    Image.fromarray(pixels).save(path)
+
+
+def test_one_name_needs_texture_bands(server, outputs):
+    write_smooth_png(outputs / "a.png")
+
+    with server(success_script) as client:
+        refused = post(client, names=["a.png"])
+        assert refused.status_code == 400
+        assert "texture_bands" in refused.json()["detail"]
+
+        accepted = post(client, names=["a.png"], texture_bands=True)
+        assert accepted.status_code == 200
+        body = accepted.json()
+        assert body["metrics"] == []
+        assert len(body["texture"]) == 1 and len(body["texture"][0]["cells"]) == 1
+        assert body["texture"][0]["cells"][0]["texture"]["luma"]["fine"] > 0
+
+
+def test_texture_bands_refuse_per_name_crops_and_a_tiny_crop(server, outputs):
+    write_smooth_png(outputs / "a.png")
+    write_smooth_png(outputs / "b.png", seed=1)
+    names = ["a.png", "b.png"]
+
+    with server(success_script) as client:
+        each = post(
+            client,
+            names=names,
+            crop=[[0, 0, 32, 32], [0, 0, 32, 32]],
+            texture_bands=True,
+        )
+        assert each.status_code == 400 and "one shared box" in each.json()["detail"]
+
+        tiny = post(client, names=names, crop=[0, 0, 10, 10], texture_bands=True)
+        assert tiny.status_code == 400
+        assert "too small for the 2-8 px band" in tiny.json()["detail"]
+
+
+def test_texture_ratio_is_null_on_a_zero_baseline_through_the_route(server, outputs):
+    Image.fromarray(numpy.full((64, 64, 3), 90, numpy.uint8)).save(outputs / "flat.png")
+    write_smooth_png(outputs / "b.png", seed=2)
+
+    with server(success_script) as client:
+        body = post(client, names=["flat.png", "b.png"], texture_bands=True).json()
+
+        base, other = body["texture"]
+        assert base["baseline"] is True and other["baseline"] is False
+        cell = other["cells"][0]
+        assert cell["texture"]["luma"]["fine"] > 0
+        assert cell["texture_ratio"]["luma"]["fine"] is None
+        assert body["metrics"][0]["cells"][0]["texture_ratio"] == cell["texture_ratio"]
+
+
+def test_texture_bands_through_the_route_on_real_files(server, outputs):
+    """A still, an identical copy and a video decoded for real: ratios of one
+    for the copy, a cell per video moment, a crop honoured."""
+    write_smooth_png(outputs / "a.png", seed=3)
+    write_smooth_png(outputs / "copy.png", seed=3)
+    write_ramp_mp4(outputs / "v.mp4", width=64, height=32)
+
+    with server(success_script) as client:
+        body = post(
+            client, names=["a.png", "copy.png"], texture_bands=True, crop=[0, 0, 48, 48]
+        ).json()
+        ratios = body["texture"][1]["cells"][0]["texture_ratio"]
+        assert ratios["luma"]["fine"] == 1.0
+        assert body["texture_bands"]["fine"] == "2-8 px"
+        assert body["region"] == "crop [0, 0, 48, 48]"
+
+        video = post(
+            client,
+            names=["v.mp4"],
+            texture_bands=True,
+            at=["frame:0", "frame:12", "frame:23"],
+        ).json()
+        assert len(video["texture"][0]["cells"]) == 3
+        assert video["texture"][0]["cells"][1]["texture"]["luma"]["fine"] == 0
+
+
+def test_the_reply_has_no_texture_keys_without_texture_bands(server, outputs):
+    write_smooth_png(outputs / "a.png")
+    write_smooth_png(outputs / "b.png", seed=1)
+
+    with server(success_script) as client:
+        body = post(client, names=["a.png", "b.png"]).json()
+
+        assert "texture" not in body and "texture_bands" not in body
diff --git a/tests/test_mcp_server.py b/tests/test_mcp_server.py
index 2d837148..14883415 100644
--- a/tests/test_mcp_server.py
+++ b/tests/test_mcp_server.py
@@ -887,6 +887,81 @@ def handler(request):
     assert "crop" not in sent[1]
 
 
+BANDS = {"fine": 1.5, "mid": 0.5, "coarse": None}
+TEXTURE = {"luma": BANDS, "chroma_rg": BANDS, "chroma_bg": BANDS}
+TEXTURE_BODY = {
+    **COMPARE_BODY,
+    "texture_bands": {"fine": "2-8 px", "mid": "8-32 px", "coarse": "32-128 px"},
+    "texture": [
+        {
+            "name": "a.mp4",
+            "row": 0,
+            "baseline": True,
+            "cells": [{"texture": TEXTURE, "reason": None}],
+        },
+        {
+            "name": "b.mp4",
+            "row": 1,
+            "baseline": False,
+            "cells": [{"texture": TEXTURE, "texture_ratio": TEXTURE, "reason": None}],
+        },
+    ],
+}
+
+
+def test_compare_outputs_sends_texture_bands_only_when_true():
+    sent = []
+
+    def handler(request):
+        sent.append(json.loads(request.content))
+        return httpx.Response(200, json=COMPARE_BODY)
+
+    client = DwClient(transport=httpx.MockTransport(handler))
+
+    media.compare_outputs(client, ["a.mp4", "b.mp4"], texture_bands=False)
+    media.compare_outputs(client, ["a.mp4"], texture_bands=True)
+
+    assert "texture_bands" not in sent[0]
+    assert sent[1]["texture_bands"] is True
+
+
+def test_compare_summary_prints_the_bands_lines():
+    text = media.compare_summary(TEXTURE_BODY)
+
+    assert "texture bands" in text and "fine 2-8 px" in text
+    assert "- row 1 a.mp4  (baseline) @ frame:0:  Y 1.5/0.5/-" in text
+    assert "- row 2 b.mp4 @ frame:0:  Y 1.5/0.5/-  R-G" in text
+    assert "B-G 1.5/0.5/-  ratio Y 1.5/0.5/-" in text
+    assert "psnr_db mean 31.2" in text
+
+
+def test_compare_summary_keeps_no_metrics_for_an_old_shape_body_only():
+    no_ssim = {
+        "name": "b.mp4",
+        "row": 1,
+        "reason": "size differs",
+        "cells": [],
+        "psnr_db": None,
+        "ssim": None,
+        "mean_abs_diff": None,
+    }
+    old = media.compare_summary({**COMPARE_BODY, "metrics": [no_ssim]})
+    new = media.compare_summary({**TEXTURE_BODY, "metrics": [no_ssim]})
+
+    assert "no metrics - size differs" in old and "texture" not in old
+    assert "no pairwise metrics - size differs" in new
+    assert "no metrics" not in new
+
+
+def test_compare_summary_for_one_name_prints_bands_and_no_metrics_line():
+    body = {**TEXTURE_BODY, "metrics": [], "texture": TEXTURE_BODY["texture"][:1]}
+
+    text = media.compare_summary(body)
+
+    assert "no metrics" not in text
+    assert "- row 1 a.mp4  (baseline) @ frame:0:" in text
+
+
 class RecordingServer:
     def __init__(self):
         self.transport = None
diff --git a/tests/test_media_compare.py b/tests/test_media_compare.py
index 6bfedc49..fc4d8185 100644
--- a/tests/test_media_compare.py
+++ b/tests/test_media_compare.py
@@ -185,3 +185,121 @@ def test_crop_box_refuses_a_malformed_box_as_resolve_crop_box_does():
     with pytest.raises(ValueError) as compare_error:
         crop_box([1, 2, 3], 32, 16, "a.mp4")
     assert str(compare_error.value) == str(frames_error.value)
+
+
+# Texture bands (#1152)
+
+
+def still(name, array):
+    from PIL import Image
+
+    return {"name": name, "kind": "image", "image": Image.fromarray(array)}
+
+
+def smooth_noise(seed=0, size=64):
+    from scipy.ndimage import gaussian_filter
+
+    plane = gaussian_filter(
+        numpy.random.default_rng(seed).normal(128, 50, (size, size)), 1.0
+    )
+    return numpy.stack([numpy.clip(plane, 0, 255).astype(numpy.uint8)] * 3, axis=-1)
+
+
+def run_compare(sources, texture_bands=True, crop=None, at=None):
+    from dw.media_compare import compare
+
+    row, mode = check_request(
+        [s["name"] for s in sources], at, crop, None, texture_bands
+    )
+    return compare(sources, at, crop, row, mode, 128, texture_bands=texture_bands)
+
+
+def test_check_request_allows_one_name_only_with_texture_bands():
+    assert check_request(["a.mp4"], None, None, None, True) == (0, None)
+    with pytest.raises(ValueError, match="compare takes 2 to 8"):
+        check_request(["a.mp4"], None, None, None)
+    with pytest.raises(ValueError, match="compare takes 2 to 8"):
+        check_request(["a.mp4"], None, None, None, False)
+
+
+def test_check_request_refuses_a_per_name_crop_with_texture_bands():
+    with pytest.raises(ValueError, match="one shared box"):
+        check_request(NAMES, None, [[0, 0, 32, 32], [0, 0, 32, 32]], None, True)
+
+
+def test_check_request_refuses_a_shared_crop_too_small_for_the_fine_band():
+    with pytest.raises(ValueError, match="too small for the 2-8 px band"):
+        check_request(NAMES, None, [0, 0, 10, 40], None, True)
+    assert check_request(NAMES, None, [0, 0, 16, 16], None, True) == (0, "shared")
+
+
+def test_compare_reports_bands_for_the_baseline_and_a_ratio_for_the_rest():
+    base = smooth_noise(1)
+    answer = run_compare([still("a.png", base), still("b.png", smooth_noise(2))])
+
+    assert answer["texture_bands"] == {
+        "fine": "2-8 px",
+        "mid": "8-32 px",
+        "coarse": "32-128 px",
+    }
+    first, second = answer["texture"]
+    assert [first["name"], first["row"], first["baseline"]] == ["a.png", 0, True]
+    assert [second["name"], second["row"], second["baseline"]] == ["b.png", 1, False]
+    assert len(first["cells"]) == len(second["cells"]) == 1
+    assert first["cells"][0]["texture"]["luma"]["fine"] > 0
+    assert "texture_ratio" not in first["cells"][0]
+    assert second["cells"][0]["texture_ratio"]["luma"]["fine"] > 0
+    metric_cell = answer["metrics"][0]["cells"][0]
+    assert metric_cell["texture"] == second["cells"][0]["texture"]
+    assert metric_cell["texture_ratio"] == second["cells"][0]["texture_ratio"]
+
+
+def test_the_same_image_twice_has_ratios_of_one():
+    image = smooth_noise(3)
+    answer = run_compare([still("a.png", image), still("b.png", image.copy())])
+
+    ratios = answer["texture"][1]["cells"][0]["texture_ratio"]
+    assert ratios["luma"]["fine"] == 1.0 and ratios["luma"]["mid"] == 1.0
+    # a 64 px box can't hold the 32-128 px band
+    assert ratios["luma"]["coarse"] is None
+
+
+def test_the_ratio_is_none_where_the_baseline_band_is_zero():
+    flat = numpy.full((64, 64, 3), 100, numpy.uint8)
+    answer = run_compare([still("flat.png", flat), still("b.png", smooth_noise(4))])
+
+    cell = answer["texture"][1]["cells"][0]
+    assert cell["texture"]["luma"]["fine"] > 0
+    assert cell["texture_ratio"]["luma"]["fine"] is None
+
+
+def test_one_video_gives_one_texture_entry_with_a_cell_per_moment(tmp_path):
+    from dw import media
+    from tests.test_media_frames import write_ramp_mp4
+
+    path = tmp_path / "a.mp4"
+    write_ramp_mp4(path, frames=24, width=64, height=32)
+    source = {
+        "name": "a.mp4",
+        "kind": "video",
+        "path": str(path),
+        "shape": media.video_shape(str(path)),
+    }
+
+    answer = run_compare([source], at=["frame:0", "frame:10", "frame:20"])
+
+    assert answer["metrics"] == []
+    assert len(answer["texture"]) == 1
+    entry = answer["texture"][0]
+    assert entry["baseline"] is True and len(entry["cells"]) == 3
+    assert all(cell["texture"] is not None for cell in entry["cells"])
+
+
+def test_without_texture_bands_the_reply_has_no_texture_keys():
+    answer = run_compare(
+        [still("a.png", smooth_noise(1)), still("b.png", smooth_noise(2))],
+        texture_bands=False,
+    )
+
+    assert "texture" not in answer and "texture_bands" not in answer
+    assert "texture" not in answer["metrics"][0]["cells"][0]
diff --git a/tests/test_texture_bands.py b/tests/test_texture_bands.py
new file mode 100644
index 00000000..56da4c9e
--- /dev/null
+++ b/tests/test_texture_bands.py
@@ -0,0 +1,102 @@
+"""Texture energy by wavelength band (#1152): what each band measures, on
+synthetic arrays."""
+
+import numpy
+import pytest
+from scipy.ndimage import gaussian_filter
+
+from dw.texture_bands import CHANNELS, ratio, texture_bands
+
+SIZE = 256
+
+
+def grey(plane):
+    """A float plane as a grey uint8 RGB image."""
+    plane = numpy.clip(numpy.rint(plane), 0, 255).astype(numpy.uint8)
+    return numpy.stack([plane] * 3, axis=-1)
+
+
+def stripes(period, amplitude=60):
+    x = numpy.arange(SIZE)[None, :].repeat(SIZE, axis=0)
+    return grey(128 + amplitude * numpy.sin(2 * numpy.pi * x / period))
+
+
+def textured(seed=0):
+    rng = numpy.random.default_rng(seed)
+    return gaussian_filter(rng.normal(128, 50, (SIZE, SIZE)), 1.0)
+
+
+@pytest.mark.parametrize("period, band", [(4, "fine"), (16, "mid"), (64, "coarse")])
+def test_a_stripe_puts_its_energy_in_the_band_of_its_wavelength(period, band):
+    luma = texture_bands(stripes(period))["luma"]
+
+    for other in set(luma) - {band}:
+        assert luma[band] > 10 * luma[other]
+    # a sine of amplitude 60 measures about 60/sqrt(2)
+    assert luma[band] == pytest.approx(42.4, rel=0.15)
+
+
+def test_a_linear_gradient_measures_nothing_in_any_band():
+    y, x = numpy.mgrid[0:SIZE, 0:SIZE]
+    bands = texture_bands(grey(20 + 0.5 * x + 0.3 * y))
+
+    for channel in CHANNELS:
+        for value in bands[channel].values():
+            assert value < 0.5
+
+
+def test_blurring_lowers_every_luma_band():
+    sharp = textured()
+    soft = gaussian_filter(sharp, 3.0)
+
+    before = texture_bands(grey(sharp))["luma"]
+    after = texture_bands(grey(soft))["luma"]
+
+    for band in before:
+        assert after[band] < before[band]
+
+
+def test_a_hue_change_at_constant_luma_moves_chroma_not_luma():
+    base = textured()
+    pattern = 20 * numpy.sin(2 * numpy.pi * numpy.arange(SIZE) / 6)[None, :]
+    pattern = pattern.repeat(SIZE, axis=0)
+    # +d on R and -d*0.299/0.114 ... keep luma: 0.299*dr + 0.114*db = 0
+    red = base + pattern
+    blue = base - pattern * 0.299 / 0.114
+    tinted = numpy.stack([red, base, blue], axis=-1)
+    tinted = numpy.clip(numpy.rint(tinted), 0, 255).astype(numpy.uint8)
+
+    plain = texture_bands(grey(base))
+    changed = texture_bands(tinted)
+
+    assert plain["chroma_rg"]["fine"] < 0.5
+    assert changed["chroma_rg"]["fine"] > 5 * max(plain["chroma_rg"]["fine"], 1)
+    assert changed["chroma_bg"]["fine"] > 5
+    for band in ("fine", "mid", "coarse"):
+        assert changed["luma"][band] == pytest.approx(plain["luma"][band], rel=0.05)
+
+
+def test_a_small_box_holds_only_the_bands_that_fit():
+    bands = texture_bands(numpy.zeros((20, 40, 3), numpy.uint8) + 7)
+
+    for channel in CHANNELS:
+        assert bands[channel]["fine"] is not None
+        assert bands[channel]["mid"] is None
+        assert bands[channel]["coarse"] is None
+
+
+def test_a_box_under_16_px_a_side_is_refused():
+    with pytest.raises(ValueError, match="too small"):
+        texture_bands(numpy.zeros((15, 64, 3), numpy.uint8))
+
+
+def test_ratio_is_none_for_a_zero_or_missing_band_and_one_for_itself():
+    flat = texture_bands(numpy.full((20, 40, 3), 9, numpy.uint8))
+    busy = texture_bands(grey(textured())[:20, :40])
+
+    assert ratio(busy, busy)["luma"]["fine"] == 1.0
+    assert ratio(busy, busy)["luma"]["mid"] is None  # None band stays None
+    # flat baseline: every fine band is exactly 0
+    assert flat["luma"]["fine"] == 0
+    assert ratio(busy, flat)["luma"]["fine"] is None
+    assert ratio(flat, busy)["luma"]["fine"] == 0

From 4f1d392f877a03123324fb863dc8c970c1c04715 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 02:50:09 -0600
Subject: [PATCH 20/74] docs(proposals): #1151 - design record for the H3
 anti-burn continuation probe

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../h3-antiburn-continuation-complete.md      | 132 ++++++++++++++++++
 docs/proposals/todo.md                        |   9 ++
 2 files changed, 141 insertions(+)
 create mode 100644 docs/proposals/complete/h3-antiburn-continuation-complete.md

diff --git a/docs/proposals/complete/h3-antiburn-continuation-complete.md b/docs/proposals/complete/h3-antiburn-continuation-complete.md
new file mode 100644
index 00000000..179892a3
--- /dev/null
+++ b/docs/proposals/complete/h3-antiburn-continuation-complete.md
@@ -0,0 +1,132 @@
+# H3 Anti-Burn LoRA on chain continuation segments (#1151)
+
+Written by model `claude-opus-5-5` via provider `anthropic` (close-out session,
+2026-11-02). Don filed the idea on 2026-11-01 from a review of
+[knoic/ComfyUI-MiniMaxH3-PrefixStream](https://github.com/knoic/ComfyUI-MiniMaxH3-PrefixStream).
+Plan v1 was designed on #1151
+(https://github.com/dkackman/diffusers-workflow/issues/1151#issuecomment-6569178311),
+and Don approved it as written. It was built as one probe stage, #1159. The plan
+comment is still the full design; this file records what shipped and what is
+still open.
+
+## The problem
+
+Texture coarsens with each seam of an H3 chain: skin goes blotchy and
+over-sharpened, fabric turns crunchy, flat regions block up
+(`plugins/dw/skills/minimax-h3/SKILL.md`, "Drift compounds per seam").
+Imagineer13's [H3-Anti-Burn-LoRA](https://huggingface.co/Imagineer13/H3-Anti-Burn-LoRA)
+is trained to prevent that, applied to continuation segments only. The question
+was whether it loads and helps on our stack: diffusers' H3 loader, guide-row
+`fl2va` continuity, the 4-step turbo at 5 steps, SDNQ int4 and top-k sparse
+attention. Its author trained it on masked-latent `ref2va` continuations, lightx2v
+8-step, and int8.
+
+## Verdict: build smaller
+
+Probe only: a load check, an A/B on lem, and one catalog entry. **Cut:
+`chain.continuation_loras`**, a chain option that would apply a LoRA from
+segment 2 on. It would be new workflow syntax.
+*What would bring it back:* Don's taste verdict (Q4) that arm B beats arm A on
+skin and fabric without going waxy. It would then come back as plan v2 on #1151.
+The plan's "Follow-up" section sketches it: the schema's chain block,
+`ChainConfig`, a `set_adapter_scales` call before `_run_once`, `FakePipeline`
+adapter tracking, template variables, a SKILL.md line, and the guide. Before
+that, someone has to show that scale 0 switches the adapter off.
+
+**The plan corrected the issue's premise.** A chained template can't attach a
+LoRA to segments 2+ only without engine code. The chain loop runs one loaded
+pipeline whose LoRA state is set once at load, and `_segment_arguments` never
+touches adapters. The only way today is a hand-built chain: one `fl2va` step
+per segment, each later segment guided by the previous clip's tail via
+`trim_video`, with the guide heads dropped and the clips joined with
+`concat_videos`.
+
+Non-goals that stayed out: engine, schema, MCP or REST changes; a custom key
+converter; changes to any shipped template; a `ref2va` chain; a wider strength
+sweep.
+
+## Plan history
+
+- **v1 (approved, 2026-11-01).** There was one version and no revisions.
+  - Q1 [spend]: yes. The 310 MB download and about an hour of lem, with arm C
+    (0.4) allowed if B looked waxy.
+  - Q2 [trust]: yes. The Imagineer13 safetensors may be loaded under the
+    MiniMax H3 Community License.
+  - Q3 [none]: `guide_frames` 39, the LoRA's training context, rather than
+    the issue's 22.
+  - Q4 [taste]: **still open.** Don judges the comparison comment on #1151
+    (https://github.com/dkackman/diffusers-workflow/issues/1151#issuecomment-6572404958).
+
+## What was built
+
+### Stage 1 (#1159): load check, continuation A/B/C, catalog entry
+
+Commit `dece403d`, merged to `develop` as `adff4958`, and deployed to lem on
+2026-11-02. No code changed.
+
+- **`loras/minimax-h3/h3-antiburn.json`**, `status: trial`. It has
+  `workflow` `["fl2va"]`, `scale` 0.6 with a range of [0.4, 0.8], and
+  `stacks_with` `minimax-h3/turbo-4step-keyframe`. Its `license` is
+  `minimax-h3-community`, the id the catalog already uses, and its
+  `revision` is pinned (`b98eb1d3…`). `use_when` is "continuation segments
+  only, never segment 1; needs a hand-built chain until a chain option
+  exists". `evidence` lists the four jobs below.
+- **Probe workflows** saved in workspace `probe-1151`: `antiburn-load-check`,
+  and `antiburn-chain-ab`, a hand-built 5-segment chain of 18 explicit steps.
+
+**Load check: passed.** The anti-burn file uses ComfyUI `diffusion_model.*`
+keys, but it loads through the stock `load_lora_weights` alongside turbo with
+no unmatched-key warning. `3dc3f60fb217` (turbo + anti-burn 0.6) differs from
+`e82ea2430448` (turbo only). The lead measured PSNR 22.75 dB and SSIM 0.78 on
+one frame set; the tester measured 25.88 dB and 0.828 at frames 0, 61 and 123.
+
+**A/B/C:** seed 42, 960×544, `guide_frames` 39 with the guide carrying audio,
+turbo-4step-keyframe at 5 steps. The join is 464 frames. Segments 1, 3 and 5
+were judged at frames 62, 251 and 421, cropped to skin, cable knit and a flat
+wall.
+
+| Arm | Job | Anti-burn on segments 2-5 | Segment 5 |
+|---|---|---|---|
+| A | `e0c2ff0dd654` | none | Burnt: blotchy, posterised skin, haloed edges, crunchy knit, blocky wall |
+| B | `bf477d3089dd` | 0.6 | Holds segment 1's look. Pores and lines intact, fine knit slightly softened |
+| C | `f8287f133e1c` | 0.4 | In between: blotching and edge halo partly back |
+
+`assess_output` has no burn metric, so the reading rests on the frames.
+The lead's reading is that 0.6 is the right default and that 0.4
+under-corrects over 5 segments. That reading is not the taste verdict.
+
+**Deviations recorded at build:**
+- `license` uses the catalog id `minimax-h3-community`, not the prose name.
+- `stacks_with` names the catalog entry id, not the bare word "turbo".
+- Validate and runtime both warn `adapter_unrecognized`, because the file
+  name names neither fl2v nor ref2v. The entry's description notes this.
+  The tester saw a cosmetic mismatch: `validate_workflow` matches the file to
+  the catalog and doesn't warn on fl2va, but the runtime warning still fires.
+
+**Verify:** the tester verified all seven cases (M-F298 to M-F304) on the
+first pass. M-F300, the `rejected` branch, did not apply. `recommend_loras`
+does not offer the entry for `ref2va` or `t2va` requests. No shipped template
+or skill changed.
+
+## Cost and bounces
+
+| Stage | Bounces | Cost |
+|---|---|---|
+| #1159 | 0 | Not recorded on the stage. Lem time was about the plan's estimate: the load check plus three arms, with C run |
+
+## Deferred and open
+
+- **Q4, Don's taste verdict** on the comparison comment. "Yes" moves the entry
+  to `proven`, adds a line to the H3 skill, and brings back
+  `chain.continuation_loras` as plan v2. "No" moves the entry to `rejected`
+  with his note. Until he rules, the entry stays `trial` and the skill doesn't
+  recommend it.
+- **One seed, one scene.** The card reports ±15 points of variance per chain.
+  The probe shows a large effect, but it can't establish `proven` by itself.
+- **The result comes from a hand-built chain.** It applies to the engine's
+  `chain` only by inference.
+- Untried: `ref2va` continuations, 22-frame guides, other turbo variants.
+- Related: #1150 (latent-tail continuity) attacks the same drift at its
+  source. #1152 (texture-drift measure) has since added `compare_outputs`
+  `texture_bands` (stage #1160, after this probe ran). It could put a number
+  on a rerun.
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index beaa0739..8258ff0a 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -37,6 +37,15 @@ remaining deferred fix is recorded in
 
 ## Shipped since the ranking
 
+- **H3 Anti-Burn LoRA on chain continuation segments** (#1151, stage
+  #1159), probed 2026-11-02. Imagineer13's anti-burn LoRA loads through
+  the stock loader beside turbo. On a seed-42, 5-segment hand-built
+  `fl2va` chain, 0.6 on segments 2-5 held segment 1's skin, knit and wall,
+  where the unaided chain burnt by segment 5. It is in the catalog as
+  `minimax-h3/h3-antiburn`, `trial`, until Don's taste verdict (Q4). The
+  `chain.continuation_loras` option was cut and returns only on a positive
+  Q4: `complete/h3-antiburn-continuation-complete.md`.
+
 - **`pcm_sha256`, an audio identity check over MCP** (#1139, stage #1158),
   shipped 2026-11-02. `get_gallery_metadata`'s `media` now carries a
   SHA-256 of the decoded samples, with `sample_format` and `frames`, for any

From 55819d2fbd3ff7d62650cd2be22d59cd932d6636 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 03:03:57 -0600
Subject: [PATCH 21/74] fix(guides): #1160 - Texture bands is a #### heading
 get_guide reaches by name

The tester's C-F669 fetched get_guide("workflows", section="Texture bands")
and was refused: it was a bold paragraph, and the matcher stopped at ###.
It is now a #### heading under Comparing outputs (which still carries it),
and _extract_subsection tries #### headings as a second pass after every ###,
so existing resolutions are unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/WORKFLOW_GUIDE.md      |  4 ++-
 dw/server/guides.py         | 52 ++++++++++++++++++++++-------------
 tests/test_server_guides.py | 54 ++++++++++++++++++++++++++++++++++++-
 3 files changed, 90 insertions(+), 20 deletions(-)

diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index 694fc3ad..1822f462 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1136,7 +1136,9 @@ cache, attention backend or quantization) but the shot is the same; about 19 dB
 means it was re-staged (the composition changed). Look at the grid before
 trusting a number.
 
-**Texture bands.** `compare_outputs(names, texture_bands=true)` adds, per output,
+#### Texture bands
+
+`compare_outputs(names, texture_bands=true)` adds, per output,
 the RMS energy of the picture's texture by wavelength over the measured region:
 `fine` (2-8 px), `mid` (8-32 px) and `coarse` (32-128 px), in three channels,
 `luma`, `chroma_rg` (R-G) and `chroma_bg` (B-G), on a 0-255 scale. A
diff --git a/dw/server/guides.py b/dw/server/guides.py
index c20c668e..df0e9268 100644
--- a/dw/server/guides.py
+++ b/dw/server/guides.py
@@ -85,6 +85,12 @@
 # because it isn't in the listing is the gap #503 reported.
 SUBSECTION_PATTERN = re.compile(r"^### (.+)$", re.M)
 
+# One level deeper, as a second pass only: a `####` heading is named content
+# too (the workflows guide's *Texture bands*, which plugin skills point at by
+# name, #1160), but trying it after every `###` keeps each term a `###`
+# already resolved resolving to the same place.
+MINOR_SUBSECTION_PATTERN = re.compile(r"^(###|####) (.+)$", re.M)
+
 # A doc's link to an example is a repo-relative path, which resolves for a
 # reader with a checkout and dead-ends for the one these guides are served
 # to: an agent holding the MCP and nothing else. Said once, on a payload
@@ -181,30 +187,40 @@ def _extract_subsection(text, section):
     a subsection heading is usually a full sentence ("One step per entry:
     for_each"), and the term a caller actually has in hand is the concept
     inside it ("for_each"), not the sentence.
+
+    A `####` heading is tried only after no `###` matched, and runs to the
+    next `###` or `####` heading (or the end of its `##` section).
     """
     wanted = _normalized(section)
     if not wanted:
         return None
     section_matches = list(SECTION_PATTERN.finditer(text))
-    for index, section_match in enumerate(section_matches):
-        section_end = (
-            section_matches[index + 1].start()
-            if index + 1 < len(section_matches)
-            else len(text)
-        )
-        section_text = text[section_match.start() : section_end]
-        sub_matches = list(SUBSECTION_PATTERN.finditer(section_text))
-        for sub_index, sub_match in enumerate(sub_matches):
-            normalized_heading = _normalized(sub_match.group(1))
-            if wanted != normalized_heading and wanted not in normalized_heading:
-                continue
-            sub_end = (
-                sub_matches[sub_index + 1].start()
-                if sub_index + 1 < len(sub_matches)
-                else len(section_text)
+    for pattern, heading_group in (
+        (SUBSECTION_PATTERN, 1),
+        (MINOR_SUBSECTION_PATTERN, 2),
+    ):
+        for index, section_match in enumerate(section_matches):
+            section_end = (
+                section_matches[index + 1].start()
+                if index + 1 < len(section_matches)
+                else len(text)
             )
-            content = section_text[sub_match.start() : sub_end].rstrip() + "\n"
-            return sub_match.group(1), content, section_match.group(1)
+            section_text = text[section_match.start() : section_end]
+            sub_matches = list(pattern.finditer(section_text))
+            for sub_index, sub_match in enumerate(sub_matches):
+                if heading_group == 2 and sub_match.group(1) != "####":
+                    continue
+                heading = sub_match.group(heading_group)
+                normalized_heading = _normalized(heading)
+                if wanted != normalized_heading and wanted not in normalized_heading:
+                    continue
+                sub_end = (
+                    sub_matches[sub_index + 1].start()
+                    if sub_index + 1 < len(sub_matches)
+                    else len(section_text)
+                )
+                content = section_text[sub_match.start() : sub_end].rstrip() + "\n"
+                return heading, content, section_match.group(1)
     return None
 
 
diff --git a/tests/test_server_guides.py b/tests/test_server_guides.py
index 45ba1d45..8132f5c5 100644
--- a/tests/test_server_guides.py
+++ b/tests/test_server_guides.py
@@ -37,7 +37,16 @@ def checkout(tmp_path, monkeypatch):
         "### One step per entry: for_each\n\n"
         "for_each runs a step once per entry.\n\n"
         "### previous_result\n\n"
-        "previous_result chains steps.\n"
+        "previous_result chains steps.\n\n"
+        "#### Steps in detail\n\n"
+        "a minor heading under previous_result.\n\n"
+        "## Comparing\n\n"
+        "### Comparing outputs\n\n"
+        "the grid.\n\n"
+        "#### Texture bands\n\n"
+        "bands by wavelength.\n\n"
+        "### Remediation\n\n"
+        "recut or regenerate.\n"
     )
     monkeypatch.setattr(guides, "__file__", str(root / "dw" / "server" / "guides.py"))
     monkeypatch.setattr(
@@ -180,6 +189,37 @@ def test_a_section_match_wins_over_a_subsection_match(self, checkout):
         assert guide["section"] == "Cross-Step Data Flow"
         assert "parent_section" not in guide
 
+    def test_a_minor_heading_is_reachable_by_name(self, checkout):
+        # #1160: a `####` heading is named content a skill points at; it
+        # runs to the next `###`/`####` and names its `##` section.
+        guide = guides.get_guide("workflows", section="Texture bands")
+
+        assert guide["section"] == "Texture bands"
+        assert guide["parent_section"] == "Comparing"
+        assert "bands by wavelength." in guide["content"]
+        assert "recut or regenerate." not in guide["content"]
+        assert "the grid." not in guide["content"]
+
+    def test_a_subsection_still_carries_its_minor_headings(self, checkout):
+        guide = guides.get_guide("workflows", section="Comparing outputs")
+
+        assert guide["section"] == "Comparing outputs"
+        assert "the grid." in guide["content"]
+        assert "bands by wavelength." in guide["content"]
+        assert "recut or regenerate." not in guide["content"]
+
+    def test_a_subsection_match_wins_over_a_minor_heading_match(self, checkout):
+        # The `####` pass runs only when no `###` matched, so a term a `###`
+        # heading already resolved still lands there.
+        assert (
+            guides.get_guide("workflows", section="previous_result")["section"]
+            == "previous_result"
+        )
+        assert (
+            guides.get_guide("workflows", section="Steps in detail")["section"]
+            == "Steps in detail"
+        )
+
     def test_a_section_linking_an_example_says_how_to_reach_it(self, checkout):
         """A guide's link to an example is a repo path. The reader these
         guides exist for has no checkout - it has the MCP and nothing else -
@@ -250,6 +290,18 @@ def test_the_authoring_section_is_reachable_by_name(self):
 
         assert guide["section"] == "Authoring a workflow from an agent"
 
+    def test_the_texture_bands_heading_is_reachable(self):
+        """#1160: the minimax-h3 skill points at *Comparing outputs*,
+        *Texture bands*; both names resolve, and the first carries the second."""
+        bands = guides.get_guide("workflows", section="Texture bands")
+        assert bands["section"] == "Texture bands"
+        assert "texture_bands=true" in bands["content"]
+        assert "not a score" in bands["content"]
+
+        comparing = guides.get_guide("workflows", section="Comparing outputs")
+        assert comparing["section"] == "Comparing outputs"
+        assert "#### Texture bands" in comparing["content"]
+
     def test_the_for_each_subsection_is_reachable_without_its_parent(self):
         """#503: both terms the issue's repro used reach the `###` subsection
         directly, rather than only through its `##` parent."""

From c121d01019152065e44c7c45353632ea13be3c3b Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 03:13:31 -0600
Subject: [PATCH 22/74] docs(proposals): #1152 - design record for texture-band
 drift measurement

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../complete/texture-bands-complete.md        | 109 ++++++++++++++++++
 docs/proposals/todo.md                        |   9 ++
 2 files changed, 118 insertions(+)
 create mode 100644 docs/proposals/complete/texture-bands-complete.md

diff --git a/docs/proposals/complete/texture-bands-complete.md b/docs/proposals/complete/texture-bands-complete.md
new file mode 100644
index 00000000..65b3247b
--- /dev/null
+++ b/docs/proposals/complete/texture-bands-complete.md
@@ -0,0 +1,109 @@
+# Texture-band drift measurement on `compare_outputs` (#1152)
+
+Written by model `claude-opus-5-5` via provider `anthropic` (close-out session,
+2026-11-02). Don filed the idea on 2026-11-01 from a review of
+[knoic/ComfyUI-MiniMaxH3-PrefixStream](https://github.com/knoic/ComfyUI-MiniMaxH3-PrefixStream)
+(MIT). Plan v1 was designed on #1152
+(https://github.com/dkackman/diffusers-workflow/issues/1152#issuecomment-6569250864),
+and Don approved it as written. It was built as one stage, #1160. The plan
+comment is still the full design; this file records what shipped and what is
+still open.
+
+## The problem
+
+H3 chain texture drift (blotches, stripes, coarsening, or the over-smoothing a
+fix can cause) had no number, so drift A/Bs such as #1151 rested on the eye
+alone. `minimax-h3/references/cuts.md` measured drift as lightness, saturation
+and contrast only.
+
+## Verdict: build smaller
+
+One opt-in parameter on `compare_outputs`. No new tool and no assess probe.
+
+**Cut**, with what brings each back:
+- **`region` + bands on `assess_output`.** That would be a fourth name for the
+  crop concept, a second full-resolution decode, and a break in the "exactly
+  three probes" invariant. *Comes back* if agents need bands inside an
+  unattended assess pass (for example a seam rule), shown by a field report.
+- **Pre-encode PNG export of chain segments** (a hook in
+  `dw/pipeline_processors/chain.py`). Chains only ever write MP4, so every
+  measurement includes encoder texture, mostly in the 2-8 px band. *Comes back*
+  if an A/B under this tool lands on a fine-band difference that encoding could
+  explain.
+- **Any rule, threshold or verdict on the bands.** The output stays a
+  measurement.
+
+**The plan corrected the issue on three points:**
+- `compare_outputs` metrics were all pairwise against the baseline. Bands are
+  per output, so the baseline gets values too, plus a ratio to the baseline per
+  band on every other output.
+- `compare_outputs` needed 2+ names. Within-chain drift (segments 1, 3, 5 of one
+  video) needs one, so `texture_bands=true` lowers the minimum to 1 (Don's Q2).
+- "Measure before encode" isn't possible in dw today (see Cut). The guide says
+  so.
+
+## What shipped (stage #1160)
+
+- **`dw/texture_bands.py`**: a pure function on an RGB array. For each channel
+  (Y with ITU-601 weights, R−G and B−G, on the 0-255 scale) it removes a
+  least-squares plane, applies a 2D Hann window, runs `rfft2`, and reports RMS
+  over radial annuli. The bands are `fine` 2-8 px, `mid` 8-32 and `coarse`
+  32-128. Values are rescaled for the window, so a sine of amplitude A reads
+  about A/√2. A band longer than the box's short side is `null`.
+- **`compare_outputs(texture_bands=true)`** on the MCP tool, the REST compare
+  body (`CompareRequest.texture_bands`) and `dw/media_compare.py`. It is off by
+  default, and the payload and summary are byte-identical without it.
+  - One name is accepted with it. Without it, 2-8 names are still required.
+  - A per-name crop list with bands is refused (bands need one shared box). A
+    shared crop under 16 px a side is refused.
+  - The reply adds a `texture_bands` header (band → px range) and a top-level
+    `texture` list with one entry per output, `{name, row, baseline, cells:
+    [{texture, texture_ratio, reason}]}`. `texture_ratio` (output ÷ baseline,
+    `null` where the baseline band is 0) is set on non-baseline outputs. Both
+    are also copied into the existing pairwise `metrics` cells.
+  - The MCP summary adds a "texture bands (RMS, a measurement not a score)"
+    block.
+- **Docs:** a `#### Texture bands` subsection under `### Comparing outputs` in
+  `docs/WORKFLOW_GUIDE.md` (the caveats, measuring after encode, the A/B
+  method). Also `docs/MCP.md`, `docs/SERVER.md`, `docs/ARCHITECTURE.md` (Output
+  comparison row) and `docs/ATTRIBUTIONS.md` (the PrefixStream credit). Pointers
+  in `plugins/dw/skills/minimax-h3/SKILL.md` and `references/cuts.md`. The
+  OpenAPI document and UI schema were regenerated. The tool docstring grew by one
+  clause, and the 16,000-token surface budget held without being raised.
+- **Guide lookup:** `dw/server/guides.py` `_extract_subsection` now tries `####`
+  headings in a second pass, after no `###` heading matched, so
+  `get_guide("workflows", section="Texture bands")` resolves. Names that resolved
+  before still resolve to the same place. A side effect: existing `####`
+  headings such as "On-demand components" now resolve too.
+
+**Deviations from the plan**, all recorded on #1160:
+1. The baseline has no `metrics` entry, so per-output band numbers live in the
+   top-level `texture` list as well as in the non-baseline `metrics` cells.
+2. "Comparing outputs" and "Remediation" in the workflows guide were bold
+   paragraphs that `get_guide` couldn't reach. Both became `###` headings.
+3. No separate plugin version bump: the plugin's version follows the engine's
+   and the release script bumps both.
+
+## Acceptance
+
+Eight cases, C-F662 to C-F669 in `regression-suite-complete.md`, written by the
+tester from the plan. All eight pass on `develop` @ `768400da` on lem, and they
+are now ordinary regression cases.
+
+## Bounces
+
+- **#1160: 1 bounce.** C-F669 step 2: `get_guide("workflows",
+  section="Texture bands")` was refused because *Texture bands* was a bold
+  paragraph. The fix made it a `####` heading and taught the guide matcher a
+  `####` pass. It verified on the second pass.
+
+## Cost
+
+The stage comments give no `usage:` figures, so no cost is recorded. The plan
+estimated $4-6.
+
+## Still open
+
+- No consumer has used it yet. #1151's taste verdict (its Q4) and #1150 (parked)
+  are the expected first users.
+- The three cuts above, each with its trigger.
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index 8258ff0a..0cd97d2c 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -37,6 +37,15 @@ remaining deferred fix is recorded in
 
 ## Shipped since the ranking
 
+- **Texture-band drift measurement on `compare_outputs`** (#1152, stage
+  #1160), shipped 2026-11-02. `compare_outputs(texture_bands=true)` reports
+  RMS energy in the fine 2-8, mid 8-32 and coarse 32-128 px bands (luma, R−G,
+  B−G) per output per moment over one shared crop, plus ratios to the
+  baseline. It accepts one name, so one chain's segments can be compared. It
+  is a measurement, not a score. The `assess_output` shape, pre-encode frame
+  export and any thresholds were cut:
+  `complete/texture-bands-complete.md`.
+
 - **H3 Anti-Burn LoRA on chain continuation segments** (#1151, stage
   #1159), probed 2026-11-02. Imagineer13's anti-burn LoRA loads through
   the stock loader beside turbo. On a seed-42, 5-segment hand-built

From b77ea8e1775866a850981dde67e5420ff1d880d1 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:06:37 -0600
Subject: [PATCH 23/74] docs(proposals): #1141 - record fix-forward #1164
 (C-F647 amended by ruling)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/proposals/complete/cinematic-controls-complete.md | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/docs/proposals/complete/cinematic-controls-complete.md b/docs/proposals/complete/cinematic-controls-complete.md
index c709c15e..42442fbc 100644
--- a/docs/proposals/complete/cinematic-controls-complete.md
+++ b/docs/proposals/complete/cinematic-controls-complete.md
@@ -148,8 +148,13 @@ skill defines that marking for a future trial.
 
 - **D's estimate.** The plan said `validate_workflow` "quotes a near-zero
   GPU estimate". The animatic loads no model, so the estimate is reported
-  as `unknown` with the reason "task-only workflow". Don noted this before
-  the spec, and C-F647 tests it.
+  as `unknown` with the reason "task-only workflow" until the box has run
+  it. After that it quotes the template's own run history (`basis:
+  "observed"`, `minutes: 0.0`), by design (#439/#921). The final check
+  failed C-F647 on that, and fix-forward #1164 took it to Don as a record
+  conflict. He ruled to amend the case, not the engine: C-F647 accepts
+  `unknown` with the task-only reason, or `observed` with `minutes` under
+  0.1 (case change dkackman/harnest#174). No code changed.
 - **C's caps.** Beyond the plan's 600 s cap, C adds a 36000-frame cap and a
   4 GiB run-time byte budget. So 600 s at 120 fps is refused.
 - **C's silence.** The plan said "an AudioVideo that is silent". After the
@@ -174,6 +179,7 @@ skill defines that marking for a future trial.
 | E #1146 | 0 | |
 | F #1147 | 0 | |
 | G #1148 | 0 | |
+| Final check | 1 | C-F647: the `animatic` estimate was `observed`, not `unknown`. A record conflict, not a build miss: fix-forward #1164 carried Don's ruling to amend the case; re-run passed on develop @ 06f08f37. |
 
 Cost: no stage comment recorded `usage:` figures, so none is given here.
 G used the approved ~1 h of lem GPU (18 clips) plus the K6 download above.

From 5e8460feef370509a45a89e3b9c9f2901fab2a92 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:18:55 -0600
Subject: [PATCH 24/74] docs: update README and SERVER documentation; add new
 images and screenshot script

---
 README.md                         | 410 ++++++++++++++----------------
 docs/SERVER.md                    |  23 ++
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 docs/img/ui-gallery-dark.jpg      | Bin 0 -> 381658 bytes
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diff --git a/README.md b/README.md
index 75769632..bdff0347 100644
--- a/README.md
+++ b/README.md
@@ -4,261 +4,229 @@
 
 Your GPU, as something an agent can drive.
 
-diffusers-workflow wraps the [Hugging Face Diffusers library](https://github.com/huggingface/diffusers)
-in an engine that runs image, video and audio generation as jobs, and puts
-two front ends on it: an **MCP server**, so Claude Code (or any MCP client)
-can author, run and inspect generations; and a **web UI** for doing the same
-by hand. A CLI sits underneath for when you want neither.
+diffusers-workflow runs [Hugging Face Diffusers](https://github.com/huggingface/diffusers)
+image, video and audio generation as jobs on your own GPU. **An agent does the work.**
+Claude Code, or any MCP client, picks or writes the workflow, checks what it will
+cost, runs it, and looks at what came back. **You steer from the browser.** Every
+workflow, run and output the agent made is in the web UI, where you can change a
+variable, run it again, compare takes and keep the one you like.
 
 **Python 3.12-3.14 | CUDA (NVIDIA) | MPS (Apple Silicon) | CPU**
 
 <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="docs/img/ui-workflows-dark.png">
-  <img alt="The workflow browser: every workflow as a card with its description, output kinds, and variables" src="docs/img/ui-workflows.png">
+  <source media="(prefers-color-scheme: dark)" srcset="docs/img/ui-overview-dark.jpg">
+  <img alt="A workspace's overview in the web UI: recent outputs from an agent-made musical trailer, its jobs, and the workflows the agent wrote" src="docs/img/ui-overview.jpg">
 </picture>
 
 ## Getting started
 
-**1. Install.** The script picks the right torch build for your platform,
-creates a virtual environment and installs everything, MCP server included.
+**1. Install.** The script picks the right torch build for your platform, creates a
+virtual environment and installs everything, MCP server included.
 
 ```bash
-# Linux / macOS
-bash ./install.sh
-source ./activate
-
-# Windows
-.\install.ps1
-.\venv\scripts\activate
+bash ./install.sh && source ./activate      # Linux / macOS
+.\install.ps1; .\venv\scripts\activate      # Windows
 ```
 
-`python -m dw.test` runs a small built-in workflow end to end - it downloads
-SD 1.5 (a few GB) and generates one image on whichever accelerator was found.
-
-**Or run it in Docker** (Linux with an NVIDIA GPU). The image
-[`dkackman/diffusers-workflow`](https://hub.docker.com/r/dkackman/diffusers-workflow)
-has torch, the engine and the web UI built in, and it shares the model cache
-and workspace with a native install on the same box. diffusers itself lives
-on a volume, so it can follow GitHub main. That makes step 2 a container:
+**2. Start the engine.** Leave it running; the agent and the browser both talk to it.
 
 ```bash
-bash docker/init-env.sh
-docker compose -f docker/compose.yaml up -d --build
-docker compose -f docker/compose.yaml logs -f    # wait for the server to start
-docker compose -f docker/compose.yaml exec dw python /opt/dw/entrypoint.py login-url   # the login link
+dw-serve        # web UI on http://127.0.0.1:8765
 ```
 
-Setup, data sharing and updates are in [docs/DOCKER.md](docs/DOCKER.md).
-
-**2. Start the engine.** Leave it running; everything else talks to it.
-
-```bash
-dw-serve
-# diffusers-workflow server on http://127.0.0.1:8765
-```
-
-That address is the web UI. Open it and run `templates/text-to-image` — a
-small, ungated model, so the first generation needs no Hugging Face login and
-downloads only a few GB.
-
-**3. Connect Claude Code.** Register the MCP server with the absolute path to
-`dw-mcp` in the venv you just made (the relative path is the one setup detail
-that reliably goes wrong):
+**3. Connect your agent.** Register the MCP server with the absolute path to `dw-mcp`
+in the venv you just made (a relative path is the setup detail that most often goes
+wrong). Then add the [dw plugin](plugins/dw/README.md):
 
 ```bash
 claude mcp add dw -- "$(pwd)/venv/bin/dw-mcp"
 ```
 
-Then, optionally, the [dw plugin](plugins/dw/README.md) — one skill per model
-family that knows which workflow fits a request and the rules that bite:
-
 ```
 /plugin marketplace add dkackman/diffusers-workflow
 /plugin install dw@diffusers-workflow
 ```
 
-Most of the shipped workflows (Flux, LTX-2, MiniMax...) use **gated** models.
-Request access on the model's Hugging Face page, then `hf auth login`
-once; without it the run fails partway through with a 401/403 from the Hub.
-
-> **GPU on another machine?** Start the engine there with `--mcp` and connect
-> over HTTP — nothing to install on the laptop:
->
-> ```bash
-> # on the GPU box
-> dw-serve --host 0.0.0.0 --token "$DW_API_TOKEN" --mcp --workspace ~/studio
->
-> # on your laptop
-> claude mcp add --transport http dw http://gpu-box:8765/mcp \
->   --header "Authorization: Bearer $DW_API_TOKEN"
-> ```
->
-> The server's own **Server** page composes that line for the address you
-> pick. End to end: [Remote GPU server](docs/REMOTE.md).
-
-## Drive it from an agent
-
-Then just ask. The agent has 65 tools covering the whole surface — the
-workflow catalog, the real diffusers pipeline signatures, the job queue, the
-gallery, the model cache:
+Most of the shipped models (Flux, LTX, MiniMax, ...) are **gated**. Request access on
+the model's Hugging Face page and run `hf auth login` once. Without it a run fails
+partway through with a 401/403 from the Hub. `templates/text-to-image` uses a small,
+ungated model, so it makes a good first run.
 
-![Claude Code driving the dw MCP server: creating a workspace, authoring a script, and generating from it](docs/img/claude-authoring.png)
+<details>
+<summary><b>GPU on another machine, or in Docker?</b></summary>
 
-Generation is the long pass, and the agent stays with it — queuing each shot,
-waiting it out, and reporting what came back:
-
-![The same session hours later: shots rendering one at a time, roughly 30 minutes each, with the agent reporting progress between them](docs/img/claude-generating.png)
-
-What a session looks like:
-
-- **"What can this box run, and what do I already have?"** — `get_server_info`
-  for the accelerator and workspace, `list_workflows` for the catalog with each
-  entry's shape, cost and variables, `list_models` for what is already in the
-  hub cache. The agent knows the device before it proposes anything CUDA-only.
-- **"Take my Flux workflow, swap in the portrait LoRA, render four at 1024."**
-  — `get_workflow`, `get_pipeline_signature` to check the arguments exist,
-  `validate_workflow` (free: schema *and* signature checking, no model loads),
-  `save_workflow`, `run_workflow`. That last one refuses until the agent passes
-  `acknowledged_cost=true`, so it has to tell you what it is about to spend.
-- **"How's it going?"** — `wait_for_job` blocks for a bounded interval instead
-  of polling; `get_output_image` brings the result back into the conversation
-  so the agent can look at what it made.
-- **"That third frame is the one — keep it and seed the video pass from it."**
-  — `keep_output` promotes the file into the asset library under a name you
-  pick, and the next workflow references `asset:hero-frame.png`.
-
-Everything that costs real GPU time or real disk (`run_workflow`, `rerun_job`,
-`enhance_prompt`, `download_model`, `delete_model`, `update_diffusers`,
-`delete_workspace`) refuses until it is explicitly acknowledged, so an agent
-cannot quietly burn an hour of GPU or delete 40GB of weights.
-
-One server holds several **workspaces** — each with its own workflows, assets
-and outputs — so two agents, or an agent and you in the browser, share the GPU
-without saving over each other. An agent calls `use_workspace` once and the
-rest of the session lands there.
-
-**Feedback from a session.** At the end of a working session, ask the agent
-what got in its way: bugs, gaps, misleading skill text, tools it reached for
-and couldn't find. Have it file each one as an issue on
-`dkackman/diffusers-workflow` with the `field-report` label, for example:
-
-> File each bug or gap you hit as an issue on dkackman/diffusers-workflow
-> with the label `field-report`.
-
-The label marks a report as coming from real use, not from the automated test
-loop. The agent loop (see [Agent Loop](docs/AGENT_LOOP.md)) picks the report up
-like any other issue when you filed it yourself; a report filed under any
-other GitHub login is parked for the maintainer to review first (relabelled
-`owner:don` + `status:needs-approval`), since the loop must not act
-unattended on third-party text in a public repo, and is only handed to the
-loop, or not, after that review. The label is also what feature planning
-reads as evidence of demand.
-
-The complete tool reference, client configuration for other MCP hosts, and the
-troubleshooting table: [MCP Server](docs/MCP.md). Workspaces in depth:
-[Workspaces](docs/WORKSPACES.md).
-
-## The web UI
-
-Everything the engine does, in a browser, backed by the same persistent GPU
-worker — models stay loaded between runs.
-
-**An editor built from the real pipeline signatures.** Forms and argument
-autocomplete are generated by introspecting diffusers itself, so every knob a
-pipeline exposes is there with its documentation. Validation catches schema
-errors *and* argument typos before any model loads.
-
-![The editor: introspection-driven forms beside live JSON in Monaco](docs/img/ui-editor.png)
-
-**A gallery where every image is a recipe.** Outputs carry their full workflow
-and seed; *open as workflow* drops any image back into the editor, ready to
-reproduce or riff on. *Keep as asset* promotes a generated file into the asset
-library for later workflows to build on.
-
-![The gallery with generated images and videos](docs/img/ui-gallery.jpg)
-
-**A prompt library** stores a prompt once and lets any workflow reference it,
-with an *Enhance with AI* panel that expands an idea into a full prompt using
-a local language model. **A model manager** inventories the Hugging Face hub
-cache — sizes, last use, free space — and downloads or deletes models with
-live progress.
-
-![The model manager listing cached models with sizes](docs/img/ui-models.png)
-
-Jobs queue, stream progress live per denoising step, cancel cooperatively and
-persist to a searchable history. See [Server & Web UI](docs/SERVER.md) for the
-pages and the HTTP API.
-
-## The command line
-
-`dw.run` is a thin client of `dw.serve`: it queues a job over HTTP, prints
-its progress, and reports the run directory the server wrote to - start the
-server first.
+Start the engine on the GPU box with `--mcp` and connect over HTTP. There is nothing
+to install on the laptop:
 
 ```bash
-python -m dw.serve
-python -m dw.run workflows/templates/text-to-image.json
-python -m dw.run workflows/templates/text-to-image.json prompt="a cat" num_images_per_prompt=4
-python -m dw.validate workflows/models/flux-dev.json
+# on the GPU box
+dw-serve --host 0.0.0.0 --token "$DW_API_TOKEN" --mcp --workspace ~/studio
+
+# on your laptop
+claude mcp add --transport http dw http://gpu-box:8765/mcp \
+  --header "Authorization: Bearer $DW_API_TOKEN"
 ```
 
-`dw.run` takes `WORKFLOW [name=value ...] [--server URL] [--workspace NAME]
-[--token TOKEN]` - `--server` defaults to `DW_MCP_URL`, else
-`http://127.0.0.1:8765`; `--token` to `DW_API_TOKEN`. Where the output lands,
-which workspace's `prompts:`/`asset:` references resolve, and the output
-layout are all `dw.serve` flags now (`--workspace`, `--output-dir`,
-`--prompt-dir`, `--asset-dir`, `--output-layout` on the server) - `dw.run`
-only says which server and which of its workspaces to run in.
-
-## What's underneath
-
-Every front end reads and writes the same thing: a JSON document of named
-steps, each a diffusers pipeline or a utility task, whose arguments reference
-variables, earlier steps' outputs, stored prompts and assets rather than
-hard-coded values. That is what makes text-to-image chain into image-to-video,
-and what makes a generated image reopen as the exact recipe that produced it.
-[workflows/](workflows/) is a corpus of runnable examples across model
-families; the [Workflow Guide](docs/WORKFLOW_GUIDE.md) is the reference when
-you do want to write one.
-
-Because a workflow reaches any diffusers pipeline or quantization backend by
-dynamic import, loading one can execute arbitrary Python. Treat a workflow
-file from someone else the way you'd treat a `.py` script — see
+The server's **Status** page writes that line for you. The whole setup is in
+[Remote GPU server](docs/REMOTE.md).
+
+On Linux with an NVIDIA GPU, the
+[`dkackman/diffusers-workflow`](https://hub.docker.com/r/dkackman/diffusers-workflow)
+image runs the server with torch, the engine and the web UI built in. It shares the
+model cache and workspace with a native install on the same box. See
+[Docker](docs/DOCKER.md).
+
+</details>
+
+## Ask the agent
+
+Describe what you want. The agent has 65 tools covering the workflow catalog, the
+real diffusers pipeline signatures, the job queue, the gallery and the model cache,
+and it stays with a long render until it finishes:
+
+![Claude Code driving the dw MCP server: creating a workspace, authoring a script, and generating from it](docs/img/claude-authoring.png)
+
+A session sounds like this:
+
+- **"Make a 30-second musical trailer: four men in boater hats, a giant eye, a
+  score under it."** The agent picks a shape from the catalog (`list_workflows`),
+  breaks the brief into shots, writes each prompt to the prompt library, and builds
+  the workflow that renders the shots and cuts them to the score.
+- **"What will that cost?"** `validate_workflow` checks the JSON against the schema
+  *and* the real pipeline signatures, with no model loads, and quotes an estimate in
+  GPU minutes from this box's own history. Anything that spends GPU time or disk
+  refuses to run until the agent passes `acknowledged_cost`, so it has to tell you
+  what it is about to spend first.
+- **"How's it going?"** `wait_for_job` waits out a render without polling. Then
+  `get_output_frames`, `get_output_image` and `get_output_audio` bring the result
+  back into the conversation, so the agent judges what it made against what you
+  asked for.
+- **"Shot three is the one. Keep it and start the next scene from it."**
+  `keep_output` turns the file into a named asset, and the next workflow references
+  it as `asset:hero-frame.png`.
+
+The [dw plugin](plugins/dw/README.md) adds what a general-purpose agent can't know
+on its own: one skill per model family (MiniMax H3 and Music 3, LTX-2.5, Kandinsky 6,
+FLUX 3 Klein) that picks the template for a request and states the rules that bite.
+Cross-cutting skills cover script-to-video, series with a consistent cast,
+storyboards and animatics, and a shared camera-and-look vocabulary.
+
+![The same session hours later: shots rendering one at a time, with the agent reporting progress between them](docs/img/claude-generating.png)
+
+One server holds several **workspaces**, each with its own workflows, assets and
+outputs. Two agents, or an agent and you in the browser, can share one GPU without
+saving over each other's work.
+
+The tool reference, setup for other MCP hosts and the troubleshooting table are in
+[MCP Server](docs/MCP.md).
+
+## Then tweak it in the browser
+
+The web UI shows everything the agent made and lets you take over at any point. It
+runs on the same persistent GPU worker, so models stay loaded between runs.
+
+**A workflow the agent wrote is a form you can edit.** The workflow's description,
+recent outputs and variables are on one page. Change a prompt, a frame count or a
+LoRA scale and press **Run**.
+
+![A workflow page: the agent's description of the trailer, its recent shots, and its variables ready to edit and run](docs/img/ui-workflow.jpg)
+
+**Every job is a recipe.** A job keeps the exact workflow it ran and draws it as a
+graph of how data flows between steps. Click a step to see what it produced. The
+run's warnings sit above the graph, so a problem the agent missed, such as an
+audible level jump at a cut, is in front of you with the argument that fixes it.
+**Run again** repeats the job, and **New seed** gives you another take. Progress
+streams live for each denoising step, and a cancel stops the job cleanly at the
+next step.
+
+![A finished cut-and-score job: a warning about a level jump at a cut, then the workflow as a data-flow graph from the edit and soundtrack through resampling, mixing and normalizing to the final film](docs/img/ui-job.png)
+
+**The gallery** groups outputs by the workflow that made them, with final and
+intermediate files kept apart. Any image reopens as the workflow that produced it.
+*Keep as asset* adds a file to the asset library for later workflows to use.
+
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="docs/img/ui-gallery-dark.jpg">
+  <img alt="The gallery: a poster workflow's four takes and the frames grabbed from the trailer's shots" src="docs/img/ui-gallery.jpg">
+</picture>
+
+**The editor works on the agent's JSON.** You can view the workflow as a form, as
+JSON, side by side, or as a flow graph. The forms and autocomplete come from the
+real diffusers pipeline signatures, so every argument a pipeline takes is there with
+its documentation. **Validate** catches schema errors and argument typos before any
+model loads.
+
+![The editor in split view: variables as a form beside the workflow JSON](docs/img/ui-editor.png)
+
+There is also a **prompt library**, with an *Enhance with AI* panel that expands an
+idea into a full prompt using a local language model. The **model manager** shows
+the Hugging Face cache (size, last use, free disk) and downloads or deletes models
+with live progress.
+
+![The model manager: every cached model with its size and when it was last used](docs/img/ui-models.png)
+
+The pages and the HTTP API behind them are in [Server & Web UI](docs/SERVER.md).
+
+## Underneath
+
+Every front end reads and writes the same thing: a JSON workflow of named steps,
+each a diffusers pipeline or a utility task. Its arguments are references to
+variables, earlier steps' results, stored prompts and assets, not hard-coded
+values. That is how text-to-image chains into image-to-video, and how a generated
+image reopens as the recipe that made it. You rarely need to write one by hand. When
+you do, the [Workflow Guide](docs/WORKFLOW_GUIDE.md) is the reference, and
+[workflows/](workflows/) has runnable examples for each model family. There is also
+a command-line client, `python -m dw.run workflow.json prompt="a cat"`, which queues
+a job on a running server (see [Server & Web UI](docs/SERVER.md#the-command-line)).
+
+The engine also covers:
+
+- **Quantization:** BitsAndBytes, TorchAO, GGUF, SDNQ and optimum-quanto, set per
+  component.
+- **Acceleration:** FirstBlockCache, FasterCache, MagCache, TaylorSeerCache,
+  `torch.compile` and attention backends.
+- **Adapters and prompts:** LoRA, IP-Adapter and A1111-style prompt weighting.
+- **Long video and caching:** long-video chaining with audio-driven length, and
+  step-output caching, so re-running a fixed-seed workflow finishes instantly.
+- **Utility tasks:** upscaling, face restoration, segmentation, captioning, frame
+  interpolation, audio slicing, cut-and-score assembly and more.
+
+A workflow can reach any diffusers pipeline or quantization backend by dynamic
+import, so loading one can execute arbitrary Python. Treat a workflow file from
+someone else the way you'd treat a `.py` script. See
 [Trust model](docs/SECURITY.md#trust-model).
 
-Under the hood the engine also handles: quantization (BitsAndBytes, TorchAO,
-GGUF, SDNQ, optimum-quanto); inference acceleration
-(FirstBlockCache, FasterCache, MagCache, TaylorSeerCache); LoRA and IP-Adapter;
-A1111-style prompt weighting; long-video chaining with audio-driven length;
-step-output caching, so re-running a fixed-seed workflow finishes instantly;
-and utility tasks for upscaling, face restoration, segmentation, captioning,
-frame interpolation and more.
+**Field reports.** At the end of a session, ask the agent what got in its way, and
+have it file each bug or gap as an issue on `dkackman/diffusers-workflow` with the
+`field-report` label. The label marks a report as coming from real use. A report
+from anyone but the maintainer is reviewed before the [agent loop](docs/AGENT_LOOP.md)
+acts on it.
 
 ## Documentation
 
-### Guides
-
-- [MCP Server](docs/MCP.md) — The agent tool surface (Claude Code, Claude Desktop)
-- [Server & Web UI](docs/SERVER.md) — The web UI, jobs API, and introspection service
-- [Remote GPU server](docs/REMOTE.md) — Using the server, UI and MCP from another machine
-- [Docker](docs/DOCKER.md) — The server in a container on a Linux NVIDIA box, sharing data with a native install
-- [Workspaces](docs/WORKSPACES.md) — Where your content lives, run directories, and several workspaces on one server
-- [Workflow Guide](docs/WORKFLOW_GUIDE.md) — JSON structure, variables, steps, data flow
-- [Quantization](docs/QUANTIZATION.md) — BitsAndBytes, TorchAO, GGUF, SDNQ
-- [Inference Acceleration](docs/ACCELERATION.md) — torch.compile, FirstBlockCache, MagCache, TaylorSeer
-- [Fast on 24GB](docs/RECIPES_24GB.md) — Recommended speed/memory configurations per model family
-- [LoRA](docs/LORAS.md) — Loading and stacking LoRA adapters
-- [IP-Adapter](docs/IP_ADAPTER.md) — Image-prompt conditioning
-- [Prompt Weighting](docs/PROMPT_WEIGHTING.md) — A1111-style syntax
-- [Prompt References](docs/WORKFLOW_GUIDE.md#prompt-references) — The stored prompt library and `prompt:` references
-- [Tasks](docs/TASKS.md) — Image processing, ControlNet preprocessors, utilities
-
-### Reference
-
-- [Worker Guide](docs/WORKER_GUIDE.md) — GPU persistence and troubleshooting
-- [Dependencies](docs/DEPENDENCIES.md) — Installation details
-- [Security](docs/SECURITY.md) — Security model
-- [Testing](docs/TESTING.md) — Running the test suite
-- [Releasing](docs/RELEASING.md) — Cutting a release from a version tag
-- [Agent Loop](docs/AGENT_LOOP.md) — What the automated implementer/tester ticket labels mean, for anyone joining an Issue
-- [Attributions](docs/ATTRIBUTIONS.md) — Code included from other projects, and the projects whose ideas dw reimplemented
+**Using it**
+
+- [MCP Server](docs/MCP.md) - the agent's tool surface (Claude Code, Claude Desktop, other MCP hosts)
+- [dw plugin](plugins/dw/README.md) - the model-family and composition skills
+- [Server & Web UI](docs/SERVER.md) - the web UI, the command line, the jobs and introspection APIs
+- [Workspaces](docs/WORKSPACES.md) - where your content lives, run directories, several workspaces on one server
+- [Remote GPU server](docs/REMOTE.md) - the server, UI and MCP from another machine
+- [Docker](docs/DOCKER.md) - the server in a container on a Linux NVIDIA box
+
+**Workflows in depth**
+
+- [Workflow Guide](docs/WORKFLOW_GUIDE.md) - JSON structure, variables, steps, data flow, references
+- [Tasks](docs/TASKS.md) - image, video and audio utility tasks, ControlNet preprocessors
+- [Quantization](docs/QUANTIZATION.md) - BitsAndBytes, TorchAO, GGUF, SDNQ
+- [Inference Acceleration](docs/ACCELERATION.md) - caches, `torch.compile`, attention backends, MPS
+- [Fast on 24GB](docs/RECIPES_24GB.md) - tested speed/memory settings per model family
+- [LoRA](docs/LORAS.md), [IP-Adapter](docs/IP_ADAPTER.md), [Prompt Weighting](docs/PROMPT_WEIGHTING.md)
+
+**Reference**
+
+- [Architecture](docs/ARCHITECTURE.md) - each concept, the module that owns it, and the check that enforces it
+- [Worker Guide](docs/WORKER_GUIDE.md) - the persistent GPU worker and troubleshooting
+- [Security](docs/SECURITY.md) - validators and the trust gate ([quick reference](docs/SECURITY_QUICKREF.md))
+- [Dependencies](docs/DEPENDENCIES.md), [Testing](docs/TESTING.md), [Releasing](docs/RELEASING.md)
+- [Agent Loop](docs/AGENT_LOOP.md) - what the automated implementer/tester labels mean
+- [Attributions](docs/ATTRIBUTIONS.md) - code from other projects, and the projects whose ideas dw reimplemented
diff --git a/docs/SERVER.md b/docs/SERVER.md
index 10d0bddc..038ab5ad 100644
--- a/docs/SERVER.md
+++ b/docs/SERVER.md
@@ -28,6 +28,29 @@ The server keeps a persistent GPU worker underneath: models stay
 loaded between runs, so re-running a workflow with a new prompt skips the
 load entirely.
 
+### The command line
+
+`dw.run` is a thin client of `dw.serve`: it queues a job over HTTP, prints
+its progress, and reports the run directory the server wrote to - start the
+server first.
+
+```bash
+python -m dw.run workflows/templates/text-to-image.json
+python -m dw.run workflows/templates/text-to-image.json prompt="a cat" num_images_per_prompt=4
+python -m dw.validate workflows/models/flux-dev.json
+```
+
+`dw.run` takes `WORKFLOW [name=value ...] [--server URL] [--workspace NAME]
+[--token TOKEN]` - `--server` defaults to `DW_MCP_URL`, else
+`http://127.0.0.1:8765`; `--token` to `DW_API_TOKEN`. Where the output lands,
+which workspace's `prompts:`/`asset:` references resolve, and the output
+layout are all `dw.serve` flags (`--workspace`, `--output-dir`,
+`--prompt-dir`, `--asset-dir`, `--output-layout` on the server) - `dw.run`
+only says which server and which of its workspaces to run in.
+
+`python -m dw.test` runs a small built-in workflow end to end - it downloads
+SD 1.5 (a few GB) and generates one image on whichever accelerator was found.
+
 ### Environment
 
 The server reads these from its environment (e.g. `.env`) and its workers inherit them; changing one needs a restart.
diff --git a/docs/img/ui-editor.png b/docs/img/ui-editor.png
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diff --git a/ui/scripts/readme-screenshots.mjs b/ui/scripts/readme-screenshots.mjs
new file mode 100644
index 00000000..57c7172b
--- /dev/null
+++ b/ui/scripts/readme-screenshots.mjs
@@ -0,0 +1,93 @@
+// Captures the README's web UI screenshots into docs/img/ from a running
+// server that has real work in it - the e2e fixture's one-pixel outputs make
+// a poor gallery. Read-only: it only navigates, it never queues a job.
+//
+//   DW_URL=http://gpu-box:8765 DW_API_TOKEN=... node scripts/readme-screenshots.mjs
+//
+// DW_SHOT_WORKSPACE, DW_SHOT_WORKFLOW and DW_SHOT_JOB pick what is shown;
+// DW_SHOT_KEEP is a comma list of the workspaces left in the sidebar (the
+// rest - test and probe workspaces - are hidden from the picture only).
+// Shots land as PNG in DW_SHOT_OUT (default: a temp dir) for review before
+// they are converted and copied into docs/img/.
+import { mkdtempSync } from 'node:fs'
+import { tmpdir } from 'node:os'
+import { join } from 'node:path'
+import { chromium } from '@playwright/test'
+
+const base = process.env.DW_URL ?? 'http://127.0.0.1:8765'
+const token = process.env.DW_API_TOKEN ?? ''
+const ws = process.env.DW_SHOT_WORKSPACE ?? 'shedding-skin-quartet'
+const workflow = process.env.DW_SHOT_WORKFLOW ?? 'tool-musical-trailer'
+// The job shot is there for its flow graph, so it defaults to a job whose
+// workflow has one worth showing, in a workspace of its own
+const jobWs = process.env.DW_SHOT_JOB_WORKSPACE ?? 'acorn-wars'
+const job = process.env.DW_SHOT_JOB ?? '8e31d14dd7b1'
+const keep = (process.env.DW_SHOT_KEEP ?? `default,${ws},${jobWs}`).split(',')
+const out = process.env.DW_SHOT_OUT ?? mkdtempSync(join(tmpdir(), 'dw-shots-'))
+
+// [file, hash route, color schemes, options]. `view` is the editor view to
+// click; `flow` scales the data-flow graph to its card instead of scrolling,
+// and crops the picture just below it.
+const shots = [
+  ['ui-overview', `ws/${ws}/overview`, ['light', 'dark']],
+  ['ui-workflow', `ws/${ws}/workflows/${workflow}`, ['light']],
+  ['ui-job', `ws/${jobWs}/jobs/${job}`, ['light'], { flow: true }],
+  ['ui-gallery', `ws/${ws}/gallery`, ['light', 'dark']],
+  ['ui-editor', `ws/${ws}/edit/${workflow}`, ['light'], { view: 'split' }],
+  ['ui-models', 'server/models', ['light']],
+]
+
+const browser = await chromium.launch()
+for (const [name, route, schemes, { view, flow } = {}] of shots) {
+  for (const colorScheme of schemes) {
+    const context = await browser.newContext({
+      viewport: { width: 1440, height: 900 },
+      deviceScaleFactor: 2,
+      colorScheme,
+    })
+    const page = await context.newPage()
+    const login = token ? `?token=${encodeURIComponent(token)}` : ''
+    await page.goto(`${base}/${login}#/${route}`)
+    await page.waitForLoadState('networkidle').catch(() => {})
+    await page.waitForTimeout(3000)
+    if (view) {
+      await page
+        .getByRole('button', { name: view, exact: true })
+        .first()
+        .click()
+      await page.waitForTimeout(2500)
+    }
+    await page.evaluate((keep) => {
+      for (const el of document.querySelectorAll('.wsgroup, .hiddentoggle'))
+        el.style.display = 'none'
+      for (const el of document.querySelectorAll('.ws.shut')) {
+        const label = el.querySelector('.name')?.textContent?.trim()
+        if (!keep.includes(label)) el.style.display = 'none'
+      }
+    }, keep)
+    let clip
+    if (flow) {
+      clip = await page.evaluate(() => {
+        const area = document.querySelector('.scrollarea')
+        const svg = area.querySelector('svg')
+        area.style.width = '100%'
+        svg.style.width = '100%'
+        svg.style.height = 'auto'
+        return {
+          x: 0,
+          y: 0,
+          width: 1440,
+          height: area.getBoundingClientRect().bottom + 24,
+        }
+      })
+    }
+    const file = join(
+      out,
+      `${name}${colorScheme === 'dark' ? '-dark' : ''}.png`,
+    )
+    await page.screenshot({ path: file, clip })
+    console.log(file)
+    await context.close()
+  }
+}
+await browser.close()

From bec9cbe6b6ed89da37d589090d4e410548c97dde Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:25:09 -0600
Subject: [PATCH 25/74] feat(tasks): #1165 - face_drift shot index, frames and
 times in measurements and findings

Each analyze_face_drift measurement and each finding's at now carries the
shot's index, start_frame, num_frames and start/end seconds, so two shots
with one name stay apart (C-F651's shape gap).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/TASKS.md            |  9 ++++---
 dw/tasks/face_drift.py   | 57 ++++++++++++++++++++++++++++++----------
 tests/test_face_drift.py | 47 ++++++++++++++++++++++++++++++---
 3 files changed, 93 insertions(+), 20 deletions(-)

diff --git a/docs/TASKS.md b/docs/TASKS.md
index e3de2605..75163ed8 100644
--- a/docs/TASKS.md
+++ b/docs/TASKS.md
@@ -2405,10 +2405,13 @@ and `opencv/face_detection_yunet`, which `validate_workflow` lists under
 | `threshold` | Median cosine similarity below which a shot raises `face_drift`, from -1 to 1. Default 0.363, OpenCV's published SFace threshold |
 | `samples_per_shot` | Frames read per shot, evenly spaced, 1 to 64. Default 8 |
 
-Each shot is a `measurements[]` entry: `shot`, `frames_sampled`,
-`faces_found` (sampled frames with a face), `similarity_median` and
+Each shot is a `measurements[]` entry: `shot`, `index` (its place in the
+shot list), `start_frame`, `num_frames`, `start` and `end` (seconds; null
+when the clip has no fps), `frames_sampled`, `faces_found` (sampled frames with a face), `similarity_median` and
 `similarity_min` - over those frames, the best cosine match among the faces
-in each frame. Both are null when no sampled frame had a face. Findings:
+in each frame. Both are null when no sampled frame had a face. Each
+finding's `at` carries the shot's `shot`, `index`, `start_frame`,
+`num_frames`, `start` and `end`, so two shots with one name stay apart. Findings:
 `face_drift` (warn) when the median is below `threshold`, and `no_face`
 (info) for a shot where no sampled frame had a face. Like the probes, they
 are places to look, not verdicts: a turned head or a mask also lowers the
diff --git a/dw/tasks/face_drift.py b/dw/tasks/face_drift.py
index 6d8f4252..887928b0 100644
--- a/dw/tasks/face_drift.py
+++ b/dw/tasks/face_drift.py
@@ -57,6 +57,9 @@
 DETECTOR_CONFIDENCE = 0.6
 PLACES = 3
 
+# What a finding's `at` carries of its shot's measurement
+AT_KEYS = ("shot", "index", "start_frame", "num_frames", "start", "end")
+
 RULES = {
     "face_drift": {
         "name": "face_drift",
@@ -157,10 +160,15 @@ def _reference_embedding(detect, recognizer, reference, notes):
     return faces[0][1]
 
 
+def _span(start, count, total):
+    """A shot's (first, last) frame numbers, clipped to the clip; last is exclusive."""
+    first = max(0, min(int(start), total))
+    return first, max(first, min(int(start) + int(count), total))
+
+
 def _sample_indexes(start, count, total, samples):
     """Up to `samples` evenly spaced frame numbers of a shot, inside the clip."""
-    first = max(0, min(int(start), total))
-    last = max(first, min(int(start) + int(count), total))
+    first, last = _span(start, count, total)
     span = last - first
     if span <= 0:
         return []
@@ -197,9 +205,26 @@ def _best_match(recognizer, embedding, faces):
     return max(float(recognizer.match(embedding, face, COSINE)) for _, face in faces)
 
 
-def _shot_measurement(name, matches, sampled, faces_found):
+def _seconds(frame, fps):
+    return round(frame / float(fps), PLACES) if fps else None
+
+
+def _where(index, name, first, last, fps):
+    """Where a shot sits in the clip: its frames and, given an fps, its times
+    in seconds - two shots with the same name stay apart by these (#1165)."""
     return {
         "shot": name,
+        "index": index,
+        "start_frame": first,
+        "num_frames": last - first,
+        "start": _seconds(first, fps),
+        "end": _seconds(last, fps),
+    }
+
+
+def _shot_measurement(where, matches, sampled, faces_found):
+    return {
+        **where,
         "frames_sampled": sampled,
         "faces_found": faces_found,
         "similarity_median": round(float(np.median(matches)), PLACES)
@@ -209,26 +234,27 @@ def _shot_measurement(name, matches, sampled, faces_found):
     }
 
 
-def _measure_shot(video, shot, media, models, embedding, samples):
+def _measure_shot(video, index, shot, media, models, embedding, samples):
     detect, recognizer = models
+    start = int(shot.get("start_frame", 0))
     count = shot.get("num_frames")
     if count is None:
-        count = media.frame_count - int(shot.get("start_frame", 0))
-    indexes = _sample_indexes(
-        shot.get("start_frame", 0), count, media.frame_count, samples
-    )
+        count = media.frame_count - start
+    first, last = _span(start, count, media.frame_count)
+    where = _where(index, shot.get("name"), first, last, media.fps)
+    indexes = _sample_indexes(start, count, media.frame_count, samples)
     matches = []
     for index in indexes:
         faces = _embeddings(detect, recognizer, _picture_at(video, index))
         if faces:
             matches.append(_best_match(recognizer, embedding, faces))
-    return _shot_measurement(shot.get("name"), matches, len(indexes), len(matches))
+    return _shot_measurement(where, matches, len(indexes), len(matches))
 
 
 def _findings(measurements, threshold):
     found = []
     for record in measurements:
-        at = {"shot": record["shot"]}
+        at = {key: record[key] for key in AT_KEYS}
         if record["faces_found"] == 0:
             found.append(finding(RULES["no_face"], 0, at))
         elif record["similarity_median"] < threshold:
@@ -271,8 +297,11 @@ def analyze_face_drift(
         device: Where the models run
 
     Returns:
-        {measurements: [{shot, frames_sampled, faces_found, similarity_median,
-        similarity_min}], findings, rules_applied, shots_source, notes}
+        {measurements: [{shot, index, start_frame, num_frames, start, end,
+        frames_sampled, faces_found, similarity_median, similarity_min}],
+        findings, rules_applied, shots_source, notes}. `start`/`end` are
+        seconds (None when the clip has no fps); each finding's `at` carries
+        the shot's name, index, frames and times
     """
     threshold, samples = _checked_settings(threshold, samples_per_shot)
     models = _models(device)
@@ -287,8 +316,8 @@ def analyze_face_drift(
             "no shot boundaries found; the whole file was measured as one shot"
         )
     measurements = [
-        _measure_shot(video, shot, media, models, embedding, samples)
-        for shot in records
+        _measure_shot(video, index, shot, media, models, embedding, samples)
+        for index, shot in enumerate(records)
     ]
     return {
         "measurements": measurements,
diff --git a/tests/test_face_drift.py b/tests/test_face_drift.py
index bc656e0f..273d0085 100644
--- a/tests/test_face_drift.py
+++ b/tests/test_face_drift.py
@@ -103,7 +103,24 @@ def test_other_face_drift_on_every_shot(probe):
         clip([a_face(PERSON_B)] * 6, shots_of(3, 3)), a_face(), samples_per_shot=3
     )
     drifted = [f for f in answer["findings"] if f["rule"] == "face_drift"]
-    assert [f["at"] for f in drifted] == [{"shot": "shot0"}, {"shot": "shot1"}]
+    assert [f["at"] for f in drifted] == [
+        {
+            "shot": "shot0",
+            "index": 0,
+            "start_frame": 0,
+            "num_frames": 3,
+            "start": 0.0,
+            "end": 0.125,
+        },
+        {
+            "shot": "shot1",
+            "index": 1,
+            "start_frame": 3,
+            "num_frames": 3,
+            "start": 0.125,
+            "end": 0.25,
+        },
+    ]
     assert drifted[0]["threshold"] == 0.363
     assert drifted[0]["value"] < 0.363
 
@@ -113,15 +130,39 @@ def test_empty_shot_reports_no_face_only_there(probe):
     answer = fd.analyze_face_drift(
         clip(frames, shots_of(3, 3)), a_face(), samples_per_shot=3
     )
-    assert [(f["rule"], f["at"]) for f in answer["findings"]] == [
-        ("no_face", {"shot": "shot0"})
+    assert [(f["rule"], f["at"]["shot"]) for f in answer["findings"]] == [
+        ("no_face", "shot0")
     ]
+    assert answer["findings"][0]["at"]["start_frame"] == 0
     first, second = answer["measurements"]
     assert first["faces_found"] == 0 and first["similarity_median"] is None
     assert first["similarity_min"] is None
     assert second["faces_found"] == 3
 
 
+def test_measurements_carry_each_shots_frames_and_times(probe):
+    # two shots share a name: their index, frames and times tell them apart
+    records = [shot_record("take", 0, 2), shot_record("take", 2, 4)]
+    answer = fd.analyze_face_drift(
+        clip([a_face(PERSON_B)] * 6, records), a_face(), samples_per_shot=2
+    )
+    first, second = answer["measurements"]
+    assert (first["index"], first["start_frame"], first["num_frames"]) == (0, 0, 2)
+    assert (first["start"], first["end"]) == (0.0, 0.083)
+    assert (second["index"], second["start_frame"], second["num_frames"]) == (1, 2, 4)
+    assert (second["start"], second["end"]) == (0.083, 0.25)
+    places = [(f["at"]["index"], f["at"]["start"]) for f in answer["findings"]]
+    assert places == [(0, 0.0), (1, 0.083)]
+
+
+def test_whole_file_shot_has_times(probe):
+    answer = fd.analyze_face_drift(clip([a_face()] * 48), a_face())
+    whole = answer["measurements"][0]
+    assert whole["shot"] == "whole"
+    assert (whole["start_frame"], whole["num_frames"]) == (0, 48)
+    assert (whole["start"], whole["end"]) == (0.0, 2.0)
+
+
 def test_reference_without_face_refused(probe):
     with pytest.raises(ValueError, match="reference"):
         fd.analyze_face_drift(clip([a_face()]), frame())

From 98acfffadb4cd37b58fcadc29335d62f82ee5ecf Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:29:25 -0600
Subject: [PATCH 26/74] docs: link the HTTP API's OpenAPI spec and the workflow
 schema

Neither was linked from anywhere a reader would look.

- docs/SERVER.md: "The HTTP API" section - /docs (Swagger UI), /redoc,
  /openapi.json on a running server, and the committed
  ui/src/lib/generated/openapi.json, which is generated and CI-checked
  and shows as plain JSON on GitHub
- docs/WORKFLOW_GUIDE.md: Structure links dw/workflow_schema.json,
  GET /api/schema and the Schema page
- README.md: links both from the doc list and the Underneath section

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md              |  9 ++++++---
 docs/SERVER.md         | 25 +++++++++++++++++++++++--
 docs/WORKFLOW_GUIDE.md |  7 ++++++-
 3 files changed, 35 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index bdff0347..51f1d8ff 100644
--- a/README.md
+++ b/README.md
@@ -165,7 +165,9 @@ with live progress.
 
 ![The model manager: every cached model with its size and when it was last used](docs/img/ui-models.png)
 
-The pages and the HTTP API behind them are in [Server & Web UI](docs/SERVER.md).
+The pages are described in [Server & Web UI](docs/SERVER.md). The UI and the agent
+both use the server's HTTP API, which you can browse at `/docs` on a running server
+(see [The HTTP API](docs/SERVER.md#the-http-api)).
 
 ## Underneath
 
@@ -174,7 +176,8 @@ each a diffusers pipeline or a utility task. Its arguments are references to
 variables, earlier steps' results, stored prompts and assets, not hard-coded
 values. That is how text-to-image chains into image-to-video, and how a generated
 image reopens as the recipe that made it. You rarely need to write one by hand. When
-you do, the [Workflow Guide](docs/WORKFLOW_GUIDE.md) is the reference, and
+you do, the [Workflow Guide](docs/WORKFLOW_GUIDE.md) is the reference, the
+[JSON Schema](dw/workflow_schema.json) is the exact structure, and
 [workflows/](workflows/) has runnable examples for each model family. There is also
 a command-line client, `python -m dw.run workflow.json prompt="a cat"`, which queues
 a job on a running server (see [Server & Web UI](docs/SERVER.md#the-command-line)).
@@ -208,7 +211,7 @@ acts on it.
 
 - [MCP Server](docs/MCP.md) - the agent's tool surface (Claude Code, Claude Desktop, other MCP hosts)
 - [dw plugin](plugins/dw/README.md) - the model-family and composition skills
-- [Server & Web UI](docs/SERVER.md) - the web UI, the command line, the jobs and introspection APIs
+- [Server & Web UI](docs/SERVER.md) - the web UI, the command line, and the [HTTP API](docs/SERVER.md#the-http-api) with its OpenAPI spec
 - [Workspaces](docs/WORKSPACES.md) - where your content lives, run directories, several workspaces on one server
 - [Remote GPU server](docs/REMOTE.md) - the server, UI and MCP from another machine
 - [Docker](docs/DOCKER.md) - the server in a container on a Linux NVIDIA box
diff --git a/docs/SERVER.md b/docs/SERVER.md
index 038ab5ad..b176c63b 100644
--- a/docs/SERVER.md
+++ b/docs/SERVER.md
@@ -21,8 +21,29 @@ python -m dw.serve --devices cuda:0,cuda:1   # one worker per card, two jobs at
 python -m dw.serve --trust-workflows      # only if nothing untrusted can reach POST /api/jobs - see Security model
 ```
 
-Installed as a package, the same server is `dw-serve`. Interactive API docs
-(OpenAPI) are at `/docs`.
+Installed as a package, the same server is `dw-serve`.
+
+### The HTTP API
+
+Everything the web UI and the MCP server do goes through one HTTP API, and a
+running server describes it:
+
+| Path | What it is |
+| ---- | ---------- |
+| `/docs` | Swagger UI: every route, its parameters and response, with *Try it out* |
+| `/redoc` | The same document in ReDoc, for reading |
+| `/openapi.json` | The OpenAPI document itself |
+
+The same document is committed as
+[`ui/src/lib/generated/openapi.json`](../ui/src/lib/generated/openapi.json)
+for client generation or reading without a server. It is generated, not
+written: `scripts/dump_openapi.py` writes it from the server, CI fails when
+the committed copy is stale, and its `info.version` is fixed at `"0"` so it
+is the same on every machine. GitHub shows it as plain JSON; to browse it,
+use `/docs` on a running server. Neither document declares the bearer token;
+see [Authentication](#authentication). The sections below cover the
+[Jobs API](#jobs-api), the [Introspection API](#introspection-api) and
+[Files and models](#files-and-models) in prose.
 
 The server keeps a persistent GPU worker underneath: models stay
 loaded between runs, so re-running a workflow with a new prompt skips the
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index 1822f462..d8fba20f 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -34,7 +34,12 @@ it is leaves every reader - and every agent - to guess from the filename.
 
 ## Structure
 
-Every workflow is a JSON file with an `id`, optional `variables`, and a list of `steps`:
+Every workflow is a JSON file with an `id`, optional `variables`, and a list of `steps`.
+The full structure is the JSON Schema in
+[dw/workflow_schema.json](../dw/workflow_schema.json), which is what `dw.validate`
+and the server check a workflow against. A running server also serves it at
+`GET /api/schema` and shows it as a browsable tree on its **Schema** page
+(see [Server & Web UI](SERVER.md)).
 
 ```json
 {

From 4542a142546e77b1eba57b23eb0bb6fcaebf83c7 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:32:42 -0600
Subject: [PATCH 27/74] fix(schema): workflow schema $id names this repo, not
 diffusers-helper

The $id still carried the project's old name and resolved nowhere. It now
is the raw URL of the schema on master, so it serves the document it names.
Nothing resolves $refs through it; GET /api/schema?section= copies it into
each fragment.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/workflow_schema.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dw/workflow_schema.json b/dw/workflow_schema.json
index c6a08fdc..1c4b6840 100644
--- a/dw/workflow_schema.json
+++ b/dw/workflow_schema.json
@@ -1,5 +1,5 @@
 {
-    "$id": "https://github.com/dkackman/diffusers-helper/workflow",
+    "$id": "https://raw.githubusercontent.com/dkackman/diffusers-workflow/master/dw/workflow_schema.json",
     "$schema": "https://json-schema.org/draft/2020-12/schema#",
     "description": "The definition of the diffusers-workflow.",
     "type": "object",

From 954ddbcdcca39fd563933fb15f588f02911ab391 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:47:17 -0600
Subject: [PATCH 28/74] docs: #1141 - record fix-forward #1165 in the
 cinematic-controls design record

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../complete/cinematic-controls-complete.md       | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/docs/proposals/complete/cinematic-controls-complete.md b/docs/proposals/complete/cinematic-controls-complete.md
index 42442fbc..082a350f 100644
--- a/docs/proposals/complete/cinematic-controls-complete.md
+++ b/docs/proposals/complete/cinematic-controls-complete.md
@@ -110,7 +110,10 @@ Kandinsky 6 and FLUX 3 Klein. It adds no new generator.
   CUDA. Both weights are at pinned revisions and listed in the plan's
   weights. SFace is `opencv/face_recognition_sface` @ `3d708243…`.
 - Findings come in the `assess.py` answer shape: `face_drift` (median
-  below threshold) and `no_face`.
+  below threshold) and `no_face`. Each finding's `at` and each
+  `measurements[]` entry carry `shot`, `index`, `start_frame`,
+  `num_frames`, `start` and `end` (seconds; null without fps), so shots
+  that share a name stay distinct (fix-forward #1165, `bec9cbe6`).
 - Validate refuses `threshold` outside [-1, 1] and `samples_per_shot`
   outside 1–64.
 - `workflows/templates/face-drift-check.json` is the template. The
@@ -155,6 +158,15 @@ skill defines that marking for a future trial.
   conflict. He ruled to amend the case, not the engine: C-F647 accepts
   `unknown` with the task-only reason, or `observed` with `minutes` under
   0.1 (case change dkackman/harnest#174). No code changed.
+- **E's same-person fixture.** The final check flagged `face_drift` on
+  `ep6-shot1-priya.mp4` against `priya-portrait.jpg` (median 0.352 under
+  0.363). Fix-forward #1165 checked the measurement first; Don ruled the
+  code right: that clip is not the portrait's person. The threshold stays
+  0.363. C-F651 arm 1 was re-pointed at a fresh H3 render conditioned on
+  the portrait (`asset:qa-cast/h3-priya-portrait-i2v.mp4`, median 0.483,
+  no findings; case change dkackman/harnest#177). The same check found
+  findings named only the shot, so #1165 added per-shot index, frames and
+  times to them.
 - **C's caps.** Beyond the plan's 600 s cap, C adds a 36000-frame cap and a
   4 GiB run-time byte budget. So 600 s at 120 fps is refused.
 - **C's silence.** The plan said "an AudioVideo that is silent". After the
@@ -180,6 +192,7 @@ skill defines that marking for a future trial.
 | F #1147 | 0 | |
 | G #1148 | 0 | |
 | Final check | 1 | C-F647: the `animatic` estimate was `observed`, not `unknown`. A record conflict, not a build miss: fix-forward #1164 carried Don's ruling to amend the case; re-run passed on develop @ 06f08f37. |
+| Final check | 2 | C-F651 arm 1: `face_drift` on the same person. The fixture's clip was not the portrait's person (Don's ruling, threshold unchanged); fix-forward #1165 re-pointed the case's fixture and added per-shot times to findings (`bec9cbe6`, merged `eb8d1424`); all three arms passed on lem @ eb8d1424. |
 
 Cost: no stage comment recorded `usage:` figures, so none is given here.
 G used the approved ~1 h of lem GPU (18 clips) plus the K6 download above.

From 7d1e1213852ceffdb9e41a48ad8e1d7cc7e741c9 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 06:57:31 -0600
Subject: [PATCH 29/74] docs: #1167 - chains.md drift guidance from community
 reports

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 .../dw/skills/minimax-h3/references/chains.md | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/plugins/dw/skills/minimax-h3/references/chains.md b/plugins/dw/skills/minimax-h3/references/chains.md
index ceed2090..bdd859cd 100644
--- a/plugins/dw/skills/minimax-h3/references/chains.md
+++ b/plugins/dw/skills/minimax-h3/references/chains.md
@@ -20,3 +20,28 @@ previous segment's last `guide_frames` and its voice as a guide.
 
 Drift compounds per seam. Reference the subject picture in each segment,
 prefer `last_segment`, and use the longest segments memory allows.
+
+### Community reports on drift
+
+These are reports from other users (roycho87/NerdNotes,
+`20261102/h3_latents_and_degradation.md`, collecting Banodoco and
+r/StableDiffusion threads), paraphrased. They are not dw measurements.
+
+- **Static and half-static shots are reported to drift worst.** On a moving
+  shot the background keeps being replaced. On a locked-off shot the drift is
+  mostly darkening and colour rather than texture.
+- **A hard cut or a new angle is reported to reset drift.** People describe
+  about 30 s of clean chain with good setups, with drift usually showing around
+  segments 3-6. Where the story allows, cut or change angle roughly that often;
+  `references/cuts.md` has the cut itself.
+- **Speed LoRAs are reported to add crunch over a long chain.** See the speed
+  tradeoff in `references/quality-and-speed.md`; the 4-step default is
+  unchanged.
+- **Measuring drift:** compare a patch the subject never enters against
+  segment 1 at the same frames. Whole-frame averages let changes cancel out.
+  `compare_outputs(texture_bands=true)` does the texture side (see
+  `references/cuts.md`), and `assess_output(probe="analyze_seams")` reports a
+  `jump_ratio` per seam.
+- **Colour matching** is reported to fix colour but not texture, and matching
+  to a frame with different content makes it worse. Match on a masked area or
+  a similar frame. #1033 tracks an engine version of this; it is not promised.

From 3de1506ca58574cd94c9619a9a30fa9f500bd9ca Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:03:13 -0600
Subject: [PATCH 30/74] fix(flux3): #1168 - load only Klein's four components,
 not the repo's 18.8 GB prompt upsampler

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 tests/test_flux3_catalog.py                       | 15 +++++++++++++++
 workflows/models/flux3-klein.json                 |  6 ++++++
 workflows/templates/flux3/image-edit.json         |  6 ++++++
 workflows/templates/flux3/layout-boxes.json       |  6 ++++++
 workflows/templates/flux3/multi-reference.json    |  6 ++++++
 workflows/templates/flux3/text-to-image.json      |  8 +++++++-
 .../templates/flux3/upsample-then-generate.json   |  6 ++++++
 7 files changed, 52 insertions(+), 1 deletion(-)

diff --git a/tests/test_flux3_catalog.py b/tests/test_flux3_catalog.py
index 53898c90..36ffe9ca 100644
--- a/tests/test_flux3_catalog.py
+++ b/tests/test_flux3_catalog.py
@@ -186,3 +186,18 @@ def test_the_layout_caption_carries_tags_and_a_0_to_1000_box_table():
         top, left, bottom, right = row["bbox"]
         assert 0 <= top < bottom <= 1000 and 0 <= left < right <= 1000
     assert klein_steps(load(os.path.join(TEMPLATES, "layout-boxes.json")))
+
+
+@pytest.mark.parametrize("path", flux3_definitions())
+def test_every_klein_step_skips_the_repo_s_prompt_upsampler(path):
+    # The repo's modular index also names an 18.8 GB prompt_upsampler (from the -pup
+    # repo); load_components() with no names pulls it too, and with Klein's ~19 GB
+    # that overflows a 24 GB card while loading (#1168)
+    for step in klein_steps(load(path)):
+        load_arguments = step["pipeline"]["configuration"]["load_components"]
+        assert load_arguments["names"] == [
+            "text_encoder",
+            "tokenizer",
+            "transformer",
+            "vae",
+        ], path
diff --git a/workflows/models/flux3-klein.json b/workflows/models/flux3-klein.json
index 1bf5457c..07bca7af 100644
--- a/workflows/models/flux3-klein.json
+++ b/workflows/models/flux3-klein.json
@@ -22,6 +22,12 @@
                 "configuration": {
                     "component_type": "ModularPipeline",
                     "load_components": {
+                        "names": [
+                            "text_encoder",
+                            "tokenizer",
+                            "transformer",
+                            "vae"
+                        ],
                         "dtype": "torch.bfloat16"
                     },
                     "components": {
diff --git a/workflows/templates/flux3/image-edit.json b/workflows/templates/flux3/image-edit.json
index c94480dd..26409281 100644
--- a/workflows/templates/flux3/image-edit.json
+++ b/workflows/templates/flux3/image-edit.json
@@ -37,6 +37,12 @@
                 "configuration": {
                     "component_type": "ModularPipeline",
                     "load_components": {
+                        "names": [
+                            "text_encoder",
+                            "tokenizer",
+                            "transformer",
+                            "vae"
+                        ],
                         "dtype": "torch.bfloat16"
                     },
                     "components": {
diff --git a/workflows/templates/flux3/layout-boxes.json b/workflows/templates/flux3/layout-boxes.json
index 3a0ec424..982ae9ed 100644
--- a/workflows/templates/flux3/layout-boxes.json
+++ b/workflows/templates/flux3/layout-boxes.json
@@ -32,6 +32,12 @@
                 "configuration": {
                     "component_type": "ModularPipeline",
                     "load_components": {
+                        "names": [
+                            "text_encoder",
+                            "tokenizer",
+                            "transformer",
+                            "vae"
+                        ],
                         "dtype": "torch.bfloat16"
                     },
                     "components": {
diff --git a/workflows/templates/flux3/multi-reference.json b/workflows/templates/flux3/multi-reference.json
index 76e86780..358cfb13 100644
--- a/workflows/templates/flux3/multi-reference.json
+++ b/workflows/templates/flux3/multi-reference.json
@@ -40,6 +40,12 @@
                 "configuration": {
                     "component_type": "ModularPipeline",
                     "load_components": {
+                        "names": [
+                            "text_encoder",
+                            "tokenizer",
+                            "transformer",
+                            "vae"
+                        ],
                         "dtype": "torch.bfloat16"
                     },
                     "components": {
diff --git a/workflows/templates/flux3/text-to-image.json b/workflows/templates/flux3/text-to-image.json
index 2498f8c4..5049ff5f 100644
--- a/workflows/templates/flux3/text-to-image.json
+++ b/workflows/templates/flux3/text-to-image.json
@@ -1,6 +1,6 @@
 {
     "id": "Flux3KleinT2I",
-    "description": "FLUX 3 Image Klein (distilled, 5.6B) text to image, the baseline of the FLUX 3 templates. The prompt here is already a full caption - BFL's own upsampled caption for their man walking a dog example - because Klein was trained on long upsampler captions and turns short prompts into generic pictures; upsample-then-generate shows how to get one from a short request. Sixteen steps with no guidance: the distilled model is a flow map, so it takes no guidance scale and no negative prompt. Width and height must be multiples of 16, which the workflow declares; BFL states sides of 64 to 4096, which is not checked. Every component is placed on the GPU explicitly, about 19 GB of bf16 weights, which fits a 24 GB card at 1024x1024 with under 2 GB to spare. The weights are gated under the FLUX Kommunity License: accept it on the model page and log in with hf auth login first.",
+    "description": "FLUX 3 Image Klein (distilled, 5.6B) text to image, the baseline of the FLUX 3 templates. The prompt here is already a full caption - BFL's own upsampled caption for their man walking a dog example - because Klein was trained on long upsampler captions and turns short prompts into generic pictures; upsample-then-generate shows how to get one from a short request. Sixteen steps with no guidance: the distilled model is a flow map, so it takes no guidance scale and no negative prompt. Width and height must be multiples of 16, which the workflow declares; BFL states sides of 64 to 4096, which is not checked. Only the four components the pipeline runs are loaded (the repo index also names an 18.8 GB prompt upsampler, which loading everything would put on the card too and overflow 24 GB). Every component is placed on the GPU explicitly, about 19 GB of bf16 weights, which fits a 24 GB card at 1024x1024 with under 2 GB to spare. The weights are gated under the FLUX Kommunity License: accept it on the model page and log in with hf auth login first.",
     "cost_drivers": [
         "width",
         "height"
@@ -31,6 +31,12 @@
                 "configuration": {
                     "component_type": "ModularPipeline",
                     "load_components": {
+                        "names": [
+                            "text_encoder",
+                            "tokenizer",
+                            "transformer",
+                            "vae"
+                        ],
                         "dtype": "torch.bfloat16"
                     },
                     "components": {
diff --git a/workflows/templates/flux3/upsample-then-generate.json b/workflows/templates/flux3/upsample-then-generate.json
index f9d8e5e8..aecb3768 100644
--- a/workflows/templates/flux3/upsample-then-generate.json
+++ b/workflows/templates/flux3/upsample-then-generate.json
@@ -46,6 +46,12 @@
                 "configuration": {
                     "component_type": "ModularPipeline",
                     "load_components": {
+                        "names": [
+                            "text_encoder",
+                            "tokenizer",
+                            "transformer",
+                            "vae"
+                        ],
                         "dtype": "torch.bfloat16"
                     },
                     "components": {

From 342bc465adc93d46826aa8e2bcc47331635cfe88 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:06:51 -0600
Subject: [PATCH 31/74] test: #1168 - re-pin the Klein templates' step cache
 keys after names

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 tests/test_step_cache.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tests/test_step_cache.py b/tests/test_step_cache.py
index a62d34aa..1a3b9150 100644
--- a/tests/test_step_cache.py
+++ b/tests/test_step_cache.py
@@ -668,19 +668,19 @@ def test_normalized_downstream_false_when_nothing_references_it():
     "expand-prompt.json": {},
     "face-drift-check.json": {},
     "flux3/image-edit.json": {
-        "edit": "58be5755e37a15b2780cc4fa3eead66e91e2eeec21e15f15624f60a9a072f235"
+        "edit": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
     },
     "flux3/layout-boxes.json": {
-        "layout_to_image": "58be5755e37a15b2780cc4fa3eead66e91e2eeec21e15f15624f60a9a072f235"
+        "layout_to_image": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
     },
     "flux3/multi-reference.json": {
-        "edit": "58be5755e37a15b2780cc4fa3eead66e91e2eeec21e15f15624f60a9a072f235"
+        "edit": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
     },
     "flux3/text-to-image.json": {
-        "text_to_image": "58be5755e37a15b2780cc4fa3eead66e91e2eeec21e15f15624f60a9a072f235"
+        "text_to_image": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
     },
     "flux3/upsample-then-generate.json": {
-        "text_to_image": "58be5755e37a15b2780cc4fa3eead66e91e2eeec21e15f15624f60a9a072f235"
+        "text_to_image": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
     },
     "generate-speech.json": {},
     "image-edit.json": {

From bdad13476163d6ba1b7388638b021d5ea04fcd8a Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:06:41 -0600
Subject: [PATCH 32/74] docs: accuracy and concision pass over every guide;
 page screenshots

Every doc checked against the code it describes, repetition and history
cut, headings agents and skills name kept. 24 files, ~950 lines shorter.

Accuracy, the larger fixes:
- TASKS.md: six registered tasks had no entry (loop_frames, frame_grid,
  select, judge, upscale_h3_latents, decode_h3_latents); ~50 wrong
  arguments, defaults and refusals; invalid JSON examples; the ControlNet
  detector arguments sit beside `image`, not inside it
- MCP.md: every signature matched to the registered tools (a dozen were
  missing parameters); the security section claimed no auth
- SECURITY.md: dw/ does run subprocesses (pip, sysctl, nvidia-smi); name
  validators tabled; a new HTTP section says /outputs, /inputs and
  /exports are readable without the token (#592)
- SERVER/WORKSPACES/REMOTE/DOCKER/WORKER_GUIDE: undescribed pages and
  routes, the 409 upload refusal, the 200-event cap is history-only,
  DW_ASSET_DIR is overwritten by dw.serve, no commit pin on Models
- WORKFLOW_GUIDE.md: undeclared variable: refs, closed objects, content
  types, library lookup order; /outputs needs no token
- ACCELERATION/RECIPES/QUANTIZATION/LORAS/IP_ADAPTER: kernels is a
  dependency, MagCache/FasterCache defaults, H3 and LTX placements;
  measured numbers left as measured
- AGENT_LOOP.md: every label in use; DEPENDENCIES, TESTING, RELEASING
  process (now at the top under "Cutting a release"), ARCHITECTURE counts

Screenshots: each page in SERVER.md's "The pages", the Caches card in
place of the pre-sidebar ui-server-dark.png, and REMOTE.md's "Connect from
another machine" card.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/ACCELERATION.md           |  278 ++---
 docs/AGENT_LOOP.md             |  174 +--
 docs/ARCHITECTURE.md           |    6 +-
 docs/ATTRIBUTIONS.md           |    4 +-
 docs/DEPENDENCIES.md           |   60 +-
 docs/DOCKER.md                 |   47 +-
 docs/IP_ADAPTER.md             |   40 +-
 docs/LORAS.md                  |   42 +-
 docs/MCP.md                    |  695 +++++------
 docs/PROMPT_WEIGHTING.md       |   31 +-
 docs/QUANTIZATION.md           |   73 +-
 docs/RECIPES_24GB.md           |   68 +-
 docs/RELEASING.md              |  151 +--
 docs/REMOTE.md                 |  151 ++-
 docs/SECURITY.md               |  658 +++++-----
 docs/SECURITY_QUICKREF.md      |  118 +-
 docs/SERVER.md                 |  725 +++++------
 docs/TASKS.md                  | 2058 +++++++++++++++----------------
 docs/TESTING.md                |   57 +-
 docs/WORKER_GUIDE.md           |   91 +-
 docs/WORKFLOW_GUIDE.md         | 2077 +++++++++++---------------------
 docs/WORKSPACES.md             |  143 +--
 docs/img/ui-caches-dark.png    |  Bin 0 -> 98770 bytes
 docs/img/ui-caches.png         |  Bin 0 -> 100032 bytes
 docs/img/ui-compare.jpg        |  Bin 0 -> 471416 bytes
 docs/img/ui-connect-dark.png   |  Bin 0 -> 82223 bytes
 docs/img/ui-connect.png        |  Bin 0 -> 82221 bytes
 docs/img/ui-examples.jpg       |  Bin 0 -> 453619 bytes
 docs/img/ui-gallery-detail.jpg |  Bin 0 -> 347550 bytes
 docs/img/ui-prompt-editor.png  |  Bin 0 -> 452624 bytes
 docs/img/ui-server-dark.png    |  Bin 117558 -> 0 bytes
 docs/img/ui-status-dark.png    |  Bin 0 -> 187867 bytes
 plugins/dw/README.md           |   26 +-
 ui/README.md                   |   19 +-
 34 files changed, 3424 insertions(+), 4368 deletions(-)
 create mode 100644 docs/img/ui-caches-dark.png
 create mode 100644 docs/img/ui-caches.png
 create mode 100644 docs/img/ui-compare.jpg
 create mode 100644 docs/img/ui-connect-dark.png
 create mode 100644 docs/img/ui-connect.png
 create mode 100644 docs/img/ui-examples.jpg
 create mode 100644 docs/img/ui-gallery-detail.jpg
 create mode 100644 docs/img/ui-prompt-editor.png
 delete mode 100644 docs/img/ui-server-dark.png
 create mode 100644 docs/img/ui-status-dark.png

diff --git a/docs/ACCELERATION.md b/docs/ACCELERATION.md
index e1db7444..437ffd86 100644
--- a/docs/ACCELERATION.md
+++ b/docs/ACCELERATION.md
@@ -1,49 +1,28 @@
 # Inference Acceleration
 
-Speed up generation by caching intermediate computations and skipping redundant transformer steps. Diffusers' built-in caching (`first_block`, `mag` and the others below) provides it. Beyond caching, `torch.compile`, attention backend selection, layerwise casting, and device-level settings (TF32, cuDNN) also affect throughput - see below. Memory offloading trades speed for VRAM and is covered in depth in [WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#memory-offloading).
-
-For ready-made configurations that combine these levers per model family, see [RECIPES_24GB.md](RECIPES_24GB.md).
+The levers that make a generation faster: diffusers' step caches, attention backends, `torch.compile`, layerwise casting, device settings (TF32, cuDNN) and offloading. Offloading trades speed for VRAM; its syntax is in [WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#memory-offloading). Tested combinations per model family are in [RECIPES_24GB.md](RECIPES_24GB.md); quantization is in [QUANTIZATION.md](QUANTIZATION.md).
 
 ## Diffusers Built-in Cache
 
-Applied at pipeline load time via the `cache` configuration. Hooks auto-reset between runs.
+A `cache` block in the pipeline `configuration` attaches one of diffusers' cache hooks to the pipeline's `transformer` at load time. The hooks reset between runs. `cache` may also be a whole-object `variable:` reference; one that resolves to `null` means no cache ([WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#cache-acceleration)). Every MiniMax-H3 template takes a `cache` argument, `null` by default and wired to the H3 step only.
 
-`validate_workflow` warns (it never refuses) about two cache settings that cost a render to find out:
+`validate_workflow` warns, and never refuses, about cache settings that cannot pay off (`dw/acceleration_warnings.py`):
 
-- **`cache_short_schedule`: a threshold cache on a short schedule.** `first_block` and `mag` decide each step's skip on a residual threshold, and the consecutive steps of a short schedule differ too much for it: under 20 `num_inference_steps` such a cache will likely skip nothing and still perturbs the result. On MiniMax-H3 a `first_block` cache at `0.05` skipped nothing at 12 steps and took 22% off at 30 ([FirstBlockCache](#firstblockcache)). Drop the cache or run 20 steps or more. A turbo schedule (9 steps or fewer) never benefits. `taylorseer` and `faster` skip on a fixed interval and `text_kv` skips no step, so the rule does not apply to them.
-- **`taylorseer_state_unestimated`: TaylorSeer's state is not in the VRAM estimate.** Said whenever `taylorseer` is set - see [TaylorSeerCache](#taylorseercache).
+- **`cache_short_schedule`**: a `first_block` or `mag` cache under 20 `num_inference_steps`. Both decide each step's skip on a residual threshold, and the consecutive steps of a short schedule differ too much for it, so the cache likely skips nothing and still perturbs the result. On MiniMax-H3 a `first_block` cache at `0.05` skipped nothing at 12 steps or on a turbo schedule, and took 22% off at 30. Drop the cache or run 20 steps or more. `taylorseer` and `faster` skip on a fixed interval and `text_kv` skips no step, so the rule does not apply to them.
+- **`cache_unsupported`**: the pipeline class has no `transformer` (a UNet pipeline such as SD 1.5), or its transformer class has no `enable_cache()` (Z-Image's). The step runs uncached and the job warns that the cache was not applied.
+- **`taylorseer_state_unestimated`**: said whenever `taylorseer` is set - see [TaylorSeerCache](#taylorseercache).
+- **`compile_fullgraph_with_cache`**: see [torch.compile](#torchcompile).
 
 ### Models diffusers has not registered
 
-`first_block`, `mag` and `layer_skip` look a model's transformer block class up in
-diffusers' own registry and raise when it is absent, which is how a model that supports
-`enable_cache()` ends up with no usable cache. [cache_blocks.json](../dw/cache_blocks.json)
-fills those gaps in, registering the missing block metadata on demand; entries become
-redundant, not wrong, once diffusers registers the same class upstream. MiniMax-H3,
-LTX-2 and Qwen-Image-2.1 are listed there today.
-
-Qwen-Image-2.1's pipeline keeps a key/value cache of its own (`use_kv_cache`, on by
-default) that drops the text tokens from the sequence after the first step. `first_block`
-and `mag` compare one step's residual against the previous one, so the sequence length
-changing under them fails the second step; pass `"use_kv_cache": false` in the step's
-`arguments` to cache it.
-
-A transformer that does not support `enable_cache()` at all - Z-Image's, for one - takes
-no cache of any type. The step still runs, uncached, and the job carries a warning that the
-cache was not applied.
-
-LTX-2's block needs one thing more than the metadata diffusers defines. It returns two
-streams - video and audio - and diffusers reads the second one back out of a forward
-argument named literally `encoder_hidden_states`, the only two-stream shape it registers
-upstream (text beside image). LTX-2's blocks take an `encoder_hidden_states` of their
-own, the text conditioning, so the fixed name reads the wrong tensor and feeds the text
-embeddings back as the audio stream on every skipped block. The entry names the argument
-its second stream actually comes from (`encoder_hidden_states_argument_name`), which is
-what makes caching correct there rather than merely quiet.
+`first_block` and `mag` look the transformer's block class up in diffusers' block registry and raise when it is absent, so a model that supports `enable_cache()` can still have no usable cache. [cache_blocks.json](../dw/cache_blocks.json) registers the missing block metadata on demand (`dw/cache_blocks.py`); an entry becomes redundant, not wrong, once diffusers registers the class itself. It lists MiniMax-H3, LTX-2 and Qwen-Image-2.1.
+
+- **LTX-2**: its blocks return video and audio streams, and diffusers reads the second stream back from a forward argument named `encoder_hidden_states`. LTX-2's own `encoder_hidden_states` is the text conditioning, so a skipped block would feed the text embeddings back as audio. The entry's `encoder_hidden_states_argument_name` names the argument the second stream actually comes from.
+- **Qwen-Image-2.1**: the pipeline's own key/value cache (`use_kv_cache`, on by default) drops the text tokens from the sequence after the first step, and the changed sequence length fails `first_block` and `mag` on the second step. Pass `"use_kv_cache": false` in the step's `arguments` to cache it.
 
 ### FirstBlockCache
 
-Simplest and broadest support. Compares first-block residuals to decide whether to skip remaining blocks.
+Compares the first block's residual between steps to decide whether to skip the remaining blocks. The broadest support.
 
 ```json
 "configuration": {
@@ -55,13 +34,13 @@ Simplest and broadest support. Compares first-block residuals to decide whether
 }
 ```
 
-Higher threshold = more speedup, more quality loss. Start with `0.05` and increase to taste. On MiniMax-H3 (RTX 3090, 1344x768, 4 references) `0.05` took 22% off a 30-step base-model job and nothing off a 12-step or turbo one, which it never skips; `torch.compile` there cost 289 s on the first pass for ~1% per pass, `text_kv` did nothing on Ref2VA, and `taylorseer` ran out of memory. Every MiniMax-H3 template takes a `cache` argument, `null` (off) by default and wired to the H3 step only: pass `{"type": "first_block", "threshold": 0.05}` rather than editing the JSON, and only at ~20 or more base steps, never with the turbo LoRAs.
+`threshold` defaults to `0.05`. Higher skips more and costs more quality. On MiniMax-H3 (RTX 3090, 1344x768, 4 references) `0.05` took 22% off a 30-step base-model job and nothing off a 12-step or turbo one. On H3 `torch.compile` cost 289 s on the first pass for about 1% per pass, `text_kv` did nothing on Ref2VA, and `taylorseer` ran out of memory. On an H3 template pass `cache={"type": "first_block", "threshold": 0.05}`, at 20 or more base steps and never with a turbo LoRA.
 
 **Example:** [step-caching.json](../workflows/templates/step-caching.json)
 
 ### MagCache
 
-Magnitude-based caching with error accumulation. Requires `num_inference_steps` to match the pipeline arguments, and `mag_ratios` — the per-step magnitude ratios, which are checkpoint-dependent:
+Magnitude-based skipping with error accumulation:
 
 ```json
 "cache": {
@@ -76,29 +55,33 @@ Magnitude-based caching with error accumulation. Requires `num_inference_steps`
 
 | Property | Default | Description |
 | -------- | ------- | ----------- |
-| `mag_ratios` | required | Preset name or explicit per-step ratio array — see below |
+| `mag_ratios` | required unless `calibrate` | Preset name or per-step ratio array - see below |
 | `threshold` | 0.06 | Accumulated error threshold for skipping |
-| `num_inference_steps` | required | The transformer forwards the call runs: the pipeline's `num_inference_steps`, except where the scheduler runs fewer (MiniMax-H3 runs one fewer) |
+| `num_inference_steps` | 28 (diffusers) | The transformer forwards the call runs: the pipeline's `num_inference_steps`, except where the scheduler runs fewer (MiniMax-H3 runs one fewer). Set it to match the call |
 | `max_skip_steps` | 3 | Max consecutive steps to skip |
 | `retention_ratio` | 0.2 | Fraction of initial steps where skipping is disabled |
-| `calibrate` | false | Measure ratios for a new model instead of skipping — see below |
+| `calibrate` | false | Measure ratios for a new model instead of skipping - see below |
 
 #### Supplying `mag_ratios`
 
-MagCache needs to know how each denoising step's output magnitude typically behaves for *your* checkpoint, so unlike the other cache types it cannot run on defaults alone. Give it either:
+The ratios describe how each step's output magnitude behaves for one checkpoint, so MagCache cannot run on defaults alone. Give either:
 
-- **A preset name** — `"mag_ratios": "flux"` resolves to the ratios diffusers ships for Flux. Any preset a later diffusers release adds is usable by name without a change here.
-- **An explicit array** — `"mag_ratios": [1.0, 0.98, 0.96, ...]`. The array is interpolated automatically when its length differs from `num_inference_steps`, so ratios measured at one step count can be reused at another.
+- **A preset name**: `"mag_ratios": "flux"` resolves to diffusers' `FLUX_MAG_RATIOS`. A preset a later diffusers adds (`<NAME>_MAG_RATIOS`) works by name with no change here; an unknown name fails with the list of presets.
+- **An array**: `"mag_ratios": [1.0, 0.98, 0.96, ...]`. An array whose length differs from `num_inference_steps` is interpolated.
 
-For a model with no preset, run once with `"calibrate": true`. Calibration skips nothing. At the end of the run the measured ratios appear as a job warning and a `log` event (`get_job`'s `warnings`, `get_job_events`, `export_job`): `MagCache calibration (N steps): [ ... ] — paste into mag_ratios`. While calibrating, the run's own `num_inference_steps` is the step count, so the cache's can be left at its default (a mismatch is reported as a warning). The cache attaches to a pipeline's `transformer`, and only one that supports `enable_cache()`: a UNet pipeline such as SD 1.5 has no transformer, and Z-Image's transformer has no cache support, so both get a warning that the cache was not applied and no calibration. Paste that array into `mag_ratios` and drop the `calibrate` flag for subsequent runs. `N` is the forwards the run actually made, which is not always the call's `num_inference_steps`: MiniMax-H3's scheduler turns N sigmas into N - 1 evaluations, so a 30-step H3 call reports 29 ratios. Set the cache's `num_inference_steps` to that `N` when you paste them, so each ratio lines up with its forward. A pipeline that runs classifier-free guidance as two transformer passes in their own `cond` and `uncond` cache contexts (Qwen-Image-2.1 with `true_cfg_scale` above 1 and a `negative_prompt`, or the other Qwen-Image pipelines) keeps a separate state for each, so it emits **two** lists, each labeled `[cond]` or `[uncond]`; use the `cond` one, since `mag_ratios` takes one array. A guidance-distilled pipeline such as Flux, or one that batches both passes into one forward emits one unlabeled list.
+For a model with no preset, run once with `"calibrate": true`:
 
 ```json
 "cache": { "type": "mag", "calibrate": true, "num_inference_steps": 28 }
 ```
 
+Calibration skips nothing. At the end of the run the ratios appear as a job warning and a `log` event (`get_job`'s `warnings`, `get_job_events`, `export_job`): `MagCache calibration (N steps): [ ... ] — paste into mag_ratios`. While calibrating, the run's own `num_inference_steps` is used, and a mismatch with the cache's is reported as a warning. Paste the array into `mag_ratios`, drop `calibrate`, and set the cache's `num_inference_steps` to the reported `N`, which counts the forwards actually made: MiniMax-H3's scheduler turns N sigmas into N - 1 evaluations, so a 30-step H3 call reports 29 ratios.
+
+A pipeline that runs classifier-free guidance as two passes in separate `cond` and `uncond` cache contexts (Qwen-Image-2.1 with `true_cfg_scale` above 1 and a `negative_prompt`, or the other Qwen-Image pipelines) emits two lists, labeled `[cond]` and `[uncond]`; use the `cond` one. A guidance-distilled pipeline such as Flux, or one that batches both passes into one forward, emits one unlabeled list. A pipeline the cache cannot attach to (see `cache_unsupported` above) reports nothing.
+
 ### TaylorSeerCache
 
-Taylor series approximation of cached outputs:
+Approximates skipped outputs with a Taylor series:
 
 ```json
 "cache": {
@@ -111,13 +94,13 @@ Taylor series approximation of cached outputs:
 | Property | Default | Description |
 | -------- | ------- | ----------- |
 | `cache_interval` | 5 | Full computation every N steps |
-| `max_order` | 1 | Taylor series order (higher = better approximation, and more state kept on the GPU) |
+| `max_order` | 1 | Taylor series order (higher = better approximation, more state on the GPU) |
 
-TaylorSeer keeps per-block state on the GPU that no `vram_estimate` charges, so a job that fits without the cache can run out of memory with it. On MiniMax-H3 at 1344x768, 30 steps and `max_order` 1, a run that `validate_workflow` had passed ran out of memory 2 min in on a 24 GB card. Validate says so as `taylorseer_state_unestimated` whenever `taylorseer` is set, with the job's estimate and the card's capacity when both are known; the state itself is on top of that figure, and is not sized.
+TaylorSeer keeps per-block state on the GPU that no `vram_estimate` charges, so a job that fits without it can run out of memory with it. On MiniMax-H3 at 1344x768, 30 steps and `max_order` 1, a job `validate_workflow` had passed ran out of memory 2 min in on a 24 GB card. Validate warns `taylorseer_state_unestimated`, with the job's estimate and the card's capacity when both are known; the state comes on top of that figure and is not sized.
 
 ### FasterCache
 
-Experimental, video-oriented. Uses FFT frequency decomposition:
+Experimental and video-oriented (CogVideoX, for example); uses FFT frequency decomposition. dw wires diffusers' `current_timestep_callback` to the pipeline, so no other parameter is needed for basic use:
 
 ```json
 "cache": {
@@ -125,16 +108,13 @@ Experimental, video-oriented. Uses FFT frequency decomposition:
 }
 ```
 
-Best for video models like CogVideoX. No additional parameters needed for basic use.
-
 | Property | Default | Description |
 | -------- | ------- | ----------- |
-| `is_guidance_distilled` | false | Set `true` for a guidance-distilled model (FLUX.1-dev, FLUX.1-Krea-dev), where `guidance_scale` is an embedding and the transformer sees a batch of 1. FasterCache otherwise assumes a cond/uncond batch of 2 and fails at the first step with `tuple index out of range` (#895). Validate cannot tell a distilled model from a CFG one, so it does not check this |
+| `is_guidance_distilled` | inferred | `true` for a guidance-distilled model (FLUX.1-dev, FLUX.1-Krea-dev), where `guidance_scale` is an embedding and the transformer sees a batch of 1. Unset, dw sets it `true` when the transformer's config has `guidance_embeds: true`, else diffusers' `false`. Wrong, FasterCache assumes a cond/uncond batch of 2 and fails at the first step with `tuple index out of range` (#895). Validate does not check it |
 
 ### TextKVCache
 
-Caches the transformer's key/value projections of the (unchanging) text
-embeddings across denoising steps, recomputing only what the latents need:
+Caches the transformer's key/value projections of the unchanging text embeddings across steps. No parameters.
 
 ```json
 "cache": {
@@ -142,15 +122,15 @@ embeddings across denoising steps, recomputing only what the latents need:
 }
 ```
 
-No parameters.
-
 ### Reading the skip counts
 
-A step that set a `cache` reports `timing.cache` on its manifest entry (fields in [WORKSPACES.md](WORKSPACES.md#step-timing)). `hit_rate` is the fraction of denoiser forwards whose remaining blocks were skipped. It counts forwards, not steps: classifier-free guidance runs two per step, so a 30-step run is 60 forwards. Only `first_block` and `mag` are counted; `taylorseer`, `faster` and `text_kv` report null counts. A cache with zero skips can still change the output, so a 0-skip run is not guaranteed bit-identical to an uncached one. With `first_block` the head block always runs, and enabling any cache wraps the blocks in hooks that recompute the residual (the tail's output minus the head's, in the transformer's dtype), which changes the order and precision of operations; the MiniMax-H3 matrix run P2 saw a changed output with nothing skipped. Judge quality against an uncached run, not against `skipped == 0`. A threshold sweep (#875) reads `hit_rate` against quality.
+A step with a `cache` reports `timing.cache` on its manifest entry (fields in [WORKSPACES.md](WORKSPACES.md#step-timing)). `hit_rate` is the fraction of denoiser forwards whose remaining blocks were skipped. It counts forwards, not steps: classifier-free guidance runs two per step, so a 30-step run is 60 forwards. Only `first_block` and `mag` are counted; `taylorseer`, `faster` and `text_kv` report null counts.
+
+Zero skips is not an uncached run. With `first_block` the head block always runs, and any cache wraps the blocks in hooks that recompute the residual in the transformer's dtype, which changes the order and precision of operations; the MiniMax-H3 matrix run P2 changed output with nothing skipped. Judge quality against an uncached run (a `null` cache), not against `skipped == 0`. A threshold sweep (#875) reads `hit_rate` against quality.
 
 ## Attention Backends
 
-Select the attention implementation diffusers uses for the duration of each pipeline call, via a context manager wrapped around `pipeline(...)`:
+`attention_backend` in the pipeline `configuration` selects diffusers' attention implementation for the duration of each pipeline call, via a context manager around `pipeline(...)`:
 
 ```json
 "configuration": {
@@ -159,33 +139,48 @@ Select the attention implementation diffusers uses for the duration of each pipe
 }
 ```
 
-Common values: `"flash"`, `"flash_hub"`, `"sage"`, `"sage_hub"`, `"native"`, `"flex"`. The full set is diffusers' `AttentionBackendName` enum - availability depends on what's installed (`flash-attn`, `sageattention`, etc.) and the platform. `_hub`-suffixed backends are fetched from the Hugging Face Hub kernel registry on first use, which needs the `kernels` package installed (`pip install kernels`) - it is not a dw dependency. The only backend a bundled workflow sets is `sparse`, the default on every MiniMax-H3 template, and it needs no `kernels` install (comfy-kitchen is a core dependency), so each bundled workflow runs on a plain install. diffusers also refuses a Hub kernel whose publisher it does not list as trusted - `sage_hub` among them, since SageAttention 2 comes from `SageAttention/sage-attention` - until `DIFFUSERS_TRUST_REMOTE_KERNELS=true`, which allows remote code from any such publisher. On an RTX 3090 `sage_hub` measured about 30% faster per MiniMax-H3 step (13.8 s against 19.2 s at 960x544x124, 54.7 s against 81.2 s at 345 frames); the local `sage` backend needs `sageattention>=2.1.1`, which PyPI does not carry.
+A component can pin its backend instead, persistently, via `set_attention_backend`:
+
+```json
+"configuration": {
+    "components": {
+        "transformer": { "attention_backend": "flash_hub" }
+    }
+}
+```
+
+Pin it on a compiled component: the per-call context manager switches implementations under the compiled graph and forces a recompile on every run. Set at both levels, the component's own wins.
+
+Values are diffusers' `AttentionBackendName` enum, plus dw's [`sparse`](#sparse-minimax-h3). Common ones: `native`, `flex`, `flash`, `flash_hub`, `sage`, `sage_hub`. Availability depends on the installed packages and the platform:
+
+- `_hub` backends are fetched from the Hugging Face Hub kernel registry on first use through the `kernels` package, which dw installs.
+- diffusers refuses a Hub kernel from a publisher it does not list as trusted - `sage_hub` among them (SageAttention 2 from `SageAttention/sage-attention`) - until `DIFFUSERS_TRUST_REMOTE_KERNELS=true` is set in the server's environment (`.env`). That allows remote code from any such publisher.
+- The local `sage` backend needs `sageattention>=2.1.1`, which PyPI does not carry.
+- The flash, flash-3, sage and xformers families need CUDA, `_hub` variants included. On a server whose device is not CUDA, `validate_workflow` warns `attention_backend_unavailable`, naming the backend and the device: the job would fail at load. A `native`-family backend never warns, and a backend diffusers adds later draws no warning until dw's list (`CUDA_ONLY_ATTENTION_BACKENDS`) is updated.
+
+On an RTX 3090 `sage_hub` measured about 30% faster per MiniMax-H3 step than `native` (13.8 s against 19.2 s at 960x544x124, 54.7 s against 81.2 s at 345 frames). LTX-2.5 cannot use sage: its transformer passes an attention mask, which sage refuses ("`attn_mask` is not supported for sage attention", measured 2026-10-10), so the LTX templates expose no backend.
 
-Every MiniMax-H3 template in [templates/minimax/](../workflows/templates/minimax/)
-exposes this as a variable: `attention_backend`, pinned on the H3 step's transformer
-(`components.transformer`, or `components.transformer_ref` for a ref2va workflow) and
-`"sparse"` by default (with `sparse_tau`, below). That covers the shots, chains and episodes - `dialogue-short`,
-`shots-batch`, `music-video` and `storyboard` among them - and only their H3 steps: a
-Z-Image still or Music3 song beside an H3 shot keeps the process default. Pass
-`attention_backend=sage_hub` (or `native`) to override the default; `sage_hub` needs a box with the `kernels` package and
-`DIFFUSERS_TRUST_REMOTE_KERNELS=true` in its `.env`; on MPS the default falls back to dense, but a CUDA-only kernel such as `sage_hub` fails there at load (validation warns, below). LTX-2.5 cannot use it: its transformer
-passes an attention mask, and sage attention refuses one ("`attn_mask` is not supported
-for sage attention", measured 2026-10-10), so the LTX templates expose no backend.
+Either form stays on what it names. diffusers' `set_attention_backend` also makes its backend the process-wide default, which the persistent worker would keep for every later model; dw restores the default after pinning, so an H3 `sage_hub` pin cannot reach a Music3 or LTX job that follows. The server-wide default is `DIFFUSERS_ATTN_BACKEND` in `.env`; leave it `native`, since it reaches every model and sage cannot serve all of them.
 
 ### `sparse` (MiniMax-H3)
 
-`attention_backend: "sparse"` runs H3's attention through comfy-kitchen's block-sparse kernel: each query tile attends only to the key tiles that matter, so the cost of the long video-token sequence falls. It applies to the H3 transformer only, at pipeline or component level, and is the default on every H3 template (#931). Set at both levels, the component's own `attention_backend` wins, as it does for every backend: a pipeline-level `sparse` skips a component pinned to `sage_hub` (or anything else) and warns `attention_backend 'sparse' at pipeline level skips '<component>', which pins '<backend>'`. When it does install, the run logs `Using attention backend: sparse (tau <tau>) on <components>`, or `(topk <k>)` with top-k routing. `sparse_tau` (number, minimum 0, default 0.5; anything else, such as a `variable:` that resolves to a string or a bool, fails the step rather than running dense) sets how much of the attention mass a tile may drop; 0 keeps the most. Observed at Ref2VA 1344x768, seed 42: tau 1.0 ran 42.1 s/forward against 44.8 s at 0.5, and by Don's eye 1.0 gave smoother skin while 0.5 matched dense (the table below). That is one shape and one seed, not a rule that higher is always faster or smoother.
+`attention_backend: "sparse"` runs H3's attention through comfy-kitchen's block-sparse kernel (`dw/sparse_attention.py`, #931): each query tile attends only to the key tiles that matter, so the cost of the long video-token sequence falls. Text, references and audio stay dense. comfy-kitchen is a core dependency, so it needs no `kernels` package or `DIFFUSERS_TRUST_REMOTE_KERNELS`. The only backend a bundled workflow sets is `sparse`.
 
-`sparse_topk` (a number strictly between 0 and 1; the H3 templates set 0.15, and a workflow that sets none routes by `sparse_tau`) switches the kernel to top-k routing: each query tile keeps that fraction of key tiles and drops the rest outright, with `sparse_tau` ignored. It is the selection lightx2v's Turbo-SLA LoRAs were trained against (85% sparsity, so `0.15`). Measured on `video-with-audio-768p`, seed 42 (#932): top-k 0.15 ran 21.1 s/forward against 23.6 s at tau 0.5, about 11% faster, and Don saw no difference on that clip, with either the Turbo-SLA LoRA or the default v1.2 turbo (so dw does not adopt the SLA LoRA). That clip has no faces, so it was checked on the H3 quality ramp's 4-reference sitcom shot at seeds 42, 7 and 1234 against tau 0.5. Don judged top-k equal or better: on the turbo L7 rung tau showed blue ghosting at seed 7 and slight smearing at all three, and on the 30-step base-model L5 rung the skin held. Top-k 0.15 is therefore the H3 templates' default; pass `sparse_topk=null` to route by `sparse_tau` instead.
+Every H3 template in [templates/minimax/](../workflows/templates/minimax/) takes it as variables, pinned on the H3 step's transformer (`components.transformer`, or `components.transformer_ref` for a ref2va workflow): `attention_backend` (default `"sparse"`), `sparse_tau` (0.5) and `sparse_topk` (0.15). That covers the shots, chains and episodes (`dialogue-short`, `shots-batch`, `music-video`, `storyboard` among them), and only their H3 steps: a Z-Image still or Music3 song beside an H3 shot keeps the process default. Pass `attention_backend=sage_hub` (or `native`) to override.
+
+- **Where it applies**: the H3 transformer, at pipeline or component level. On any other component, or a pipeline with no H3 transformer, it runs dense with a warning. A pipeline-level `sparse` skips a component pinned to another backend and warns `attention_backend 'sparse' at pipeline level skips '<component>', which pins '<backend>'`. When it installs, the run logs `Using attention backend: sparse (tau <tau>) on <components>`, or `(topk <k>)`.
+- **`sparse_tau`**: number >= 0, default 0.5. How much of the attention mass a tile may drop: higher is sparser and faster, 0 keeps the most. Anything else, such as a `variable:` that resolves to a string or a bool, fails the step rather than running dense.
+- **`sparse_topk`**: a number strictly between 0 and 1, unset by default outside the templates. Switches to top-k routing: each query tile keeps that fraction of key tiles and drops the rest, and `sparse_tau` is ignored. It is the selection lightx2v's Turbo-SLA LoRAs were trained against (85% sparsity, so `0.15`). Pass `sparse_topk=null` to an H3 template to route by `sparse_tau`.
+- Both are runtime settings: changing one reuses the loaded pipeline.
+
+**Top-k 0.15 against tau 0.5** (#932). On `video-with-audio-768p`, seed 42, top-k ran 21.1 s/forward against 23.6 s at tau 0.5, about 11% faster, with no visible difference under the Turbo-SLA LoRA or the default v1.2 turbo (so dw does not adopt the SLA LoRA). On the H3 quality ramp's 4-reference sitcom shot, seeds 42, 7 and 1234, Don judged top-k equal or better: on the turbo L7 rung tau showed blue ghosting at seed 7 and slight smearing at all three, and on the 30-step base-model L5 rung the skin held.
 
 | rung (minutes per job, seeds 42 / 7 / 1234) | tau 0.5 | top-k 0.15 |
 |---|---|---|
 | L7: ref2v 4-step turbo, int8 | 6.5 / 6.5 / 6.5 | 6.1 / 6.2 / 5.8 |
 | L5: base model, int8, 30 steps | 26.8 / 26.6 / 26.4 | 23.9 / 25.5 / 23.9 |
 
-**What the default buys depends on what it replaces.** Against `native` attention, which is what an H3 template ran before #931 unless `attention_backend` was set, sparse is 2.2-4.4x faster per step. Against `sage_hub`, the gain is much smaller: 6-16% depending on the shape (the second table). So sparse's main win is for a run that set no backend, and it needs no `kernels` package or `DIFFUSERS_TRUST_REMOTE_KERNELS`. A box already on `sage_hub` gains a little, at equal quality: on the H3 skill's quality-ramp rungs, Don judged faces, skin and draft-to-final staging equal under both backends across three seeds (#931 stage 3).
-
-Measured on an RTX 3090, seed 42, s per transformer forward (one per denoise step), lower is faster:
+**Sparse against `native` and `sage_hub`.** Against `native`, which an H3 template ran before #931 unless `attention_backend` was set, sparse is 2.2-4.4x faster per step. Against `sage_hub` the gain is 6-16% depending on the shape, at equal quality: on the H3 skill's quality-ramp rungs Don judged faces, skin and draft-to-final staging equal under both across three seeds (#931 stage 3). RTX 3090, seed 42, s per transformer forward (one per denoise step):
 
 | shape | native | `sage_hub` | sparse | sparse vs native | sparse vs `sage_hub` |
 |---|---|---|---|---|---|
@@ -194,9 +189,9 @@ Measured on an RTX 3090, seed 42, s per transformer forward (one per denoise ste
 | Ref2VA 1344x768, 8-step ref2v turbo, tau 1.0 | 108.4 | - | 42.1 | 2.58x | - |
 | Ref2VA 1344x768, 8-step ref2v turbo, tau 0.5 (the default) | 108.4 | 51.0 | 44.1-44.8 | 2.42x | 1.16x |
 
-Wall time, same order: 150 -> 95 s, 457 -> 158 s, 965 -> 430 s, and for the last row 965 s native, 489 s `sage_hub`, 442-450 s sparse.
+Wall time, same order: 150 -> 95 s, 457 -> 158 s, 965 -> 430 s, and for the last row 965 s native, 489 s `sage_hub`, 442-450 s sparse. Peak VRAM did not change. By eye, tau 0.5 matched dense and tau 1.0 gave smoother skin; that is one shape and one seed, not a rule that higher tau is always faster or smoother. The native and tau 1.0 figures come from the #931 prototype; the `sage_hub` figure and the 44.1 s come from the engine on lem after the deploy, with a newer diffusers (jobs 94c04976d21d and 593131ca713f), so the sage comparison is between those two jobs only.
 
-Against `sage_hub` on the H3 skill's quality-ramp rungs (ep 6 cold open, 4 references, 1344x768, 124 frames; minutes per job, seeds 42 / 7 / 1234, all on the same diffusers):
+On the quality-ramp rungs (ep 6 cold open, 4 references, 1344x768, 124 frames; minutes per job, seeds 42 / 7 / 1234, same diffusers):
 
 | rung | `sage_hub` | sparse | sparse vs `sage_hub` |
 |---|---|---|---|
@@ -205,27 +200,11 @@ Against `sage_hub` on the H3 skill's quality-ramp rungs (ep 6 cold open, 4 refer
 | B3: base model, int8, 12 steps | 13.0 / 12.9 / 12.9 | 12.3 / 12.0 / 12.1 | 1.06-1.07x |
 | L5: base model, int8, 30 steps | 29.7 / 29.7 / 29.7 | 26.8 / 26.6 / 26.4 | 1.11-1.12x |
 
-The `first_block` cache on `sage_hub` (23.1 min at L5's settings) is still faster than sparse alone there; sparse with that cache is not measured. The native and tau 1.0 figures come from the #931 prototype. The `sage_hub` figure and the 44.1 s come from the engine on lem after the deploy, with a newer diffusers (jobs 94c04976d21d and 593131ca713f), so the sage comparison is between those two jobs only. The T2VA rows have no `sage_hub` run. Peak VRAM did not change. Judged by eye, tau 0.5 matched dense; tau 1.0 was smoother in skin.
+The `first_block` cache on `sage_hub` (23.1 min at L5's settings) is still faster than sparse alone there; sparse with that cache is not measured.
 
-It needs CUDA with compute capability 8.0 or higher. Anywhere else (MPS, CPU, an older card, an unsupported dtype or head size) the run goes dense instead of failing, and says so twice: `validate_workflow` warns `sparse_attention_fallback` when the server's device is not CUDA, and the run reports `<label>: sparse attention ran dense - <reason>` once per pipeline call. A job of one call warns once; a chained, `shots-batch` or `dialogue-short` job calls the pipeline once per segment or shot, so it warns once for each, under that segment's or shot's label. A comfy-kitchen kernel that fails on its first invocation in a call also runs that call dense, with `sparse kernel failed: <error>` as the reason; one that fails after it has run in the call fails the run, since some blocks have already run sparse.
+**Where it cannot run.** It needs CUDA with compute capability 8.0 or higher. Anywhere else (MPS, CPU, an older card, an unsupported dtype or head size) the run goes dense instead of failing, and says so twice: `validate_workflow` warns `sparse_attention_fallback` when the server's device is not CUDA, and the run reports `<label>: sparse attention ran dense - <reason>` once per pipeline call, so a chained, `shots-batch` or `dialogue-short` job warns once per segment or shot, under its label. Whether a call runs sparse is decided once, before its first forward: a comfy-kitchen kernel that fails on its first invocation runs that call dense (`sparse kernel failed: <error>`), and one that fails after it has run fails the run.
 
-Unlike sage, sparse **moves composition** relative to dense: the same subject, story and cuts, but different framing and set dressing. A draft and its final must therefore use the same backend; mixing `sparse` and `sage_hub` between tiers means the draft no longer predicts the final.
-
-A component can also pin its backend persistently instead, via `set_attention_backend`:
-
-```json
-"configuration": {
-    "components": {
-        "transformer": { "attention_backend": "flash_hub" }
-    }
-}
-```
-
-Prefer the pinned form for a compiled component - the per-call context manager switches implementations under the compiled graph and forces a recompile on every run.
-
-The flash, flash-3, sage and xformers backends need CUDA, `_hub` variants included. On a server whose device is not CUDA (`mps`, `cpu`), `validate_workflow` warns `attention_backend_unavailable` for one set at pipeline or component level, naming the backend and the device: the job would fail at load. It never refuses, and a `native`-family backend never warns. A backend diffusers adds later is not on the list until dw's is updated, and draws no warning.
-
-Either form stays on what it names. diffusers' `set_attention_backend` also makes its backend the process-wide default, which the persistent worker would keep for every later model; dw restores the default after pinning, so an H3 `sage_hub` pin cannot reach a Music3 or LTX job that follows it. The server-wide default is `DIFFUSERS_ATTN_BACKEND` in `.env` - leave it `native`, since it reaches every model and sage cannot serve all of them.
+**Sparse moves composition** relative to dense, unlike sage: the same subject, story and cuts, but different framing and set dressing. A draft and its final must use the same backend; mixing `sparse` and `sage_hub` between tiers means the draft no longer predicts the final.
 
 ## Attention Slicing
 
@@ -236,11 +215,11 @@ Either form stays on what it names. diffusers' `set_attention_backend` also make
 }
 ```
 
-Processes attention in slices to reduce memory at some cost to speed. Automatic on MPS unless `disable_attention_slicing` is set, opt-in elsewhere. Only UNet/ControlNet models take it. On MPS, whether it helps depends on the model's attention head dim: MPS SDPA is slow at 40, 48 and 160 (slicing made SD 1.5 ~20% faster end to end) and fast at 64-128 (slicing made SDXL-shaped attention 2.4x slower - set `disable_attention_slicing` for SDXL-family models). Modular pipelines have no `enable_attention_slicing()` method - the setting is silently skipped rather than failing when the pipeline doesn't support it.
+Processes attention in slices to reduce memory at some cost to speed. Automatic on MPS unless `disable_attention_slicing` is set, opt-in elsewhere. Only UNet/ControlNet models take it; a pipeline with no `enable_attention_slicing()` (a modular pipeline) skips it. On MPS the effect depends on the attention head dim: MPS SDPA is slow at 40, 48 and 160 (slicing made SD 1.5 ~20% faster end to end) and fast at 64-128 (slicing made SDXL-shaped attention 2.4x slower), so set `disable_attention_slicing` for SDXL-family models.
 
 ## torch.compile
 
-Compile a component once it is fully configured - the graph captures final dtypes, adapters, quantization, and offload hooks. Configured per component under `components`:
+Configured per component under `components`. The component is compiled once fully configured, so the graph captures final dtypes, adapters, quantization and offload hooks:
 
 ```json
 "configuration": {
@@ -258,26 +237,27 @@ Compile a component once it is fully configured - the graph captures final dtype
 
 | Property | Description |
 | -------- | ----------- |
-| `repeated_blocks` | Compile only the model's repeated block classes (diffusers regional compilation). Near the same speedup as full compilation with a fraction of the cold-start cost. Recommended. |
-| `mode` | torch.compile mode: `"default"`, `"reduce-overhead"`, `"max-autotune"`. |
-| `fullgraph` | Require a single graph with no breaks - fails fast instead of silently losing speedup. |
-| `dynamic` | Compile with dynamic shapes. Set `true` when resolutions or frame counts vary between runs to avoid recompiles. |
-
-Typical gains are 1.3-1.5x on diffusion transformers, and compilation stacks with the caches above. Notes:
-
-- **First run pays the compile cost.** The [server](SERVER.md)'s persistent worker keeps compiled pipelines loaded between runs, so the cost is paid once per session rather than once per generation. A new session still retraces, but the kernels it built come back from the on-disk cache, which dw keeps across reboots (`TORCHINDUCTOR_CACHE_DIR`, see [Environment Defaults](#environment-defaults)). SDNQ's `triton.jit`/autotune kernels are cached separately by Triton itself (`TRITON_CACHE_DIR`, else `$TRITON_HOME/.triton/cache`, else `~/.triton/cache`); dw sets no `TRITON_*` variable and leaves that cache where it is, shared with any other Triton user on the machine. Both caches' sizes show in `get_server_info`'s `caches` and `GET /api/caches`, and `DELETE /api/caches/{name}` empties one ([SERVER.md](SERVER.md)); the next first step of each shape pays the compile or re-autotune again (100-300 s per shape on MiniMax-H3 for Triton).
-- **Measure the cold cost from `timing`.** A pipeline step's manifest entry carries `timing` ([WORKSPACES.md](WORKSPACES.md#step-timing)): on a cold run the compile cost is about `first_pass_s - pass_s`, and `compiled` says whether compile was on. `pass_s` is wall time at step boundaries, not kernel time - on CUDA kernels run asynchronously, so a pass boundary is when the host saw it.
-- **Pin the attention backend** on a compiled component (`"attention_backend"` in the same `components` entry) rather than using the pipeline-level per-call context manager, which forces recompiles.
-- **Quantize-on-load time is a separate cost.** An SDNQ on-the-fly component re-reads and re-quantizes its bf16 weights on every cold load; `DW_QUANTIZED_CACHE=1` saves the quantized result and reads it back instead - see [QUANTIZATION.md](QUANTIZATION.md#cached-on-the-fly-dw_quantized_cache).
-- **Composes with offloading**: apply `group_offload` and `compile` on the same component and the offload hooks are installed first, as required. Skipped with a warning on MPS. The compile cost is paid again every time the worker releases the pipeline ("Workflow changed - releasing cached models"), i.e. on any workflow change, so it pays off only across many runs of one workflow at one shape.
-- **Don't combine `fullgraph` with a `cache`**: the cache hooks decide skip-or-compute per step, a data-dependent branch diffusers wraps in `torch.compiler.disable` - it needs the graph break that `fullgraph: true` forbids. Compile with the default (partial) graph mode when a cache is active. `validate_workflow` warns `compile_fullgraph_with_cache` on a component that sets `fullgraph: true` in a pipeline with a `cache`.
-- **TorchAO quantization needs compile to be fast** - see [QUANTIZATION.md](QUANTIZATION.md#torchao).
+| `repeated_blocks` | Compile only the model's repeated block classes (diffusers regional compilation): near the speedup of full compilation at a fraction of the cold-start cost. Recommended. A component without `compile_repeated_blocks()` fails |
+| `mode` | torch.compile mode: `"default"`, `"reduce-overhead"`, `"max-autotune"` |
+| `fullgraph` | Require a single graph with no breaks: fails fast instead of silently losing speedup |
+| `dynamic` | Compile with dynamic shapes. Set `true` when resolutions or frame counts vary between runs, to avoid recompiles |
+
+Typical gains are 1.3-1.5x on diffusion transformers, and compile stacks with the caches above.
+
+- **The first run pays the compile cost.** The [server](SERVER.md)'s persistent worker keeps compiled pipelines loaded between runs. It releases them on any workflow change ("Workflow changed - releasing cached models"), and the compile is paid again, so compile pays off across many runs of one workflow at one shape. A new worker retraces, but Inductor's kernels come back from its on-disk cache (`TORCHINDUCTOR_CACHE_DIR`, see [Environment Defaults](#environment-defaults)). SDNQ's Triton kernels are cached by Triton itself (`TRITON_CACHE_DIR`, else `$TRITON_HOME/.triton/cache`, else `~/.triton/cache`); dw sets no `TRITON_*` variable. Both caches' sizes show in `get_server_info`'s `caches` and `GET /api/caches`, and `DELETE /api/caches/{name}` empties one ([SERVER.md](SERVER.md)); the next first step of each shape pays the compile or re-autotune again (100-300 s per shape on MiniMax-H3 for Triton).
+- **Measure the cold cost from `timing`.** A pipeline step's manifest entry carries `timing` ([WORKSPACES.md](WORKSPACES.md#step-timing)): on a cold run the compile cost is about `first_pass_s - pass_s`, and `compiled` says whether compile was on. `pass_s` is wall time at step boundaries, not kernel time.
+- **Pin the attention backend** on a compiled component ([Attention Backends](#attention-backends)).
+- **Don't combine `fullgraph` with a `cache`.** The cache's per-step skip decision is a data-dependent branch diffusers wraps in `torch.compiler.disable`, which needs the graph break `fullgraph: true` forbids. `validate_workflow` warns `compile_fullgraph_with_cache` on such a component.
+- **Composes with offloading**: `group_offload` and `compile` on the same component install the offload hooks first, as required.
+- **Skipped on MPS** with a warning.
+- **Quantize-on-load is a separate cost.** An SDNQ on-the-fly component re-quantizes on every cold load unless `DW_QUANTIZED_CACHE=1` ([QUANTIZATION.md](QUANTIZATION.md#cached-on-the-fly-dw_quantized_cache)).
+- **TorchAO quantization needs compile to be fast** ([QUANTIZATION.md](QUANTIZATION.md#torchao)).
 
 **Example:** [flux-dev-compile.json](../workflows/models/flux-dev-compile.json), [flux-torchao.json](../workflows/models/flux-torchao.json)
 
 ## Layerwise Casting
 
-Store a component's weights in a narrow dtype and upcast only for compute, per component:
+Store a component's weights in a narrow dtype and upcast only for compute:
 
 ```json
 "transformer": {
@@ -290,18 +270,17 @@ Store a component's weights in a narrow dtype and upcast only for compute, per c
 }
 ```
 
-Both `storage_dtype` and `compute_dtype` are required. Applied via the component's own `enable_layerwise_casting()` right after it loads, so it composes with quantization and group offloading on the same component.
+Both dtypes are required. It is applied through the component's own `enable_layerwise_casting()` right after it loads, so it composes with quantization and group offloading on the same component. The pipeline `configuration` takes the same block.
 
 ## Memory Format and Attention Processors
 
-Three older per-component knobs, set in the pipeline `configuration` beside
-`vae` / `unet` / `transformer` and applied right after the components load:
+Three per-component keys, set in the pipeline `configuration` beside `vae` / `unet` / `transformer` and applied right after the components load:
 
 | Key | Where | Effect |
 | --- | ----- | ------ |
 | `channels_last` | `vae`, `unet` | `to(memory_format=torch.channels_last)`. Faster convolutions on CUDA for a convolutional UNet or VAE; nothing to gain on a transformer |
-| `enable_forward_chunking` | `unet` | Runs the UNet's feed-forward layers in chunks - less peak memory, slightly slower |
-| `attn_processor_type` | `unet`, `transformer` | Names an attention processor class to install with `set_attn_processor` (the name is resolved and constructed, so it goes through the `_type` conversion: `"AttnProcessor2_0"`). For a per-call backend instead, see [Attention Backends](#attention-backends) |
+| `enable_forward_chunking` | `unet` | Runs the UNet's feed-forward layers in chunks: less peak memory, slightly slower |
+| `attn_processor_type` | `unet`, `transformer` | An attention processor class to construct and install with `set_attn_processor`, named through the `_type` conversion (`"AttnProcessor2_0"`). For a per-call backend instead, see [Attention Backends](#attention-backends) |
 
 ```json
 "configuration": {
@@ -313,43 +292,32 @@ Three older per-component knobs, set in the pipeline `configuration` beside
 
 ## Memory Offloading
 
-`offload` (`"model"` or `"sequential"`) and `group_offload` trade speed for VRAM by streaming weights between system memory and the accelerator instead of keeping everything resident. `"model"` moves whole submodules and costs the least speed; `"sequential"` moves individual layers and is the slowest but uses the least memory; block/leaf-level `group_offload` sits between the two and is what a modular pipeline's self-loaded components use, since they aren't reachable in time for `offload`. Full configuration syntax is in [WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#memory-offloading). Omit both for the fastest run, when VRAM allows it. Measured on an RTX 3090 (RECIPES_24GB.md): sequential
-offload costs about 6x per step on FLUX dev and 3.4x on Z-Image against model offload;
-a block-level, streamed `group_offload` of the transformer from pinned host memory
-matches the resident per-step cost while holding one block on the card, at ~22 GB of
-pinned host RAM per FLUX pipeline - the placement the unquantized FLUX templates use.
+`offload` (`"model"` or `"sequential"`) and `group_offload` stream weights between system memory and the accelerator instead of keeping them resident; syntax is in [WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#memory-offloading). Omit both for the fastest run when VRAM allows.
 
-`"residency": "on_demand"` on a component is the cheap case of the same trade: the model rests in system memory and is moved to the device whole around each of its own calls. That is a bad deal for anything called once per step, and a good one for a VAE called twice a run - it frees the VAE's VRAM for the denoise loop at the cost of two transfers, where group offloading the same VAE would restream it once per decode tile. See [On-demand components](WORKFLOW_GUIDE.md#on-demand-components).
+- `"model"` moves whole submodules and costs the least speed.
+- `"sequential"` moves individual layers: the slowest, and the least memory.
+- `group_offload` (block or leaf level) sits between them, and is what a modular pipeline's self-loaded components use, since they are not reachable in time for `offload`.
+- `"residency": "on_demand"` on a component rests the model in system memory and moves it to the device whole around each of its own calls. A bad deal for anything called once per step; a good one for a VAE called twice a run, where group offloading would restream it once per decode tile ([On-demand components](WORKFLOW_GUIDE.md#on-demand-components)).
 
-**Example:** [flux-dev.json](../workflows/models/flux-dev.json) (`"offload": "model"`), [z-image.json](../workflows/models/z-image.json) (`"offload": "sequential"`), [video-with-audio.json](../workflows/templates/minimax/video-with-audio.json) (`group_offload` per component), [reference-to-video.json](../workflows/templates/minimax/reference-to-video.json) (`group_offload` for the transformer, `on_demand` for the VAEs)
+Measured on an RTX 3090 ([RECIPES_24GB.md](RECIPES_24GB.md)): sequential offload costs about 6x per step on FLUX dev and 3.4x on Z-Image against model offload. A block-level, streamed `group_offload` of the transformer from pinned host memory matches the resident per-step cost while holding one block on the card, at ~22 GB of pinned host RAM per FLUX pipeline; the unquantized FLUX templates use it.
+
+**Example:** [flux-dev.json](../workflows/models/flux-dev.json) (`"offload": "model"`), [krea2.json](../workflows/models/krea2.json) (`"offload": "sequential"`), [video-with-audio.json](../workflows/templates/minimax/video-with-audio.json) (`group_offload` per component), [reference-to-video.json](../workflows/templates/minimax/reference-to-video.json) (`group_offload` for the transformer, `on_demand` for the VAEs)
 
 ## Reading Memory While Offloading
 
-A workflow that offloads keeps its weights in host memory by design, so the
-card can sit near-empty through a generation and the VRAM figures alone say
-nothing about what a run holds or fails to release. `get_memory` (MCP) and
-`GET /api/memory` report both: `gpu_*` is the card, `host_memory_rss_mb` is
-what the worker process holds and `host_memory_peak_rss_mb` the most it has
-ever held, beside the machine's `host_memory_total_mb` /
-`host_memory_available_mb`.
-
-`host_pinned_reserved_mb` / `host_pinned_allocated_mb`, where the platform
-reports them, are torch's pinned-host cache - the staging buffers group
-offloading moves weights through. They are part of `host_memory_rss_mb` and
-invisible in every `gpu_*` figure, so a worker that has released every model
-and still holds gigabytes is usually holding these; they are returned when
-the worker switches to a different workflow (#98). A host field is absent,
-rather than null, on a platform that cannot measure it.
+A workflow that offloads keeps its weights in host memory, so the card can sit near-empty through a generation and the VRAM figures alone say nothing about what a run holds or fails to release. `get_memory` (MCP) and `GET /api/memory` report both: `gpu_*` is the card; `host_memory_rss_mb` is what the worker process holds and `host_memory_peak_rss_mb` the most it has held, beside the machine's `host_memory_total_mb` and `host_memory_available_mb`.
+
+`host_pinned_reserved_mb` / `host_pinned_allocated_mb`, where the platform reports them, are torch's pinned-host cache: the staging buffers group offloading moves weights through. They are part of `host_memory_rss_mb` and invisible in every `gpu_*` figure, so a worker that has released every model and still holds gigabytes is usually holding these; they are returned when the worker switches to a different workflow (#98). A host field is absent, not null, on a platform that cannot measure it.
 
 ## TF32 and cuDNN
 
-Device-level settings, read once at startup from `~/.diffusers_helper/settings.json`:
+Device-level settings, read once at startup from `~/.diffusers_helper/settings.json` (the directory moves with `DIFFUSERS_HELPER_ROOT`):
 
 | Setting | Default | Effect |
 | ------- | ------- | ------ |
-| `enable_tf32` | `true` | Sets `torch.set_float32_matmul_precision("high")`, and on CUDA also `torch.backends.cuda.matmul.allow_tf32 = True`. ~2x faster matmuls on Ampere+ GPUs (RTX 30/40 series, A100, H100) with minor precision loss. No effect outside CUDA. |
-| `cudnn_benchmark` | `true` | CUDA only. Autotunes cuDNN algorithm selection - fastest for a workflow with fixed input sizes, can add overhead when sizes vary run to run. |
-| `cudnn_deterministic` | `false` | CUDA only. Set `true` to trade speed for reproducible output given the same seed. |
+| `enable_tf32` | `true` | `torch.set_float32_matmul_precision("high")`, and on CUDA also `torch.backends.cuda.matmul.allow_tf32 = True`. ~2x faster matmuls on Ampere and newer (RTX 30/40, A100, H100) with minor precision loss |
+| `cudnn_benchmark` | `true` | CUDA only. Autotunes cuDNN algorithm selection: fastest for fixed input sizes, overhead when sizes vary run to run |
+| `cudnn_deterministic` | `false` | CUDA only. `true` trades speed for reproducible output given the same seed |
 
 ```json
 { "enable_tf32": true, "cudnn_benchmark": true, "cudnn_deterministic": false }
@@ -357,28 +325,26 @@ Device-level settings, read once at startup from `~/.diffusers_helper/settings.j
 
 ## Environment Defaults
 
-Set automatically at import unless already present in the environment (export your own value to override, or put it in a `.env` file in the directory dw starts from - [.env.example](../.env.example) lists these and the other knobs worth knowing: the Hub, diffusers, transformers and SDNQ):
+Set at import unless already in the environment. Export your own value to override, or put it in a `.env` file in the directory dw starts from; [.env.example](../.env.example) lists these and the other Hub, diffusers, transformers and SDNQ knobs.
 
 | Variable | Default | Effect |
 | -------- | ------- | ------ |
-| `PYTORCH_CUDA_ALLOC_CONF` | `expandable_segments:True` | Lets the CUDA allocator grow segments instead of fragmenting fixed-size ones. Multi-step workflows churn differently-shaped allocations (generate, upscale, interpolate); fragmentation is what OOMs a card that nominally has room. |
-| `HF_ENABLE_PARALLEL_LOADING` | `true` | Loads sharded checkpoints in parallel - faster cold starts. |
-| `PYTORCH_MPS_HIGH_WATERMARK_RATIO` | `0.0` | MPS only - use all available unified memory. |
-| `TORCHINDUCTOR_CACHE_DIR` | `$XDG_CACHE_HOME/dw/torchinductor` (`~/.cache/dw/torchinductor`) | Keeps `torch.compile`'s on-disk cache across reboots - Inductor's own default is under `/tmp`. A new worker still retraces, but a cache hit skips codegen and kernel builds, the bulk of a cold compile. Entries are per shape, so a new size or frame count compiles once more. Listed as `inductor` by `GET /api/caches` and cleared with `DELETE /api/caches/inductor`. Triton's cache is not set by dw and stays at its own default (`~/.triton/cache`) - see the compile notes above. |
+| `PYTORCH_CUDA_ALLOC_CONF` | `expandable_segments:True` | Lets the CUDA allocator grow segments instead of fragmenting fixed-size ones. Multi-step workflows churn differently-shaped allocations; fragmentation is what OOMs a card that nominally has room |
+| `HF_ENABLE_PARALLEL_LOADING` | `true`, `false` on macOS | Loads sharded checkpoints in parallel. Off on macOS: concurrent loader threads copying onto MPS segfaulted a quantized load |
+| `PYTORCH_MPS_HIGH_WATERMARK_RATIO` | `0.0` | MPS only: use all available unified memory |
+| `TORCHINDUCTOR_CACHE_DIR` | `$XDG_CACHE_HOME/dw/torchinductor` (`~/.cache/dw/torchinductor`) | Keeps `torch.compile`'s on-disk cache across reboots (Inductor's own default is under `/tmp`). A cache hit skips codegen and kernel builds, the bulk of a cold compile; entries are per shape. Listed as `inductor` by `GET /api/caches`, cleared with `DELETE /api/caches/inductor` |
 
-For faster model downloads, optionally `pip install hf_transfer` and set `HF_HUB_ENABLE_HF_TRANSFER=1`. Not enabled automatically - it bypasses the Python HTTP stack and breaks some proxy setups.
+For faster downloads, optionally `pip install hf_transfer` and set `HF_HUB_ENABLE_HF_TRANSFER=1`. It is not enabled automatically: it bypasses the Python HTTP stack and breaks some proxy setups.
 
 ## MPS Notes
 
 Apple Silicon has narrower acceleration support than CUDA:
 
-- No flash-attn, no Triton, no bitsandbytes - `attention_backend` is effectively CUDA-only; use `"native"`-family backends or leave it unset on MPS. Validate warns `attention_backend_unavailable` for a CUDA-only one ([Attention Backends](#attention-backends)). `compile` is skipped with a warning (inductor support on MPS is immature). A workflow that loads a bitsandbytes checkpoint (`flux2-dev`, `multi-image-reference`) or a TorchAO int4 config (`flux-torchao`) has no Mac path yet.
-- Workflows written for CUDA are adapted rather than refused: a `cuda` device becomes `mps`, SDNQ's `quantization_device` follows it and its quantized matmul is switched off (it runs through `torch._int_mm`, ~500x slower on MPS), group-offload CUDA streams are dropped, and `"offload": "sequential"` becomes `"model"`. Each change is logged as a warning.
-- Attention slicing is automatic; set `disable_attention_slicing` for SDXL-family models (head dim 64), where it is 2.4x slower in attention. It helps SD 1.5.
-- `float16` can produce NaN values on Apple Silicon - use `bfloat16`; dw only warns, it doesn't override the dtype for you.
-- `PYTORCH_MPS_HIGH_WATERMARK_RATIO` defaults to `0.0` (use all unified memory) unless already set in the environment.
-- Ops the MPS backend lacks: with `PYTORCH_ENABLE_MPS_FALLBACK=1` (set by the `fp4-fp8-for-torch-mps` package install.sh adds) they run on the CPU, and dw logs torch's warning when one does. Without it they raise.
-- Memory figures are real: allocated, driver-reserved, and Metal's recommended working set as the total. `vram_estimate` checks a Mac against that total, since the catalog's `cost` entries are CUDA cards; the per-voxel figures were calibrated on CUDA.
-- Cost estimates quoted on a Mac are the CUDA figures (`basis: "other_device"`) until the Mac's own runs build observed history - expect them to be optimistic.
-- Parallel checkpoint loading (`HF_ENABLE_PARALLEL_LOADING`) defaults to off on macOS - concurrent loader threads copying onto MPS segfaulted a quantized load. Set it explicitly to override.
-- Offloading has less benefit than on CUDA, since unified memory is already shared between CPU and GPU.
+- No flash-attn, Triton or bitsandbytes. Use a `native`-family `attention_backend` or leave it unset; validate warns `attention_backend_unavailable` for a CUDA-only one, and `sparse` runs dense. `compile` is skipped with a warning. A workflow that loads a bitsandbytes checkpoint (`flux2-dev`, `multi-image-reference`) or a TorchAO int4 config (`flux-torchao`) has no Mac path.
+- Workflows written for CUDA are adapted rather than refused, each change logged as a warning: a `cuda` device becomes `mps`, SDNQ's `quantization_device` and `return_device` follow it and its `use_quantized_matmul` is switched off (it runs through `torch._int_mm`, ~500x slower on MPS), group-offload CUDA streams are dropped, and `"offload": "sequential"` becomes `"model"`.
+- Attention slicing is automatic; see [Attention Slicing](#attention-slicing) for when to disable it.
+- `float16` can produce NaN values on Apple Silicon; use `bfloat16`. dw warns and does not override the dtype.
+- Ops the MPS backend lacks run on the CPU with `PYTORCH_ENABLE_MPS_FALLBACK=1` (set by the `fp4-fp8-for-torch-mps` package install.sh adds), and dw logs torch's warning when one does. Without it they raise.
+- Memory figures are real: allocated, driver-reserved, and Metal's recommended working set as the total. `vram_estimate` checks a Mac against that total; the per-voxel figures were calibrated on CUDA.
+- Cost estimates on a Mac are the CUDA figures (`basis: "other_device"`) until the Mac's own runs build observed history; expect them to be optimistic.
+- Offloading gains less than on CUDA, since unified memory is already shared between CPU and GPU.
diff --git a/docs/AGENT_LOOP.md b/docs/AGENT_LOOP.md
index a4583e0f..c3546660 100644
--- a/docs/AGENT_LOOP.md
+++ b/docs/AGENT_LOOP.md
@@ -1,95 +1,113 @@
 # Agent Loop
 
-Some of the Issues in this repo are worked by an automated implementer/tester
-agent loop, not a human. It lives in a separate repo
-([`dkackman/iterate`](https://github.com/dkackman/iterate) — private) that
-drives two Claude Code agents in strict alternation against this repo's
-Issues. Nothing about running the loop lives here; this page exists so that
-anyone who opens or comments on one of its Issues — a human, or another
-agent joining in — knows how to act on it correctly.
+Some Issues in this repo are worked by an automated agent loop, not a human.
+The harness is [`dkackman/harnest`](https://github.com/dkackman/harnest); its
+[ticket protocol](https://github.com/dkackman/harnest/blob/main/docs/harness/ticket-protocol.md)
+is the full rule set this page summarizes, and `lib/classify.jq` there is the
+authoritative map from labels to the session that acts next. Nothing about
+running the loop lives here. This page is for anyone, human or agent, who
+opens or comments on one of its Issues.
 
 ## The two roles
 
-- **Implementer** — has this repo checked out and SSH access to the box
-  running the MCP server. Reproduces, fixes, deploys, and hands the ticket
+- **Implementer**: has this repo checked out and SSH access to the box running
+  the MCP server. Triages, reproduces, fixes, deploys and hands the ticket
   back. Never verifies its own fix.
-- **Tester** — talks to the MCP server only as a protocol client (MCP tool
-  calls), no source checkout, no shell/SSH access to the server box. Its job
-  is independent verification: it re-runs the original repro over MCP and
-  either confirms the fix or bounces the ticket back.
+- **Tester**: talks to the MCP server only as a protocol client, with no
+  source checkout and no shell on the server box. It re-runs the original
+  repro over MCP and either confirms the fix or bounces the ticket.
 
-That asymmetry — the only role that can mark an issue verified is the one
-with no ability to patch around a bug — is the entire point of the loop. A
-human or third agent joining in should preserve it: don't fix and verify the
-same ticket yourself.
+The only role that can mark an issue verified is the one that cannot patch
+around a bug. Preserve that: do not fix and verify the same ticket yourself.
 
-A third, standalone **regression agent** periodically runs a growing suite
-of scripted MCP calls (`regression-suite-*.md` in the `iterate` repo) against
-the live server and files/comments on Issues for anything that regresses. It
-doesn't participate in the implementer/tester handoff.
+Other sessions in the harness:
+
+- **Lead** (`owner:lead`): designs a feature with Don, turns an `idea` into a
+  feature or a single fix, or closes it.
+- **Docs reviewer**: read-only; closes a `docs-review` ticket.
+- **Regression agent**: runs the scripted MCP suites (`regression-suite-*.md`
+  in harnest) against the live server and files `regression` and
+  `performance` issues. It takes no part in the implementer/tester hand-off.
 
 ## Outside filings
 
-The loop runs as one GitHub login (the repo owner's) and must not act
-unattended on text filed by anyone else in this public repo. An issue filed
-under a different login — including a `field-report` from someone else's
-session — is relabelled `owner:don` + `status:needs-approval` before either
-role works it, and is handed to the loop, or not, only after the maintainer
-reviews it.
+The loop runs as the repo owner's GitHub login and must not act unattended on
+text anyone else filed in this public repo. An issue from another login,
+including a `field-report` from someone else's session, is relabelled
+`owner:don` + `status:needs-approval` before any role works it, and reaches
+the loop only if the maintainer hands it back.
+
+**Security findings are never public issues.** A boundary that did not hold
+(a gate, a path, a URL, a disclosure, a destructive call without
+acknowledgement) goes to a draft GitHub security advisory. No agent may add
+the `security` label.
 
 ## Reading a ticket
 
-Tickets use the **MCP agent-loop ticket** issue template. Two label
-families carry all the state — check both before acting:
-
-- **`owner:*`** — exactly one of `owner:implementer` / `owner:tester` /
-  `owner:don` at a time: whoever is expected to act on it next. If you're
-  not that owner, leave the issue alone beyond reading it (or the specific
-  handoff comment/label a role prompt allows).
-- **`status:*`** — where the ticket is in its lifecycle:
-  - *(no status label)* — open, ready for the implementer to reproduce.
-  - `status:fixed-pending-verify` — implementer has fixed and deployed;
-    waiting on the tester to re-run the repro over MCP.
-  - `status:verified` — tester confirmed the fix over MCP; issue is closed
-    as `completed`.
-  - `status:needs-info` — a question bounce; whoever is asked needs to
-    answer before work continues.
-  - `status:needs-approval` (+ `owner:don`) — parked for the human. The
-    implementer uses this for anything beyond a rename-level change
-    (engine behavior, breaking syntax). Neither agent touches a parked
-    issue.
-
-Two built-in GitHub labels close a ticket without a fix:
-
-- **`wontfix`** — the implementer's call, with a reason in a comment; issue
-  closed as `not planned`. The tester may reopen once with new evidence; a
-  second `wontfix` is final.
-- **`duplicate`** — closed as `not planned`, with a comment naming the
-  issue it duplicates (`duplicate of #NN`). A closed issue is still
-  canonical for duplicate detection — the implementer checks
-  `gh issue list --state all` before starting work, not just open issues.
-
-`breaking-change` marks an Issue whose fix changed the MCP interface, so the
-tester adjusts its calls instead of filing the change as a new bug.
+Tickets use the **MCP agent-loop ticket** issue template (ideas use **MCP
+agent-loop idea**). Two label families carry the state; check both before
+acting.
+
+**`owner:*`** is exactly one of `owner:implementer`, `owner:tester`,
+`owner:lead` or `owner:don`: whoever acts next. If it is not you, leave the
+issue alone beyond reading it. The owner label is the only signal: a status
+addressed to Don is stale once he swaps the owner back.
+
+**`status:*`** is where the ticket is:
+
+| Label | Meaning |
+| --- | --- |
+| *(none)* | Open, ready for the implementer to reproduce |
+| `status:fixed-pending-verify` | Fixed and deployed; the tester re-runs the repro over MCP |
+| `status:verified` | The tester confirmed the fix over MCP; closed as `completed` |
+| `status:reviewed` | A `docs-review` fix closed by the docs reviewer (never `status:verified`) |
+| `status:needs-info` | A question bounce; whoever is asked answers before work continues |
+| `status:needs-approval` | With `owner:don`: parked for the human, after a written proposal. Required for engine or syntax changes and anything breaking beyond a rename. Neither agent touches it |
+| `status:plan-review` | A feature plan is posted and waiting for Don |
+| `status:plan-approved` | Don approved the plan. Only Don adds it |
+| `status:needs-spec` | The tester writes acceptance cases for the plan's stages |
+
+Labels that close a ticket without a fix:
+
+- **`wontfix`**: the implementer's call, with a reason in a comment; closed as
+  `not planned`. The tester may reopen once with new evidence; a second
+  `wontfix` is final.
+- **`duplicate`**: closed as `not planned` with `duplicate of #NN` in a
+  comment. Closed issues count for duplicate detection
+  (`gh issue list --state all`). A verified issue that reproduces again is not
+  a duplicate: it becomes a new `regression` issue (`regression of #NN`), and
+  nobody reopens the old one.
+
+Other labels:
+
+| Label | Meaning |
+| --- | --- |
+| `docs-review` | A fix to text neither the server nor the plugin serves; the docs reviewer verifies it instead of the tester |
+| `breaking-change` | The fix changed the MCP interface; the tester adjusts its calls rather than filing a new bug |
+| `field-report` | Filed from Don's own dw sessions, not the harness |
+| `regression`, `performance` | Filed by the regression agent |
+| `idea` | An idea for the lead to assess |
+| `feature`, `stage` | Work designed with Don and built in stages; a `stage` is a child issue of one feature |
+| `lane:batch` | A plan review with no question of a kind Don decides, approved in a batch |
+| `parked` | Deferred: on `owner:lead`, until every blocking issue closes; on `owner:don`, Don's |
+| `release`, `release-blocker` | While an open `release` issue exists, only `release-blocker` issues move |
+| `stabilization`, `arch-approved` | dw's stabilization freeze (in force while `docs/stabilization/FREEZE` exists on `develop`): work parked for it, and new surface Don approved during it |
+| `arch-review` | A hand-off waiting on the architecture reviewer, who checks it against `docs/ARCHITECTURE.md` and passes or bounces it |
+| `consolidation` | Filed weekly from `scripts/arch_report.py`; Don plans it (to `owner:lead`) or closes it |
+| `backend:shared`, `backend:cuda`, `backend:mps` | What the bug is about |
+| `target:lem`, `target:mini-ai`, `target:minid`, `target:local` | Which server's loop claims it; a non-lem target is for Don to triage |
+| `verified-on:mps` | Verified on the Mac only; CUDA has not re-verified the fix |
+| `priority:2`, `priority:3` | Backlog rank |
 
 ## If you want to participate
 
-Whether you're a human or another agent:
-
-- Only touch an issue that's currently owned by you (or, for a human, one
-  parked with `owner:don`).
-- When you hand a ticket to the next owner, swap the `owner:*` label and
-  say what you did in a comment — the next actor has no memory of this
-  session, only the issue thread.
-- Keep implementer and tester roles separate. If you're fixing code, don't
-  also close the issue as verified — that requires an independent MCP call
-  from someone who didn't write the patch.
-- Reference the issue number in any commit that fixes it
-  (`fix(mcp): #42 - ...`), and work on a branch merged to `develop`, never
-  `master`.
-- Only the tester (or an equivalently independent verifier) closes an issue
-  as `completed`/`status:verified`, and only after a real MCP call
-  reproduces the fix — not by reading the diff.
-
-See the `iterate` repo's `CLAUDE.md` for the full protocol this is
-summarized from, including how the automated loop itself is run.
+- Only touch an issue owned by you (or, for a human, one parked with
+  `owner:don`).
+- To hand a ticket on, swap the `owner:*` label and say what you did in a
+  comment. The next actor has no memory of your session, only the thread.
+- Keep implementer and tester separate. Only the tester (or an equally
+  independent verifier) closes an issue as `completed` with
+  `status:verified`, and only after a real MCP call shows the fix, not by
+  reading the diff. The one exception is `docs-review`.
+- Reference the issue in the commit that fixes it, with a scope
+  (`fix(mcp): #42 - ...`), on a branch merged to `develop`, never `master`.
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 14d40edb..7ca5e001 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -69,7 +69,7 @@ one to open.
 | --- | --- | --- | --- |
 | Run directories | `dw/runs.py`: `open_run`, `workflow_identity`, `new_run_id` | Each execution writes into `<output_dir>/<identity>/<run id>/` with a `manifest.json`, unless the flat layout is chosen (the test pins the flat-layout exception). | `tests/test_runs.py::TestRunDirectories::test_the_flat_layout_writes_where_it_always_did` |
 | A file's role (final / intermediate / input) | `dw/roles.py` | Decided per step: result.role, then a final/intermediate subfolder, then data flow (a step a media-writing or unsaved consumer reads is intermediate). Engine records it at run end, with `role_declared` where result.role stated it; a run that predates roles recorded none, so the listing falls back on the file's own `final`/`intermediate` subfolder, then the demotion below, and a file neither places has none. At read time the gallery (`GET /api/gallery`, its `role=` filter, the metadata route) also demotes a file a later run of the same workspace read - an `output:` input (an unpinned `latest` is the newest run started before the reader) or an `asset:` keep_output kept from it - unless its role was declared (`consumed_outputs`, `listed_role`, once per listing; no manifest is rewritten). | `tests/test_roles.py`, `tests/test_roles.py::test_a_file_a_later_run_read_is_consumed`, `tests/test_roles.py::test_listed_role`, `tests/test_roles.py::test_listed_role_of_a_run_that_recorded_none`, `tests/test_gallery_roles.py::test_older_run_reads_its_intermediate_subfolder_as_intermediate`, `tests/test_gallery_roles.py::test_a_pre_roles_file_a_later_run_read_lists_as_intermediate`, `tests/test_gallery_roles.py::test_metadata_agrees_with_the_listing_for_an_older_run`, `tests/test_gallery_roles.py::test_a_file_a_later_run_read_lists_as_intermediate`, `tests/test_gallery_roles.py::test_a_declared_final_stays_final` |
-| Notes on runs, files, assets, workspaces | dw/notes.py | notes.json beside what it describes (run dir or asset dir; a file's own note else the run's), workspace.json at a workspace root; only the server writes them, never the engine; ≤4000 chars, plain text. | tests/test_notes.py |
+| Notes on runs, files, assets, workspaces | `dw/notes.py` | A note is `notes.json` beside what it describes (run dir or asset dir; a file's own note, else the run's), or `workspace.json` at a workspace root. Only the server writes them, never the engine; at most 4000 characters, plain text. | `tests/test_notes.py` |
 | Run versions | `dw/runs.py`: `open_run`, `run_versions`, `record_run_versions`, `copy_runs_in` | A run takes its number once, when it opens (or, copied in from another workspace, when `copy_runs_in` places it), so deleting a middle run leaves a gap rather than renumbering. | `tests/test_runs.py::TestRunVersions::test_a_deleted_middle_run_leaves_a_gap_rather_than_renumbering`, `tests/test_runs.py::TestRunVersions::test_runs_started_in_the_same_second_still_number_upward` |
 | Run-version surfaces | `dw/runs.py`: `run_versions`, `dw/server/routes/gallery.py`, `dw/server/outputs.py`, `dw_mcp/tools_catalog.py`: `list_gallery`, `ui/src/lib/pages/GalleryPage.svelte` | Only `dw/runs.py` computes the number, and every other module reads it back. | `tests/test_runs.py::TestRunVersions::test_an_output_reference_can_name_a_run_by_its_version` |
 | What the server says about an output file | `dw/server/outputs.py`: `output_annotations` | The gallery listing (`dw/server/routes/gallery.py`) and the metadata route (`dw/server/routes/media.py`) describe an output file's `role`, `note`, `note_source`, `run_note`, `starred` and `version` through `output_annotations`, and neither builds those fields on its own. | `tests/test_gallery_roles.py::test_metadata_agrees_with_the_listing_on_a_starred_noted_demoted_file` |
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout and regional edit | Nine modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`, and the mask-to-rows arithmetic `binarized`, `regenerate_cells` and `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers` |
+| H3 audio hold, refine, tiled refine, guide layout and regional edit | Eleven modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
@@ -212,5 +212,5 @@ one to open.
 | UI polling | `ui/src/lib/poll.ts`: `poll`, `sleep` | A page that refreshes on a timer starts it through `poll` and stops it with the function `poll` returns. | `ui/src/lib/poll.test.ts` |
 | Workspace state in the UI | `ui/src/lib/workspaceState.svelte.ts`: `workspace` | The current workspace is one `$state`; `api.ts` reads it from here, so the request layer imports no page state. | `import_cycles` in `ui/scripts/arch-metrics.mjs` |
 | The UI's response contract | `dw/server/api_models.py` (the models; it re-exports `dw/server/api_models_base.py`: `ApiModel`, `send_rejected_responses`, and the library routes' models in `dw/server/api_models_library.py`); `scripts/dump_openapi.py` | Every JSON route `api.ts` calls declares a response model, and `types.ts` re-exports the types generated from it (`ui/src/lib/generated/`). The payload does not change: an absent key stays absent (`response_model_exclude_unset`), a sometimes-sent key is `sometimes()`. Runtime is lenient - an undeclared key passes and a rejected response is logged and sent as built; tests, the e2e server and the dump run strict (`DW_STRICT_RESPONSES=1`). The document is generated under the FastAPI and Pydantic `constraints-openapi.txt` pins: CI installs them, and the dump refuses to write under others. | `tests/test_api_contract.py`, `tests/test_ui_twins.py::test_every_json_route_the_ui_calls_declares_its_response`, CI's "Response contract" step |
-| Where `--live` appears | `ui/src/app.css` (its header) | The state colour marks machine state only, in the files `ui/scripts/design-rules.test.ts` lists. | `ui/scripts/design-rules.test.ts` |
+| Where `--live` appears | `ui/src/app.css` (its header) | `--live` marks machine state only and `--select` the user's place only (focus ring, sidebar), each in the files `ui/scripts/design-rules.test.ts` lists. | `ui/scripts/design-rules.test.ts` |
 | UI architecture ratchet | `ui/scripts/arch-metrics.mjs` | No metric rises above `docs/stabilization/ui/baseline.json`. | the `ui` job in `.github/workflows/ci.yml`; `npm run preflight` |
diff --git a/docs/ATTRIBUTIONS.md b/docs/ATTRIBUTIONS.md
index c2d28839..594bffe3 100644
--- a/docs/ATTRIBUTIONS.md
+++ b/docs/ATTRIBUTIONS.md
@@ -3,14 +3,14 @@
 diffusers-workflow is Apache-2.0. This page credits the work it builds on that
 a dependency list does not show: code that was copied in, and other projects
 whose ideas were reimplemented here. Python dependencies are in
-[requirements.txt](../requirements.txt) and [pyproject.toml](../pyproject.toml).
+[pyproject.toml](../pyproject.toml).
 Model and LoRA weights keep their own licences, and each catalog entry
 ([workflows/models/](../workflows/models/), [loras/](../loras/)) names the
 repository it downloads from.
 
 ## Code included in this repository
 
-Each file keeps its upstream licence notice in its header.
+Each copied or ported file keeps its upstream licence notice in its header.
 
 | File | Source | Licence |
 |---|---|---|
diff --git a/docs/DEPENDENCIES.md b/docs/DEPENDENCIES.md
index ca9ab4cc..48d4d39b 100644
--- a/docs/DEPENDENCIES.md
+++ b/docs/DEPENDENCIES.md
@@ -16,7 +16,7 @@ source ./activate
 .\venv\scripts\activate
 ```
 
-The install scripts handle everything: Python version detection (3.12-3.14), virtual environment creation, PyTorch, diffusers, and all dependencies.
+The install scripts need Python 3.12-3.14. Each deletes and recreates `venv/`, installs PyTorch for the detected GPU, then dw with the `server` and `dev` extras (`pip install -e ".[server,dev]"`), diffusers from git HEAD, the platform extras below, and the web UI's npm packages when `npm` is on the `PATH`.
 
 ### Docker (Linux, NVIDIA)
 
@@ -26,55 +26,53 @@ workspace and caches with a native install on the same host:
 
 ### Manual Installation
 
-If you prefer manual control:
-
 ```bash
 python3 -m venv venv
 source venv/bin/activate
 
-pip install torch torchvision torchaudio  # add --index-url https://download.pytorch.org/whl/cu130 for CUDA on Linux
-pip install -r requirements.txt
-pip install git+https://github.com/huggingface/diffusers
+pip install torch torchvision torchaudio  # add --index-url https://download.pytorch.org/whl/cu130 for CUDA 13 on Linux
+pip install -r requirements.txt           # -e .[server]; requirements-test.txt is -e .[dev]
+pip install --force-reinstall --no-deps git+https://github.com/huggingface/diffusers
+pip install diffusers                     # adds any dependency the git commit needs
 ```
 
+dw does not import torchaudio, but diffusers' MiniMax H3 blocks need it to resample an audio reference, so install it from the same index as torch.
+
 ## Where the Dependency List Lives
 
-`pyproject.toml` is the single source: its `dependencies` list (plus the
-`server` and `dev` extras) is what the published wheel declares, what
-`requirements.txt` and `requirements-test.txt` resolve (they are one-line
-pointers, `-e .[server]` and `-e .[dev]`), and what the install scripts
-and CI install. To add or change a dependency, edit pyproject.toml -
-every install path picks it up.
+`pyproject.toml` is the single source. Its `dependencies` list and its `server`, `mcp` and `dev` extras are what the wheel declares, what `requirements.txt` and `requirements-test.txt` point at, and what the install scripts and CI install. To add or change a dependency, edit `pyproject.toml`. Refresh the version floors from a working venv with `python scripts/refresh_dep_floors.py`.
 
-Things pyproject can't express stay in the scripts: the PyTorch CUDA
-index, diffusers from git HEAD, and the macOS/Windows-specific extras
-below. bitsandbytes carries a `sys_platform == 'linux'` marker in
-pyproject; install.ps1 installs it explicitly on Windows.
+The scripts handle what `pyproject.toml` cannot express: the PyTorch index, diffusers from git HEAD, and the platform extras below.
 
-## Platform-Specific Dependencies
+`constraints-openapi.txt` pins the FastAPI and Pydantic that `ui/src/lib/generated/openapi.json` is generated under. CI installs them with `pip -c`, and `scripts/dump_openapi.py` refuses to write under any other versions.
 
-**All platforms (core ML):** peft, transformers, accelerate, safetensors, controlnet_aux, sentencepiece, torchsde, torchao, optimum-quanto, gguf, kornia, ftfy, sdnq, spandrel, facexlib (spandrel + facexlib back the `upscale` and `restore_faces` tasks). On first use, `restore_faces` has facexlib download its face detection and parsing weights (about 110 MB) from facexlib's own GitHub releases into the facexlib package directory. It is the one model download that bypasses the Hugging Face cache and dw's download manager; a validate's `plan.downloads_required` lists the two weights as URL entries while they are missing from that directory.
+## Notable Dependencies
 
-**All platforms (sparse attention):** comfy-kitchen (Sol-Attn sparse attention for MiniMax H3's generated-video rows, #931). Its compiled CUDA/HIP kernels ship for Linux and Windows; elsewhere it installs an eager-only build.
+The full list is in `pyproject.toml`. These are the ones with behaviour worth knowing:
 
-**All platforms (utilities):** fastapi, uvicorn (the `dw.serve` HTTP server and web UI), av, aiohttp, matplotlib, opencv-python-headless, concurrent-log-handler, qrcode, protobuf, imageio, imageio-ffmpeg, beautifulsoup4, soundfile, jsonschema, black, python-dotenv
+- **spandrel, facexlib** back the `upscale` and `restore_faces` tasks. On first use, `restore_faces` has facexlib download its face detection and parsing weights (about 110 MB) from facexlib's GitHub releases into the facexlib package directory. It is the one model download that bypasses the Hugging Face cache and dw's download manager; while the weights are missing, a validate's `plan.downloads_required` lists them as URL entries.
+- **comfy-kitchen** (`<0.3`) is Sol-Attn sparse attention for MiniMax H3's generated-video rows (#931). Its compiled CUDA/HIP kernels ship for Linux and Windows; elsewhere it installs an eager-only build.
+- **kernels** fetches Hub-hosted compiled kernels at run time, for example NATTEN's `na3d` for LTX-2.5's diffusion decoder.
+- **speechbrain, demucs** back `generate_speech`'s `speaker_embedding` and the `attribute_voices` task. Both are imported lazily, and their weights download only when a call uses them.
+- **piexif** embeds generation metadata as EXIF `UserComment` when a step's `result.embed_metadata` is `true` and the content type is JPEG or WebP. PNG embedding uses Pillow's `PngInfo`.
+- **fastapi, uvicorn** (the `server` extra) run `dw.serve` and the web UI. **mcp** (the `mcp` extra, also in `dev`) runs `dw_mcp`.
 
-**Linux (CUDA):** bitsandbytes
+## Platform-Specific Dependencies
 
-**Windows (CUDA):** bitsandbytes, kernels
+**Linux:** bitsandbytes, through its `sys_platform == 'linux'` marker in `pyproject.toml`.
 
-**macOS (MPS):** fp4-fp8-for-torch-mps (FP8/FP4 dtypes for Metal; autoloads on every torch import, overrides mm/linear/copy on the MPS dispatch key and sets PYTORCH_ENABLE_MPS_FALLBACK=1), fluidtop (a GPU/power TUI; needs sudo)
+**Windows:** `install.ps1` installs bitsandbytes and kernels explicitly.
+
+**macOS (MPS):** `install.sh` adds fp4-fp8-for-torch-mps (FP8/FP4 dtypes for Metal; autoloads on every torch import, overrides mm/linear/copy on the MPS dispatch key and sets `PYTORCH_ENABLE_MPS_FALLBACK=1`) and fluidtop (a GPU/power TUI; needs sudo). bitsandbytes and flash_attn are not available on macOS.
 
 ## Optional
 
-**flash_attn** — Improved attention performance on CUDA. Requires the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit):
+**flash_attn** speeds up attention on CUDA, and some LLM workflows (for example Phi mini-instruct) require it. It needs the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit):
 
 ```bash
 pip install flash_attn
 ```
 
-**piexif** (installed by default) — Embeds generation metadata as EXIF `UserComment` when a step's `result.embed_metadata` is `true` and the content type is JPEG/WebP (PNG embedding uses Pillow's `PngInfo` and needs nothing extra). Without it, saving falls back to a logged warning and no embedded metadata.
-
 ## Specifying Python Version
 
 ```bash
@@ -82,18 +80,20 @@ export INSTALL_PYTHON_VERSION=3.13
 bash ./install.sh
 ```
 
+Without it, `install.sh` takes the newest of `python3.14`, `python3.13` and `python3.12` on the `PATH`. `install.ps1` always runs plain `python`; `INSTALL_PYTHON_VERSION` only has to match that interpreter's version.
+
 ## Troubleshooting
 
-**Hugging Face authentication (401/403 downloading a model):** Most workflows under `workflows/` (Flux, LTX-2, MiniMax...) point at **gated** models on the Hub — repos that require the owner to approve your account before you can download them. A run against one of these fails with an actionable error naming the repo and `hf auth login` (mapped from the Hub's 401/403 in `load_component()`, `dw/pipeline_processors/components.py`) — request access on the model's page (e.g. [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)) and then log in once, locally:
+**Hugging Face authentication (401/403 downloading a model):** Most workflows under `workflows/` (Flux, LTX-2, MiniMax...) use **gated** models, which need the repo owner to approve your account. A run against one fails with an error naming the repo and `hf auth login` (mapped from the Hub's 401/403 in `load_component()`, `dw/pipeline_processors/components.py`). Request access on the model's page (e.g. [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)), then log in once:
 
 ```bash
 hf auth login
 ```
 
-`workflows/templates/text-to-image.json` uses an ungated model and needs no login - start there if you just want to confirm the install works.
+`workflows/templates/text-to-image.json` uses an ungated model and needs no login, so use it to confirm an install works.
 
-**Package conflicts:** Re-run the install script — it recreates the venv from scratch.
+**Package conflicts:** Re-run the install script; it recreates the venv from scratch.
 
-**CUDA not detected:** `install.sh` probes for a working `nvidia-smi` on Linux and installs the CUDA build of torch (cu130) only when it finds one; otherwise (or on a manual install with plain `pip install torch`) you get PyPI's CPU-only Linux wheel. Verify with `python -c "import torch; print(torch.cuda.is_available())"`.
+**CUDA not detected:** The install scripts run `nvidia-smi` and read the CUDA version the driver supports. A CUDA 13 driver (>= 580) gets torch from the cu130 index, an older driver gets PyPI's wheel with its bundled CUDA 12 runtime, and no working `nvidia-smi` gets the CPU-only build. A manual `pip install torch` on Linux gets PyPI's wheel. Verify with `python -c "import torch; print(torch.cuda.is_available())"`.
 
 **MPS not detected:** Requires Apple Silicon. Verify with `python -c "import torch; print(torch.backends.mps.is_available())"`.
diff --git a/docs/DOCKER.md b/docs/DOCKER.md
index 6f132794..5e068ad0 100644
--- a/docs/DOCKER.md
+++ b/docs/DOCKER.md
@@ -31,12 +31,11 @@ docker compose -f docker/compose.yaml exec dw python /opt/dw/entrypoint.py login
 
 To use the published image instead of building, uncomment `DW_IMAGE` in
 `docker/.env` and run `docker compose -f docker/compose.yaml pull` before
-`up -d` (without `--build`). The first build takes a while, because torch
-alone is several GB. The first
-start then installs diffusers from GitHub main into its volume (see below),
-which takes a few more minutes before the server answers. Then `login-url`
-prints `http://localhost:8765/?token=...`. Open that link and the UI saves
-the token.
+`up -d` (without `--build`). The first build takes a while (torch alone is
+several GB), and the first start installs diffusers from GitHub main into
+its volume (see below), a few more minutes before the server answers.
+`login-url` prints `http://localhost:<DW_HOST_PORT>/?token=...`; opening it
+saves the token in the UI.
 
 ## Where the data lives: shared with a native install
 
@@ -75,8 +74,9 @@ bash docker/switch.sh native    # stop the container, start the unit
 bash docker/switch.sh status
 ```
 
-The script assumes the user unit from `scripts/dw-serve.service`. For the
-system unit in `contrib/systemd/`, set `DW_SYSTEMCTL="sudo systemctl"`.
+The script assumes the user unit from `scripts/dw-serve.service`
+(`DW_SYSTEMD_UNIT` names another unit). For the system unit in
+`contrib/systemd/`, set `DW_SYSTEMCTL="sudo systemctl"`.
 To keep the token your MCP clients already use, set `DW_API_TOKEN` in
 `docker/.env` to the one the unit passes.
 
@@ -94,16 +94,18 @@ only diffusers and any dependency a new commit needs that the image lacks.
 
 - **The first start** installs main HEAD (or `DW_DIFFUSERS_REF`; see below).
 - **A restart changes nothing.** diffusers moves only when you ask it to.
-- **Update** from the web UI's Models page, as on a native install, or:
+- **Update** to HEAD from the web UI's Models page, as on a native install,
+  or to HEAD, a commit or the release floor with
   `docker compose -f docker/compose.yaml exec dw python /opt/dw/entrypoint.py update-diffusers [--commit SHA | --revert]`,
-  then restart the container.
+  then restart the container (a running job keeps the old import).
 - **A rebuilt image** (a new dw) rebuilds the overlay and reinstalls the
   diffusers commit you had, not HEAD.
 - **If the overlay doesn't import** (a broken HEAD, or no network on the
   first start), the server starts on the copy of diffusers built into the
-  image, and says so in the log. Pin a working commit from the Models page,
-  then restart. An update made in this state goes into the container
-  rather than the volume, so it's lost when the container is re-created.
+  image and says so in the log. Pin a working commit with `update-diffusers
+  --commit SHA` (or `--revert`), then restart. An update from the Models
+  page in this state goes into the container rather than the volume, so it
+  is lost when the container is re-created.
 
 `DW_DIFFUSERS_REF` in `docker/.env`: `head` (the default), a commit hash
 (the overlay is held to it), or `image` (no overlay; use the copy built
@@ -112,19 +114,18 @@ into the image).
 ## The API token
 
 Inside the container the server listens on every interface, and it can't
-tell how the port is published. So it always gets a token: `DW_API_TOKEN`
+tell how the port is published, so it always has a token: `DW_API_TOKEN`
 if you set one, otherwise one generated on first start and saved to
 `DIFFUSERS_HELPER_ROOT/api-token`. The token is never written to the log,
-so `docker logs` output is safe to paste into an issue: `login-url` prints
-the link when you ask for it.
-`DW_INSECURE_NO_TOKEN=1` turns this off; only do that with the port
-published on localhost (`DW_BIND=127.0.0.1`, the default).
+so `docker logs` output is safe to paste into an issue; `login-url` prints
+it on request. `DW_INSECURE_NO_TOKEN=1` turns this off; do that only with
+the port published on localhost (`DW_BIND=127.0.0.1`, the default).
 
 To reach the server from your LAN, set `DW_BIND=0.0.0.0`, then run `up -d`
-again to re-create the container. Clients connect the way
-[REMOTE.md](REMOTE.md) describes. A box that switches from a systemd unit
-bound to `0.0.0.0` needs this, or after the switch the server answers only
-on the box itself.
+again to re-create the container; clients connect as
+[REMOTE.md](REMOTE.md) describes. A box switching from a systemd unit bound
+to `0.0.0.0` needs this, or the container answers only on the box itself.
+`DW_HOST_PORT` (default 8765) is the host port it is published on.
 
 ## Options
 
@@ -132,6 +133,8 @@ on the box itself.
 `--mcp --devices cuda:0,cuda:1` for one worker per card. The entrypoint adds
 `--host 0.0.0.0`, `--port 8765` and the examples catalog unless you give
 them. `HF_TOKEN` is passed through for gated models.
+`DW_MCP_MAX_WAIT_SECONDS` (written as 1800 by `init-env.sh`, the systemd
+unit's value) caps how long an MCP `wait_for_job` may block (55 s when unset).
 
 Build arguments (`docker compose build --build-arg ...`): `TORCH_INDEX`
 (default the cu130 index; `https://pypi.org/simple` gives the CUDA 12 wheel
diff --git a/docs/IP_ADAPTER.md b/docs/IP_ADAPTER.md
index 5524fce7..195b5155 100644
--- a/docs/IP_ADAPTER.md
+++ b/docs/IP_ADAPTER.md
@@ -1,18 +1,15 @@
 # IP-Adapter
 
-IP-Adapter enables image-prompt conditioning — use a reference image to influence the style or content of generated images alongside a text prompt. It works with any pipeline whose `load_ip_adapter()` diffusers supports — Flux, Stable Diffusion 1.5, SDXL, SD 3.5, and others.
+IP-Adapter conditions a generation on a reference image alongside the text prompt, to carry its style or content. It works on any pipeline that has diffusers' `load_ip_adapter()`: Flux, Stable Diffusion 1.5, SDXL, SD 3.5 and others.
 
 ## Usage
 
-Add an `ip_adapter` block to the pipeline definition and pass `ip_adapter_image` in arguments:
+Add an `ip_adapter` block to the pipeline, a sibling of `configuration`, and pass the image as the `ip_adapter_image` argument:
 
 ```json
 {
     "pipeline": {
-        "configuration": {
-            "component_type": "FluxPipeline",
-            "offload": "sequential"
-        },
+        "configuration": { "component_type": "FluxPipeline" },
         "from_pretrained_arguments": {
             "model_name": "black-forest-labs/FLUX.1-dev",
             "torch_dtype": "torch.bfloat16"
@@ -33,20 +30,22 @@ Add an `ip_adapter` block to the pipeline definition and pass `ip_adapter_image`
 }
 ```
 
+FLUX.1 dev in bf16 needs a placement to fit a 24 GB card; [ip-adapter.json](../workflows/templates/ip-adapter.json) uses the streamed group offload from [RECIPES_24GB.md](RECIPES_24GB.md#flux-dev-12b).
+
 ## Properties
 
 | Property | Required | Description |
 | -------- | -------- | ----------- |
-| `model_name` | Yes | HuggingFace Hub repo ID for the IP-Adapter weights |
+| `model_name` | Yes | Hugging Face Hub repo id of the IP-Adapter weights |
 | `weight_name` | No | Specific weight file in the repo |
 | `subfolder` | No | Subfolder within the repo |
-| `scale` | No | Adapter strength (default: 1.0). Lower = less influence from reference image |
+| `scale` | No | Adapter strength, set with `set_ip_adapter_scale()` after the load. Unset keeps diffusers' default of 1.0. Lower = less influence from the reference image |
 
-Any other property (e.g. `revision`) is forwarded as-is to the underlying `load_ip_adapter()` call.
+Any other property (e.g. `revision`) is passed to `load_ip_adapter()` unchanged.
 
 ## Models Without a Built-in Image Encoder
 
-Some base models (e.g. Stable Diffusion 3.5) don't ship a default image encoder, so diffusers needs one configured explicitly. Add `image_encoder` and `feature_extractor` components alongside `ip_adapter`, the same way any other pipeline component is configured:
+Some base models (Stable Diffusion 3.5, for one) ship no image encoder, so diffusers needs one configured. Add `image_encoder` and `feature_extractor` component blocks beside `ip_adapter`, configured like any other pipeline component:
 
 ```json
 {
@@ -77,27 +76,15 @@ Some base models (e.g. Stable Diffusion 3.5) don't ship a default image encoder,
 }
 ```
 
-See [ip-adapter.json](../workflows/templates/ip-adapter.json) for the full workflow.
-
 ## Image Argument
 
-The `ip_adapter_image` uses the standard image loading format:
-
-```json
-"ip_adapter_image": {
-    "location": "https://example.com/image.jpg"
-}
-```
+`ip_adapter_image` loads like any `*_image` argument ([Media Arguments](WORKFLOW_GUIDE.md#media-arguments), [References](WORKFLOW_GUIDE.md#references)): a URL, a path relative to the workflow file, an `asset:` name on the server, or a `{"location": ...}` dict of any of these. Only `location` is read from the dict; the image is not resized.
 
 ```json
-"ip_adapter_image": {
-    "location": "./local/reference.png",
-    "width": 512,
-    "height": 512
-}
+"ip_adapter_image": "asset:reference.png"
 ```
 
-Can also reference a previous step's output:
+It can also take an earlier step's output:
 
 ```json
 "ip_adapter_image": "previous_result:preprocessing_step"
@@ -105,5 +92,4 @@ Can also reference a previous step's output:
 
 ## Examples
 
-- [ip-adapter.json](../workflows/templates/ip-adapter.json) — Flux with IP-Adapter for style transfer
-- [ip-adapter.json](../workflows/templates/ip-adapter.json) — FLUX.1 dev with an IP-Adapter, and the face-model variant in its description
+- [ip-adapter.json](../workflows/templates/ip-adapter.json): FLUX.1 dev with `XLabs-AI/flux-ip-adapter`. Its description gives the face-model variant, which keeps a person's face across a new scene: `h94/IP-Adapter`, subfolder `models`, weight `ip-adapter-full-face_sd15.bin`, scale 0.5, on a `StableDiffusionPipeline`.
diff --git a/docs/LORAS.md b/docs/LORAS.md
index 5aa164c0..b9eb07df 100644
--- a/docs/LORAS.md
+++ b/docs/LORAS.md
@@ -1,6 +1,6 @@
 # LoRA Support
 
-LoRA (Low-Rank Adaptation) models apply lightweight style or subject modifications to a base model. Add one or more LoRAs to any pipeline step.
+A LoRA (Low-Rank Adaptation) adapter applies a style, subject or step-distillation change to a base model. A pipeline step takes a `loras` list, a sibling of `configuration`, on any pipeline diffusers can load adapters into (`load_lora_weights()`; `dw/pipeline_processors/adapters.py`).
 
 ## Basic Usage
 
@@ -33,28 +33,32 @@ LoRA (Low-Rank Adaptation) models apply lightweight style or subject modificatio
         "weight_name": "specific_weights.safetensors",
         "subfolder": "lora_subfolder",
         "adapter_name": "my_adapter",
-        "scale": 0.8
+        "scale": 0.8,
+        "alpha": 16
     }
 ]
 ```
 
 | Property | Required | Description |
 | -------- | -------- | ----------- |
-| `model_name` | Yes | HuggingFace Hub repo ID |
+| `model_name` | Yes | Hugging Face Hub repo id. `null` switches the entry off: it is not loaded and the step runs without it, with a `lora_disabled` warning at validate and run. A template's `loras` list is fixed JSON, so this is how a caller drops its adapter (MiniMax-H3: pass `lora_model_name=null`) |
 | `weight_name` | No | Specific weight file in the repo |
 | `subfolder` | No | Subfolder within the repo |
-| `adapter_name` | No | Named identifier for the adapter. Defaults to the LoRA's position in the list (`"0"`, `"1"`, ...) if omitted |
-| `scale` | No | Blend strength (default: 1.0). Lower = less effect |
+| `adapter_name` | No | The adapter's name in `set_adapters()`. Defaults to the entry's position in the list (`"0"`, `"1"`, ...), counting switched-off entries |
+| `scale` | No | Adapter weight, default 1.0. Lower = less effect. Takes a `variable:` reference |
+| `alpha` | No | Network alpha, overriding the checkpoint's. peft scales an adapter by `scale * alpha / rank`, taking alpha from the file (a per-module `.alpha` tensor, else a `__metadata__` alpha, else the rank). Positive; state it only where the file records the wrong figure, after reading its header. Fails if no loaded layer carries the adapter. The run logs the effective `alpha / rank` |
 
-Any other property (e.g. `revision`) is forwarded as-is to the underlying `load_lora_weights()` call.
+Any other property (e.g. `revision`) is passed to `load_lora_weights()` unchanged.
 
 `scale` and `alpha` are set in place on the loaded adapter, so a server run that changes
-only them reuses the warm pipeline instead of reloading it; changing `model_name` or
-`weight_name` reloads.
+only them reuses the warm pipeline; changing `model_name` or `weight_name` reloads.
+
+A step-distillation (turbo) LoRA sets the step count and sigma shift the step runs at.
+Switching one off, raise both to the base model's schedule, or the step runs undertrained.
 
 ## Multiple LoRAs
 
-Stack multiple LoRAs. They are blended via weighted adapter composition:
+Several entries load as separate adapters, each at its own `scale`, active together:
 
 ```json
 "loras": [
@@ -73,7 +77,7 @@ Stack multiple LoRAs. They are blended via weighted adapter composition:
 
 ## LoRA with Quantization
 
-LoRAs work with quantized models:
+A LoRA loads onto a quantized component as onto any other:
 
 ```json
 {
@@ -108,7 +112,7 @@ LoRAs work with quantized models:
 
 ## Variable LoRA
 
-Make the LoRA configurable via workflow variables:
+A `variable:` reference makes the adapter an argument:
 
 ```json
 {
@@ -130,9 +134,8 @@ python -m dw.run workflow.json lora="other-user/other-lora"
 
 ## Examples
 
-- [lora.json](../workflows/templates/lora.json) — Flux with realism LoRA and variables
-- [lora.json](../workflows/templates/lora.json) — SD 3.5 with yarn art style LoRA
-- [lora.json](../workflows/templates/lora.json) — A LoRA adapter on FLUX.1 dev, with the SD 3.5 variant in its description
+- [lora.json](../workflows/templates/lora.json): FLUX.1 dev with `XLabs-AI/flux-RealismLora` as the `lora` variable; its description names an SD 3.5 variant (`linoyts/yart_art_sd3-5_lora`)
+- [lora-styles.json](../workflows/templates/lora-styles.json): one prompt through ten FLUX.1 in-context LoRA styles, one image per adapter
 
 ## LoRA catalog
 
@@ -162,8 +165,9 @@ a step's `loras` entry. `base_models` holds exact repo ids, and matching is
 exact - an adapter on the wrong base usually loads and is quietly worse.
 `workflow` constrains a MiniMax-H3 entry to a partition (`t2va`, `fl2va`,
 `ref2va`) - a string, or a list of them for an adapter that covers several
-(the turbo keyframe files are `["t2va", "fl2va"]`). `proven` and `rejected` entries need `evidence`; a rejected
-entry's first note is why. The schema is `GET /api/lora-schema`.
+(the turbo keyframe files are `["t2va", "fl2va"]`). `proven` and `rejected` entries need
+`evidence`; a rejected entry's first note is why. The schema is `GET /api/lora-schema`
+(`dw/lora_catalog_schema.json`).
 
 The server's validate enforces that `workflow` (#870): a step whose workflow
 sits in the other partition is refused when the entry is `proven`, and only
@@ -180,9 +184,9 @@ it is also what makes its `workflow` refuse a mismatched step.
 place dw searches the Hub, and only when called. It returns the catalog's
 entries for the model first, ranked against the query, then Hub adapters
 whose card declares that exact base (`base_model:adapter:<repo>`), most
-downloaded first. Nothing is downloaded; the weight file's header is read to
-check its layout, for a single-weight repo only. Hub rows are candidates to trial, never recommendations,
-and carry `warnings`:
+downloaded first. Nothing is downloaded: for a single-weight repo the weight
+file's header is read to check its layout. Hub rows are candidates to trial, never
+recommendations, and carry `warnings`:
 
 | Warning | Meaning |
 | --- | --- |
diff --git a/docs/MCP.md b/docs/MCP.md
index 6e99529d..8ba7ebed 100644
--- a/docs/MCP.md
+++ b/docs/MCP.md
@@ -1,79 +1,63 @@
 # MCP Server
 
-A third way to drive the engine, alongside `dw.run` and
-`dw.serve`: a stdio [MCP](https://modelcontextprotocol.io) server that lets
-an MCP client — Claude Code first — author, validate, save, run and diagnose
-workflows without shell access or a repo checkout.
+A stdio [MCP](https://modelcontextprotocol.io) server that lets an MCP client
+(Claude Code first) author, validate, save, run and diagnose workflows without
+shell access or a repo checkout. It is the third way to drive the engine,
+beside `dw.run` and `dw.serve`.
 
-`dw_mcp/` is an HTTP client of a **running** `dw.serve`. It owns no job
-state and no GPU worker of its own; every tool call is a REST request
-against the server described in [Server & Web UI](SERVER.md). If `dw.serve`
-is not running, every tool fails with a message telling you to start it.
+`dw_mcp/` is an HTTP client of a **running** `dw.serve`. It holds no job state
+and no GPU worker; every tool call is a REST request against the server
+described in [Server & Web UI](SERVER.md). If `dw.serve` is not running, every
+tool fails with a message telling you to start it.
 
 ## Install and run
 
 ```bash
 pip install -e ".[server,mcp]"
+dw-serve          # the engine; leave it running
 ```
 
-The MCP server needs a running `dw.serve`, so two processes are involved:
-
-```bash
-# terminal 1 - the engine. Leave it running.
-dw-serve
-```
-
-You do not start `dw-mcp` yourself. Your MCP client launches it on demand,
-which is why the client needs a command it can actually find (see below).
-
-`dw-mcp` (equivalently `python -m dw_mcp`) speaks MCP over stdio. Flags:
+You do not start `dw-mcp` yourself: the MCP client launches it on demand, so
+the client needs a command it can find (see below). `dw-mcp` (or
+`python -m dw_mcp`) speaks MCP over stdio. Flags:
 
 | Flag | Default | Meaning |
 | --- | --- | --- |
 | `--url` | `$DW_MCP_URL`, else `http://127.0.0.1:8765` | Base URL of the running `dw.serve` |
-| `--token` | `$DW_API_TOKEN`, else none | Bearer token, when `dw.serve` was started with `--token` / `DW_API_TOKEN` - the same variable, so one export configures both ends |
-| `--workspace` | `$DW_MCP_WORKSPACE`, else the server's default | Which of the server's workspaces the session works in. A *name* on the server, not a directory here - `DW_WORKSPACE` means something else to the engine. `use_workspace` switches it mid-session |
+| `--token` | `$DW_API_TOKEN`, else none | Bearer token, when `dw.serve` was started with `--token` / `DW_API_TOKEN`. The same variable configures both ends |
+| `--workspace` | `$DW_MCP_WORKSPACE`, else the server's default | Which of the server's workspaces the session works in. A *name* on the server, not a local directory (`DW_WORKSPACE` means something else to the engine). `use_workspace` switches it mid-session |
 | `--timeout` | `30` | Seconds to wait on any one API request |
 | `--no-probe` | off | Skip the startup `GET /api/health` that confirms the server is reachable and the token is accepted |
 
-The `DW_MCP_URL` environment variable sets the same default the `--url` flag
-overrides.
-
-A non-loopback `--url` requires a token: `dw-mcp` exits 2 rather than start
-without one. At startup it makes one `GET /api/health`, so a wrong URL or
-token is reported once with a message instead of as a 401 on every tool
-call; that probe is fatal for a remote URL and only a warning for a
-loopback one (where it usually means `dw.serve` is not up yet).
-[REMOTE.md](REMOTE.md) covers the remote setup end to end.
+A non-loopback `--url` with no token makes `dw-mcp` exit 2. The startup probe
+reports a wrong URL or token once, instead of as a 401 on every tool call; it
+is fatal for a remote URL and only a warning for a loopback one, where it
+usually means `dw.serve` is not up yet. [REMOTE.md](REMOTE.md) covers the
+remote setup end to end.
 
-Claude Code users can add the composition skills as well:
+Claude Code users can add the composition skills with
 `/plugin marketplace add dkackman/diffusers-workflow` then
-`/plugin install dw@diffusers-workflow`. The plugin ships one skill per model
-family (MiniMax H3, MiniMax Music 3, LTX-2.5) that picks a template for a
-request's shape and states the family's rules - see
-[plugins/dw/README.md](../plugins/dw/README.md), which also gives the
-optional `npx skills add` lines for MiniMax's own prompt skills. It is
-optional; every tool below works without it.
+`/plugin install dw@diffusers-workflow`: one skill per model family (MiniMax
+H3, MiniMax Music 3, LTX-2.5, Kandinsky 6, FLUX 3 Klein) plus cross-cutting
+skills for scripts, series, cinematography and storyboards. See
+[plugins/dw/README.md](../plugins/dw/README.md), which also gives the optional
+`npx skills add` lines for MiniMax's own prompt skills. Every tool works
+without the plugin.
 
 ### Use the absolute path to `dw-mcp`
 
-**This is the one setup detail that reliably goes wrong.** If you installed
-the way `install.sh` does, `dw-mcp` lives in the project's virtualenv and is
-only on `PATH` while that venv is activated. Your MCP client is launched by
-your shell, your desktop app, or your editor — usually *without* the venv
-activated — so a bare `dw-mcp` fails to spawn:
+**This is the setup detail that most often goes wrong.** Installed the way
+`install.sh` does it, `dw-mcp` lives in the project's virtualenv and is on
+`PATH` only while that venv is active. An MCP client is usually launched
+without the venv, so a bare `dw-mcp` fails to spawn:
 
 ```
 Failed to reconnect to dw: ENOENT
 ```
 
-Registering it once from an activated terminal hides this: that session
-works, and the next one, started somewhere else, does not.
-
-Always register the venv's absolute path. Console scripts have the
-interpreter baked into their shebang, so they run correctly with no venv
-activated — which is exactly why the absolute path is more robust than
-telling people to activate first:
+Registering from an activated terminal hides the problem until a session
+starts somewhere else. Register the venv's absolute path instead; a console
+script has its interpreter in its shebang, so it runs with no venv active:
 
 ```bash
 echo "$(pwd)/venv/bin/dw-mcp"    # the value to register
@@ -83,42 +67,36 @@ echo "$(pwd)/venv/bin/dw-mcp"    # the value to register
 
 ### Remote server, no local install
 
-If `dw.serve` runs on another machine with `--mcp` (see
-[REMOTE.md](REMOTE.md)), Claude Code connects to it directly:
+If `dw.serve` runs on another machine with `--mcp` (see [REMOTE.md](REMOTE.md)),
+Claude Code connects to it directly and nothing from this repository is
+installed on the client:
 
     claude mcp add --transport http dw http://<box>:8765/mcp \
       --header "Authorization: Bearer <token>"
 
-The same token fetches generated files: see step 7 of `The loop` in [WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#the-loop).
-
-Nothing from this repository is installed on the client. The stdio setup
-below is for a machine that has its own `dw` install, and also works
+Generated files under `/outputs`, `/inputs` and `/exports` need no token
+(#592); step 7 of [The loop](WORKFLOW_GUIDE.md#the-loop) fetches them. The
+stdio setup below is for a machine with its own `dw` install, and also works
 against a remote `--url` with `--token`.
 
 ### Claude Code
 
-The CLI is the shortest path. From the project directory:
+From the project directory:
 
 ```bash
 claude mcp add dw -- "$(pwd)/venv/bin/dw-mcp"
+claude mcp add dw -- "$(pwd)/venv/bin/dw-mcp" --url http://127.0.0.1:8791   # subprocess flags go after --
 ```
 
-`--` separates Claude Code's own flags from the command it will spawn. Add
-subprocess flags after it:
-
-```bash
-claude mcp add dw -- "$(pwd)/venv/bin/dw-mcp" --url http://127.0.0.1:8791
-```
-
-Pick the scope deliberately with `-s`:
+Pick the scope with `-s`:
 
 | Scope | Stored in | Use when |
 | --- | --- | --- |
 | `local` (default) | `~/.claude.json`, keyed to this project | Just you, just this checkout |
-| `user` | `~/.claude.json`, global | You want it in every project. The absolute path makes this work |
-| `project` | `.mcp.json`, **committed to the repo** | You intend every clone to get it. Note an absolute path is machine-specific and will not port |
+| `user` | `~/.claude.json`, global | Every project. The absolute path makes this work |
+| `project` | `.mcp.json`, **committed to the repo** | Every clone gets it. An absolute path is machine-specific and will not port |
 
-Equivalent hand-written `.mcp.json`, if you prefer a file:
+The same registration as a hand-written `.mcp.json`:
 
 ```json
 {
@@ -131,14 +109,12 @@ Equivalent hand-written `.mcp.json`, if you prefer a file:
 }
 ```
 
-**A running session does not pick up a registration change** - start a new
-one after adding or editing the server.
+**A running session does not pick up a registration change**; start a new one.
 
 ### Claude Desktop
 
-`claude_desktop_config.json`, same shape - and the same absolute-path rule,
-which matters more here because a desktop app never inherits a shell's
-`PATH`:
+`claude_desktop_config.json` takes the same shape. The absolute path matters
+more here, because a desktop app never inherits a shell's `PATH`:
 
 ```json
 {
@@ -151,393 +127,302 @@ which matters more here because a desktop app never inherits a shell's
 }
 ```
 
-`DW_MCP_URL` can be set instead of `--url` via an `"env"` object alongside
-`"command"`/`"args"` in either config.
+In either config, an `"env"` object beside `"command"`/`"args"` can set
+`DW_MCP_URL` instead of `--url`.
 
 ## Verify the setup
 
-Three checks, in order. Each isolates a different failure, so run them in
-sequence rather than jumping to the last one.
+Run the three checks in order; each isolates a different failure. None costs
+GPU time.
 
 **1. The command launches without a venv.** This reproduces the environment
-your client actually spawns it in, and is the check that catches ENOENT:
+the client spawns it in, and catches ENOENT:
 
 ```bash
 env -i PATH=/usr/bin:/bin HOME="$HOME" /path/to/venv/bin/dw-mcp --help
 ```
 
-Prints usage and exits 0. If it does not, the path is wrong or the package
-is not installed into that venv.
+It prints usage and exits 0. If not, the path is wrong or the package is not
+installed into that venv.
 
-**2. The client sees the server.** Start a new Claude Code session and run
-`/mcp`; `dw` should be listed and connected. From the shell,
-`claude mcp list` and `claude mcp get dw` show the same thing.
+**2. The client sees the server.** In a new Claude Code session, `/mcp` lists
+`dw` as connected (`claude mcp list` and `claude mcp get dw` from the shell).
+"Connected" means only that the process launched, not that `dw.serve` is
+reachable.
 
-Note that a "connected" status only means the process launched - it says
-nothing about whether `dw.serve` is reachable.
-
-**3. The tools reach the engine.** With `dw-serve` running, ask the client
-something free, such as "list my diffusers workflows" (`list_workflows`) or
-"check the diffusers-workflow server health" (`get_health`). A real answer
-means the whole chain works. "Cannot reach diffusers-workflow at ..." means
-step 3 failed while steps 1 and 2 passed - the client is fine and the engine
-is not running.
-
-Nothing in this sequence costs GPU time.
+**3. The tools reach the engine.** With `dw-serve` running, ask for something
+free, such as "list my diffusers workflows" (`list_workflows`) or "check the
+diffusers-workflow server health" (`get_health`). "Cannot reach
+diffusers-workflow at ..." means the engine is not running.
 
 ## Tool reference
 
-65 tools in six groups. Names and arguments below are transcribed from
-`dw_mcp/tools_*.py` — nothing here is renamed or reshaped for the docs.
+65 tools in six groups. Names, arguments and defaults are those registered in
+`dw_mcp/tools_*.py` (wired in `dw_mcp/server.py`); each tool's own description
+is the full contract. Every `workspace` argument pins that one call to a
+workspace without moving the session's pin (see
+[Authoring, assets and workspaces](#authoring-assets-and-workspaces)).
 
 ### Catalog (read-only)
 
-The catalog is large, so the server's instructions point a client at
-`list_workflows` first: its listing carries enough about each workflow - a
-one-line `summary`, its `shape` and `traits`, its measured `cost`, output
-kinds and variable names - to pick one and know what to pass it, without
-fetching every candidate's definition. Reusing a stored workflow is a
-preference, not a rule; `run_workflow` still takes an `inline_workflow` for
-a request nothing on disk covers.
-
-The instructions also say the engine runs **one job per GPU** - one worker
-per `dw.serve --devices` card, each running its own job from the one shared
-queue - and point at `get_health`'s `workers` for what each card is running.
-A job record's `device` (`get_job`, `list_jobs`) names the card it ran on,
-and a job started by `rerun_job` carries `rerun_of`, the id of the job it
-reran (absent on a fresh submit).
-
-A request usually names a *subject* ("a lego movie trailer set in the marvel
-universe") while the catalog is written in *shapes* - a single image, an image
-set, one shot, a multi-shot cut sequence, video with speech. Nothing in a
-catalog entry will match the subject, so the shape is what has to be decided
-first and matched against. `list_guides` indexes the engine's documentation by
-section for exactly that, and `list_tasks` is what a shape gets composed from
-when no single workflow covers it.
-
-| Tool | Arguments | Purpose |
-| --- | --- | --- |
-| `list_guides()` | — | List the documentation the engine serves: each guide's name, what it covers, and its section headings. The index is the routing table - match a request's shape against a heading rather than guessing |
-| `get_guide(name, section=None)` | `name`, `section` | Get one section of a guide - name it: a guide runs to thousands of lines, and WORKFLOW_GUIDE.md whole is ~19.6k tokens, more in one call than the whole tool surface costs to connect. Called with no `section` the answer is the guide's index: its opening, its first section, and `sections`/`withheld` naming the rest, with a `note` saying how to fetch one. Section names match loosely, so a heading copied approximately still resolves |
-| `list_workflows(shape=None, traits=None, configures=None, include_models=False)` | `shape`, `traits`, `configures`, `include_models` | List stored workflows. Called with no filter at all each entry is cut to its `summary` and `shape` (`view: "summary"`, with a `note`), because the whole catalog in full detail is ~6.8k tokens for a question that is really "which shape do I want"; pass `shape` and the entries come back whole. Filtered, it is the server's compact view: each entry carries `summary`, `shape`, `traits`, `cost`, `kinds`, `variable_names`, `lists`, and `configures` only when set - `get_workflow` has the full description and definition. `lists`, present for a list-driven workflow, names the fields an entry of each list takes, the steps over it and the default's length; `cost` may carry `per_entry`, the measured cost of one entry so a run over a different-length list can be priced from it. `shape` keeps one of `image`, `image-set`, `image-edit`, `shot`, `sequence`, `audio`, `text`, `utility`; `traits` is comma-separated and every one listed must match (`has-audio`, `chained`, `image-conditioned`, `identity-referenced`, `needs-input-media`, `composes-workflows`); an unknown value in either is a 400 listing the vocabulary. Templates only by default - `configures=<template>` lists the checkpoint configs tuned for one, `include_models=true` lists them all. The first call to make for a request an existing workflow might cover `libraries` names the roots searched (`origin`, `root`, `writable`) and `shadowed` the names a nearer root hides |
-| `get_workflow(name, variables_only=False)` | `name` | Get one stored workflow's full JSON definition. `variables_only=true` answers with just its variables and their defaults (long strings cut to 200 characters, the cut ones named in `truncated`, including strings inside a list default, named like `shots[0].prompt`) — the cheap way to confirm what a variable defaults to |
-| `get_schema(section=None)` | `section` | Get the JSON schema every workflow definition must satisfy. Whole it is ~8.6k tokens, so name the part you need: `section` takes `steps`, `pipelines`, `tasks`, `result`, `variables` or `configuration` and answers `{section, sections, elsewhere, schema}` - `elsewhere` says which section holds each definition the fragment still `$ref`s. An unknown section is a 404 naming the ones there are |
-| `list_pipelines()` | — | List every diffusers pipeline class this installation provides |
-| `get_pipeline_signature(name)` | `name` | Get a pipeline's real call arguments; for a modular pipeline class (e.g. `MiniMaxH3ModularPipeline`), its block graph's inputs |
-| `list_classes(kind)` | `kind` | List class names of one kind: pipelines, models, schedulers, or quantization |
-| `get_class(name, target="init")` | `name`, `target` (`init`\|`call`\|`load`) | Get a class's argument schema from the entry point a workflow reaches it by: `init` the constructor (quantization configs, schedulers), `call` a pipeline's `__call__`, `load` `from_pretrained` plus the curated loading knobs |
-| `list_tasks()` | — | List every task command a workflow's task step can name |
-| `get_task(command)` | `command` | Get a task command's argument schema |
-| `list_models()` | — | List what the Hugging Face model cache holds, largest first |
-| `get_memory()` | `device` | Get VRAM and RAM statistics: `workers` has one reading per card (the top level repeats the first card's), and `device` (`cuda:1`) asks for that card alone. `gpu_*` is the card; `host_memory_rss_mb` / `host_memory_peak_rss_mb` are the worker process's resident and high-water host memory, beside the machine's `host_memory_total_mb` / `host_memory_available_mb`; on Linux `host_memory_rss_anon_mb` / `host_memory_rss_file_mb` split the resident figure into memory the worker owns and mapped-file pages (memory-mapped checkpoint weights) the kernel can drop, so a loader that maps rather than copies raises the resident and peak figures without raising committed memory (#709) - read both, since an offloading workflow keeps its weights in host RAM and the card says little about what it holds (a host field is absent, not null, where the platform cannot measure it). `host_pinned_reserved_mb` / `host_pinned_allocated_mb`, when present, are torch's pinned-host cache - the staging buffers group offloading moves weights through, part of `host_memory_rss_mb` and invisible in every `gpu_*` figure, which is what a worker holding GB after releasing every model is usually holding (#98). `live: true` means `info` was measured now and is the worker's own memory; only live readings are comparable with each other. `live: false` with `info: null` (and `stale: false`) means nothing has been measured because nothing is resident; `live: false` with a populated `info` is a cached earlier reading, with `reason` (`job_running`, `worker_stopped`, `worker_busy`, `worker_unreachable`) and `age_seconds` - one cached while a job loads a model understates what is resident, so ask again when the card is idle rather than comparing it with a live figure |
-| `get_health()` | — | Check that the server is alive, and which machine answered: `version`, `device`, whether any model process is resident (`worker_alive`), and the queue depth. The engine runs one job per GPU: `workers` has one entry per card - `device`, `name`, `vram_gb`, `alive`, its own `current_job`, and the worker's `host_memory_rss_mb` while it runs - and the top-level `current_job` is the longest-running job. `worker_alive: false` is the normal idle state on a server that has not run a job since startup or the last memory clear - not a fault - the on-demand worker starts with the next job (#206) |
-| `get_server_info()` | — | What this installation can do and where it keeps things: `device` (the accelerator a run will use), `version`, the `workspace` this session is working in and the workflow/asset/output/prompt `directories` of *that* workspace, the bind address and port, whether a token is required, and whether MCP is mounted. Check the device before authoring - a CUDA-only choice (bitsandbytes, `torch.compile`, flash attention) is not available on an `mps` or `cpu` server. `runtime` (#222) reports the Python version, torch version and the CUDA version torch was built against, the NVIDIA driver version (when `nvidia-smi` is reachable), and the installed versions of diffusers, transformers, accelerate, bitsandbytes, peft, safetensors and sentencepiece (`null` for one not installed) - for diagnosing an environment mismatch between boxes without shelling in. `caches` lists dw's disk caches (`inductor`, `triton`, `quantized`), each with `name`, `bytes`, `files`, `enabled`, `clearable` and `disk_free`, plus `entry_count` for a cache with entries, and `cache_entries=true` lists them (`id`, `model`, `subfolder`, `component`, `bytes`, `files`, `unreadable`, `created`, `last_hit`; the quantizer and dtype are not in `/api/caches`); it is omitted when the server has no `/api/caches`. There is no MCP tool to clear one - that is `DELETE /api/caches/{name}` or `.../entries/{entry}` ([SERVER.md](SERVER.md)) |
-| `list_jobs(limit=20, status=None, workspace=None)` | optional `limit` (newest N), `status` (one state or a comma-separated set of `queued`, `running`, `succeeded`, `failed`, `cancelled`), `workspace` | List queued, running and recent jobs, **newest first** (a queued job held for host RAM carries `waiting_for`). Bounded by default: the unbounded listing was over a client's tool-result limit on a server with a few months of history, which made it a tool that could not be called at all. `total` says how many matched and `truncated`/`next` say so when the answer was cut - raise `limit` or narrow with `status`. Without `workspace`, a named workspace lists its own jobs and the default one lists every job the server holds |
-| `list_gallery(limit=50, subfolder=None, only_orphans=False, workspace=None, folder=None, version=None, media=False, role=None)` | `limit`, `subfolder`, `only_orphans`, `workspace`, `folder`, `version`, `media`, `role` | List generated output files, newest first. A name is `<workflow>/<run id>/<file>`, where `<file>` may sit in the subfolder the step chose (`final/episode.mp4`); each entry carries `folder` (the workflow) and `subfolder` (by convention `final` or `intermediate`, `''` when the step chose none, any path the workflow wrote otherwise), and `subfolder="final"` lists only deliverables. Each entry also carries `role`, `final` or `intermediate` (null for a flat-layout file with no run, or an older run's file that neither its subfolder nor a later run's reads place; `intermediate` once a later step or a later run read the file, unless `result.role` declared it); `role=["final"]` lists only those, and the `role` filter defaults to every file. See `dw/roles.py` for how a role is decided. A file starred in the web UI's gallery (its keep mark) carries `starred: true`; an unstarred one carries no `starred`. Each entry carries `run_id` and `version` - that run's ordinal among the workflow's runs, which is how one of several runs that wrote the same basename is named to a person: the web UI labels the same file `v5`. The number is assigned when the run opens and never renumbered, so deleting a run leaves a gap rather than sliding the rest down (a failed run, or a rerun that reused every step, leaves one too - it took a number and may have nothing to list), and it is `null` under the flat output layout, which has no runs. `folder` with `version` lists that one run's files, and `output:<folder>/v5/<file>` names one in a workflow; every other tool takes `name`. Each entry also carries a ready-made `url`, already scoped to the workspace that made it - a hand-built `/outputs/<name>` URL 404s for anything but the default workspace. `only_orphans=True` inverts the call: instead of files, it returns run directories with no media anywhere under them (`runs`, each `{name, mtime}`) - a run whose output was deleted before `delete_output` could remove it by name, or one that failed before writing anything; `subfolder` does not apply in this mode, and `name` is exactly what `delete_output` accepts (#170). `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
-| `get_gallery_metadata(name, envelope=False, workspace=None, interval_seconds=None)` | `name`, `workspace` | Get the metadata embedded in a generated file — or, when `name` is an `asset:` reference, what an *input* asset holds (`source` says which; `job` is null for an asset). Reading an input's duration, frame count, fps and sample rate before a run is how a caller learns the `total_frames`, `fps` and `sample_rate` a workflow expects it to supply: the exact workflow and arguments that produced it, and, for audio/video, a `media` block (duration, rate, channels, fps, size, peak/mean dBFS) and `findings` - each level problem the server measured against `dw/audio_qc.py`'s thresholds (`full_scale`, `near_silent`), with the fix. Only an image (PNG/JPEG/WebP) embeds `metadata` this way — it is always null for audio and video, and `next` then points at `get_job_workflow(job_id)` when `job` is known, or says a kept asset carries no provenance at all when it isn't. `envelope=true` adds `media.envelope` — `rms_dbfs` and `peak_dbfs` one entry per second, or per `interval_seconds` (default 1.0, 0.05 to 3600; 0.1 places a vocal onset; a finer-than-default width that would need over 2400 bins is refused with the interval that fits) — which is what locates something in a track rather than measuring the whole of it. A file with a soundtrack also carries `media.pcm_sha256`, the SHA-256 of its decoded samples (native dtype, interleaved, little-endian, trimmed to the container duration) with the `sample_format` and `frames` hashed: two tracks are sample-identical when the hashes match, and two hashes compare only when `sample_format`, `frames`, `sample_rate` and `channels` all agree. `media.shots` is set on an output joined from shots: one `{name, start_frame, num_frames, start_sample, num_samples}` per shot, as the join measured them (see the run manifest in docs/WORKFLOW_GUIDE.md), else null. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
+The server's instructions send a client to `list_workflows` first: its
+listing carries enough per workflow to choose one and know what to pass it.
+A request usually names a *subject* while the catalog is written in *shapes*
+(one image, an image set, one shot, a cut sequence), so decide the shape
+first. `list_guides` indexes the documentation by section, and `list_tasks`
+is what a shape is composed from when no stored workflow covers it;
+`run_workflow` still takes an `inline_workflow` for that case.
+
+The engine runs **one job per GPU**: one worker per `dw.serve --devices` card,
+each taking jobs from one shared queue. A job record's `device` (`get_job`,
+`list_jobs`) names the card it ran on, and a job started by `rerun_job`
+carries `rerun_of`.
+
+| Tool | Purpose |
+| --- | --- |
+| `list_guides()` | The documentation the engine serves: each guide's name, what it covers, and its section headings. Match a request's shape against a heading |
+| `get_guide(name, section=None)` | One section of a guide, matched loosely (a `###` subsection resolves too). Without `section` the answer is the guide's index: its opening, its first section, and `sections`/`withheld` naming the rest. A whole guide can cost more than the tool surface does to connect |
+| `list_workflows(shape=None, traits=None, configures=None, include_models=False)` | Stored workflows. With no filter each entry is cut to `summary` and `shape` (`view: "summary"`, with a `note`). Filtered, each entry carries `summary`, `shape`, `traits`, `cost`, `observed_minutes`/`observed_runs` (this box's own finished runs), output kinds, `variable_names`, and when set `lists` (per list variable: entry fields, steps over it, default length; `cost[].per_entry` prices one entry), `constraints` and `configures`. `shape` is one of `image`, `image-set`, `image-edit`, `shot`, `sequence`, `audio`, `text`, `utility`; `traits` is comma-separated and all must match (`has-audio`, `chained`, `image-conditioned`, `identity-referenced`, `needs-input-media`, `composes-workflows`); an unknown value is a 400 listing the vocabulary. Templates only by default; `configures=<template>` lists the checkpoint configs tuned for one, `include_models=true` lists them all. `libraries` names the roots searched (`origin`, `root`, `writable`), `shadowed` the names a nearer root hides |
+| `get_workflow(name, variables_only=False)` | One stored workflow's full JSON. `variables_only=true` returns just its variables and defaults, long strings cut to 200 characters and named in `truncated` (`shots[0].prompt` for one inside a list) |
+| `get_schema(section=None)` | The workflow JSON schema (~8.6k tokens whole). `section` is one of `steps`, `pipelines`, `tasks`, `result`, `variables`, `configuration` and answers `{section, sections, elsewhere, schema}`; `elsewhere` says which section holds each `$ref` the fragment still has. An unknown section is a 404 naming the valid ones |
+| `list_pipelines()` | Every diffusers pipeline class this installation provides |
+| `get_pipeline_signature(name)` | A pipeline's real call arguments; for a modular pipeline class (`MiniMaxH3ModularPipeline`), its block graph's inputs |
+| `list_classes(kind)` | Class names of one kind: pipelines, models, schedulers or quantization |
+| `get_class(name, target="init")` | A class's argument schema at the entry point a workflow reaches it by: `init` the constructor (quantization configs, schedulers), `call` a pipeline's `__call__`, `load` `from_pretrained` plus the curated loading knobs |
+| `list_tasks()` | Every task command a task step can name |
+| `get_task(command)` | A task command's argument schema |
+| `list_models()` | What the Hugging Face cache holds, largest first |
+| `get_memory(device=None)` | VRAM and RAM per card in `workers` (the top level repeats the first card; `device`, e.g. `cuda:1`, asks for one). `gpu_*` is the card. `host_memory_rss_mb` / `host_memory_peak_rss_mb` are the worker's resident and peak host memory, beside the machine's `host_memory_total_mb` / `host_memory_available_mb`; on Linux `host_memory_rss_anon_mb` / `host_memory_rss_file_mb` split resident memory into owned pages and mapped checkpoint pages the kernel can drop (#709). `host_pinned_reserved_mb` / `host_pinned_allocated_mb` are torch's pinned-host cache, part of RSS and invisible to `gpu_*` (#98). A host field the platform cannot measure is absent. `live: true` is a reading taken now; `live: false` with `info: null` means nothing is resident; `live: false` with `info` is a cached reading with `reason` (`job_running`, `worker_stopped`, `worker_busy`, `worker_unreachable`) and `age_seconds`. Compare only live readings |
+| `get_health()` | Liveness and which machine answered: `version`, `device`, `worker_alive`, queue depth, and `workers` (per card: `device`, `name`, `vram_gb`, `alive`, `current_job`, `host_memory_rss_mb`). The top-level `current_job` is the longest-running job. `worker_alive: false` is the normal idle state before the first job or after a memory clear (#206) |
+| `get_server_info(cache_entries=False)` | `device`, `version`, this session's `workspace` and its `directories`, the bind address and port, `auth_required`, `trust_workflows`, the MCP mount, and `runtime` (#222: Python, torch, CUDA build, NVIDIA driver, and the diffusers, transformers, accelerate, bitsandbytes, peft, safetensors and sentencepiece versions, `null` when not installed). Check `device` before authoring: CUDA-only choices (bitsandbytes, `torch.compile`, flash attention) fail on `mps` or `cpu`. `caches` sizes dw's disk caches (`inductor`, `triton`, `quantized`); `cache_entries=true` lists the quantized cache's entries. No tool clears a cache; that is `DELETE /api/caches/{name}` ([SERVER.md](SERVER.md)) |
+| `list_jobs(limit=20, status=None, workspace=None)` | Queued, running and recent jobs, **newest first**, with queue position, `device`, `acknowledged` and (when held for host RAM) `waiting_for`. `status` is one state or a comma-separated set of `queued`, `running`, `succeeded`, `failed`, `cancelled`. `total`, `truncated` and `next` show a cut answer. Without `workspace`, a named workspace lists its own jobs and the default lists every job |
+| `list_gallery(limit=50, subfolder=None, only_orphans=False, workspace=None, folder=None, version=None, media=False, role=None)` | Generated files, newest first, named `<workflow>/<run id>/<file>` (`<file>` may sit in a subfolder: `final/episode.mp4`). Each entry carries `folder`, `subfolder` (`final`, `intermediate`, `''` or any path the workflow wrote), `role` (`final`, `intermediate`, or null; see `dw/roles.py`), `starred` when kept in the web UI, `run_id`, `version` and a workspace-scoped `url` (a hand-built `/outputs/<name>` 404s outside the default workspace). `version` is the run's ordinal (`v5` in the UI): assigned when the run opens, never renumbered, `null` in the flat layout; `output:<folder>/v5/<file>` names a file by it. `folder` with `version` lists one run. `role=["final"]` lists only deliverables. `only_orphans=True` returns run directories with no media (`runs`, each `{name, mtime}`), each a name `delete_output` accepts (#170) |
+| `get_gallery_metadata(name, envelope=False, workspace=None, interval_seconds=None, include_workflow=True)` | What produced a file and what it measures. `name` may be an `asset:` reference, for an input's duration, frame count, fps and sample rate before a run (`source` says which; `job` is null). Only an image embeds `metadata`; for audio and video it is null and `next` points at `get_job_workflow(job_id)`. `include_workflow=false` omits the embedded workflow. `media` holds duration, rates, channels, fps, size and peak/mean dBFS; `findings` lists each level problem (`full_scale`, `near_silent`, thresholds in `dw/audio_qc.py`) with its fix. `envelope=true` adds `media.envelope` (`rms_dbfs`, `peak_dbfs`) per second or per `interval_seconds` (0.05 to 3600; over 2400 bins is refused). `media.pcm_sha256` hashes the decoded samples; two hashes compare only when `sample_format`, `frames`, `sample_rate` and `channels` agree. `media.shots` places a joined output's shots (`{name, start_frame, num_frames, start_sample, num_samples}`) |
 
 ### Media
 
-| Tool | Arguments | Purpose |
-| --- | --- | --- |
-| `get_output_image(name, max_dimension=768, workspace=None, crop=None)` | `name`, `max_dimension`, `workspace`, `crop` | Look at a generated image, downscaled to `max_dimension` on its longest side. Returns the image plus a text part reporting `original_size`, `returned_size` and `bytes`, so a downscale is never silent. `crop` is `[x, y, width, height]` in the original's pixels, cut before the downscale and reported back clamped - the way to see a region of a 2K still at 100%, where the whole would be shrunk past what a small element or a decode-tiling seam can be judged at. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
-| `get_output_audio(name, start=None, duration=None, workspace=None)` | `name`, `start`, `duration`, `workspace` | Listen to a generated soundtrack as base64 - an audio output, or the track muxed into a video (#193) - in its own encoding when served whole, WAV when extracted or excerpted. No downscale exists for audio, so a whole clip over the 4MB budget is refused rather than cut (#204); ask for the part instead with `start` and `duration` in seconds, and the text part names what was cut (`excerpt: 2.0s from 10.0s of 240.0s`) so a slice is never mistaken for the whole. `get_gallery_metadata`'s envelope says where in a track to look. `workspace` names the workspace for this one call without switching the session to it |
-| `get_output_frames(name, at=None, seams=None, count=None, boundaries=None, names=None, max_dimension=512, hear=None, workspace=None)` | `name`, `at`, `seams`, `count`, `boundaries`, `names`, `max_dimension`, `hear`, `workspace` | See a generated video as frames, since there is no video content type over MCP (#193). One selector per call: `count` for an evenly spaced contact sheet, `at` for moments (seconds or `"frame:N"`), `seams` (true, or seam numbers from 1) for the last frame before and first frame after each join side by side (each pair carries `difference`, the mean pixel change across the join, 0-255 - rank seams by it and look at the worst). `boundaries` and `names` are modifiers of `seams` only - passing either alongside `at` or `count` is refused, naming the offending argument(s). On an output joined from shots (`concat_videos`, `dissolve_videos`, a chained step) `seams` alone is enough: the seams and their names are the `media.shots` its run recorded. For any other file pass `boundaries` - each later shot's first frame, the running sum of the shots' `frame_count` from `get_gallery_metadata` on their own `intermediate/` files - and `names` to name them; either one given overrides the recorded value. Tiles are fitted to `max_dimension` and, when the set would exceed the 4MB budget, shrunk together rather than dropped; the text part lists each tile and says so. `hear=N` also returns N seconds of soundtrack centred on each `at` moment, after its image - the way to check a hit point or lip-sync without reconciling two clocks; a mute clip keeps its frames and says `no soundtrack` |
-| `get_output_text(name, max_characters=20000, workspace=None)` | `name`, `max_characters`, `workspace` | Read a text output — a prompt enhancement, or any step whose result is `text/plain` or JSON. Reports the file's real length and whether it was truncated. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
-| `assess_output(name, probe=None, detail=False, workspace=None)` | `name` (a gallery name or `asset:`), `probe`, `detail`, `workspace` | Measure a finished cut on the server, without queueing: the assessment probes (`analyze_shots`, `analyze_seams`, `analyze_sync_drift`) run in the server process over one decode of the file, beside whatever job holds the GPU. Shot boundaries are the ones its run recorded (the manifest for an output, the `keep_output` sidecar for an asset). The answer merges every applicable probe's `findings` (`{rule, severity, at, value, threshold, says}`) with `rules_applied`, `rules_skipped` and `not_applicable` (`{probe: why}` - a still, a mute file, a file with no recorded shots); `detail=true` adds each probe's full body under `probes`, and `probe=` returns that one probe's full body. `probe` is checked against the three names before anything else is read, and an unknown one is refused naming them. Findings are places to look, not verdicts: drill in with `get_output_frames(seams=[n])` / `get_output_audio(start, duration)`. See *Assessing a run's output* in docs/WORKFLOW_GUIDE.md |
-| `compare_outputs(names, at=None, crop=None, baseline=None, max_dimension=384, texture_bands=False, workspace=None)` | `names`, `at`, `crop`, `baseline`, `max_dimension`, `texture_bands`, `workspace` | Read-only, queues nothing: 2-8 outputs (gallery names or `asset:`) as one grid, a row per output (labelled with its row number and short name, `(baseline)` on the baseline) and a column per moment, each cell stamped `frame N`, with PSNR, SSIM and mean absolute difference against `baseline` (default the first name). `at` is 1-8 moments (seconds or `"frame:N"`, never negative: unlike `get_output_frames`, a negative moment is a 400, not counted back from the end); they resolve to one frame index against the baseline (the first video if the baseline is a still) and every output is read at that same frame number; default 4 evenly spaced frames of the shortest video. A frame past an output's end is a blank cell and its metrics cell says `past end (N frames)`; `fps_differs` flags videos with different frame rates, where the same frame number is not the same second. All stills: one column, `at` ignored with a note; a still among videos repeats in every column. Metrics are per non-baseline output at source resolution: `psnr_db` (`identical` when equal), `ssim` (luma, 11x11 Gaussian window, sigma 1.5) and `mean_abs_diff` (0-255), per column and as mean/min/max. `crop` is `[x,y,w,h]`: one shared box is the region shown and measured (`region` reads `crop [...]`); a list of one box per name is display only and metrics stay on the full frame (no crop is `full frame`). A crop must lie wholly inside every output's frame, else 400 (stricter than `get_output_frames`, which clamps). An output whose size differs from the baseline gets null metrics with the reason `size differs from baseline: WxH vs WxH` - it is never resized. `max_dimension` is the longest side of each cell; the grid is held to 4 MiB and shrunk whole when over (never below 64 px per cell), `downscaled_to` null when nothing shrank. `texture_bands=true` adds per-output RMS texture energy by wavelength (`fine` 2-8 px, `mid` 8-32, `coarse` 32-128) for `luma`, `chroma_rg` and `chroma_bg` over the measured region, plus `texture_ratio` against the baseline for non-baseline outputs; it needs one shared `crop` (at least 16 px a side) or none, and allows a single name (the baseline, so no ratios). It is a measurement, not a score, and means something only on a static box. Returns the grid image and a text summary. Errors: 400 bad shape, out-of-bounds crop, every moment past every end, or not an image or video; 404 unknown name; 413 over the decode pixel limit; 422 undecodable file. See *Assessing a run's output* in [the workflow guide](WORKFLOW_GUIDE.md#assessing-a-runs-output) |
-| `download_output(name, destination=None, overwrite=False, workspace=None)` | `name`, `destination`, `overwrite`, `workspace` | Save one output file to local disk, of any content type. `destination` may be a full path or a directory; `~` expands and missing parent directories are created. `overwrite=True` is required to replace a file already at the resolved path. Over the stdio `dw-mcp`, omitting `destination` saves under the output's own name in the current working directory. Over a `dw.serve --mcp` endpoint the file lands on the server confined to that workspace (a relative path is joined onto it), and `destination` is required there - an omitted one is refused rather than dropped loose in the workspace root, where nothing can find or delete it later (#353); use the `url` `list_gallery` reports, `get_output_image`/`get_output_audio`/`get_output_frames` for inline content, or `keep_output` to make it a named asset instead. Returns nothing to the conversation but where the file landed — unlike the other media tools, the point is a file on disk, not a payload in context. Writes on the machine running the MCP server - over `dw.serve --mcp` that is the GPU box. A write that fails there (a path that exists only on the client, for instance) comes back as an error naming the server-side write and the client-side alternatives, not as an anonymous tool failure. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
-| `delete_output(name=None, workspace=None, job_id=None)` | exactly one of `name` / `job_id`, `workspace` | Permanently remove one generated file from the output directory, or - with `name` a `<workflow>/<run id>` run directory, or with `job_id` - a whole run. By `job_id` the server reads the run directory from the job record and the root the job ran in (`DELETE /api/jobs/{id}/run`), and the reply is `job_id`, `run_dir`, `deleted` and `run_swept`; a job that never wrote a run directory, an unknown one, or one still queued or running is an error. `workspace` names the workspace for a `name` delete without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
+| Tool | Purpose |
+| --- | --- |
+| `get_output_image(name, max_dimension=768, workspace=None, crop=None)` | A generated image, downscaled to `max_dimension` on its longest side, with a text part reporting `original_size`, `returned_size` and `bytes`. `crop` is `[x, y, width, height]` in the original's pixels, cut before the downscale and reported back clamped |
+| `get_output_audio(name, start=None, duration=None, workspace=None)` | A soundtrack (an audio output, or a video's muxed track) as base64: its own encoding when whole, WAV when excerpted. A whole clip over the 4MB budget is refused, not cut (#204); ask for `start`/`duration` in seconds, and the text part names the excerpt (`excerpt: 2.0s from 10.0s of 240.0s`) |
+| `get_output_frames(name, at=None, seams=None, count=None, boundaries=None, names=None, max_dimension=512, hear=None, workspace=None, crop=None)` | A video as frames (#193). One selector per call: `count` (an evenly spaced contact sheet), `at` (seconds or `"frame:N"`), or `seams` (true, or seam numbers from 1: the frame pair across each join, with `difference`, the mean pixel change 0-255). `boundaries` and `names` modify `seams` only; passing either with `at` or `count` is refused. On an output joined from shots, `seams` reads the recorded `media.shots`; for any other file pass `boundaries` (each later shot's first frame) and `names`. `crop` is `[x, y, width, height]` in source pixels, cut before any downscale. Over the 4MB budget, tiles shrink together. `hear=N` adds N seconds of soundtrack centred on each `at`; a mute clip says `no soundtrack` |
+| `get_output_text(name, max_characters=20000, workspace=None)` | A text output (a prompt enhancement, any `text/plain` or JSON result), with its real length and whether it was truncated |
+| `assess_output(name, probe=None, detail=False, workspace=None)` | Measures a finished cut in the server process without queueing: `analyze_shots`, `analyze_seams` and `analyze_sync_drift` over one decode, using the shot boundaries its run recorded (the manifest, or the `keep_output` sidecar for an asset). Returns merged `findings` (`{rule, severity, at, value, threshold, says}`) with `rules_applied`, `rules_skipped` and `not_applicable`; `detail=true` adds each probe's body under `probes`; `probe=` returns one probe's body, and an unknown name is refused first. Findings are places to look, not verdicts. See [Assessing a run's output](WORKFLOW_GUIDE.md#assessing-a-runs-output) |
+| `compare_outputs(names, at=None, crop=None, baseline=None, max_dimension=384, texture_bands=False, workspace=None)` | 2-8 outputs (gallery names or `asset:`) as one grid, a row per output and a column per moment, with PSNR (`psnr_db`), SSIM (luma, 11x11 Gaussian, sigma 1.5) and `mean_abs_diff` (0-255) against `baseline` (default the first name), per column and as mean/min/max. `at` is 1-8 moments (seconds or `"frame:N"`; negative is a 400), resolved to one frame index against the baseline and read at that frame in every output; default 4 evenly spaced frames of the shortest video. A frame past an output's end is a blank cell (`past end (N frames)`); `fps_differs` flags mismatched rates. One shared `crop` `[x,y,w,h]` is shown and measured; a list of one box per name is display only. A crop must lie wholly inside every frame (400). A size mismatch gives null metrics, never a resize. The grid is held to 4 MiB (never below 64 px a cell). `texture_bands=true` adds RMS texture energy per band (`fine` 2-8 px, `mid` 8-32, `coarse` 32-128) for `luma`, `chroma_rg`, `chroma_bg`, plus `texture_ratio` against the baseline; it needs one shared crop of at least 16 px a side, or none, and allows a single name. Errors: 400 bad shape, crop out of bounds, every moment past every end, or not an image or video; 404 unknown name; 413 over the decode pixel limit; 422 undecodable |
+| `download_output(name, destination=None, overwrite=False, workspace=None)` | Saves one output, any type, to disk on the machine running the MCP server, and returns only where it landed. `destination` is a path or a directory (`~` expands, parents are created); a `..` segment is refused; an existing file needs `overwrite=True`. Over stdio, no `destination` saves under the output's name in the working directory. Over `dw.serve --mcp` the file lands on the server, confined to the workspace, and `destination` is required (#353); use the entry's `url`, the inline tools, or `keep_output` instead |
+| `delete_output(name=None, workspace=None, job_id=None)` | Permanently removes one generated file, or a whole run: `name` as `<workflow>/<run id>`, or `job_id` (`DELETE /api/jobs/{id}/run`, which answers `job_id`, `run_dir`, `deleted`, `run_swept`). Exactly one of the two. A job with no run directory, an unknown one, or one still queued or running is an error. A `job_id` delete goes to the workspace the job ran in |
 
 ### Authoring, assets and workspaces
 
-Authoring happens inside one workspace. A server can hold several - each with
-its own `workflows/`, `assets/` and `outputs/`, all sharing one prompt library
-- and `use_workspace` picks the one this session reads and writes for the rest
-of its life. That is how two agents work against one GPU without saving over
-each other; see [Workspaces](WORKSPACES.md#several-workspaces-on-one-server).
-The session starts in `default` and stays there unless it is told otherwise.
-
-The pin above is per-*process* state on `client.py`'s `DwClient`, which is
-one session for the local stdio `dw-mcp`, but `dw.serve --mcp` (see
-[REMOTE.md](REMOTE.md#claude-code-no-local-install)) builds a single client
-for every agent it serves - so on that mounted surface the pin is shared by
-every concurrently connected caller, not scoped to any one of them (#298).
-A second client's `use_workspace`/`create_workspace(use=true)` can switch
-what your session reads and writes without your session calling either. This
-server is single-user by design, so nothing tracks distinct MCP sessions to
-fix that; `use_workspace`/`create_workspace` add a `warning` field to their
-result when the pin they are about to overwrite was already pointed
-somewhere other than `default` (a sign another client may depend on it), but
-they cannot warn the *other* session whose pin just moved out from under it.
-A caller that needs real isolation on a mounted server - the tester and
-regression harnesses among them - must pass `workspace=` on every call that
-accepts it (`validate_workflow`, `run_workflow`, and most of the read/media
-tools) rather than relying on the session pin.
-
-| Tool | Arguments | Purpose |
-| --- | --- | --- |
-| `validate_workflow(workflow=None, name=None, workspace=None, arguments=None)` | exactly one of `workflow` (inline definition) or `name` (a stored workflow, as `list_workflows` reports it), optional `workspace`, optional `arguments` | Check a workflow against the schema and against real pipeline signatures. Free and instant. Validating by name uses the workflow file's own directory as the base directory, so it sees what a run would. Returns every schema violation in `errors`, each with the JSON path it sits at, so a draft is fixed in one pass, and a `previous_result:` that names no earlier step is one of them. `warnings` covers what still runs but is probably wrong - a signature mismatch, and, for a list-driven variable, an entry key no step reads, at the entry's path. `workspace` names the workspace for this one call without switching the session to it - use it to pin a job whose `output:` or `asset:` references live in a workspace other than the session's. Pass the same `arguments` you will pass to `run_workflow` and they are checked too - an undeclared or renamed variable name, a value that will not coerce to the declared type, and an `asset:`, `prompt:` or `output:` reference that names nothing this workspace can reach, each reported at `arguments.<name>`. `checked_arguments` lists what was covered, so a `valid: true` about the stored defaults cannot be mistaken for one about your values. A reference set the model would refuse - too many images, videos or audio clips, or, for MiniMax-H3, audio as the only reference - is an error here too, rather than a failure minutes into a run you acknowledged. `run_workflow` makes the same check and refuses a bad argument rather than queuing a job that fails on its first step. A valid answer carries `plan` - the fingerprint, step count, list lengths, `downloads_required` (pipeline `model_name`s and the repos and single files task steps fetch), `estimate` (with `basis`), `host_memory` (`{projected_mb, basis, runs}`, the projected host-RAM peak; `basis` is `observed` from same-key successful runs on this machine or `fit`; absent when there is no figure) and `elided_steps` for the arguments given; quote from it. `steps` counts what will run: a step nothing reads and which saves no file does not run, and is named in `elided_steps` instead |
-| `list_workspaces()` | — | The server's workspaces and which one this session is using. Each has its own workflows, assets and outputs; the prompt library is shared by all of them. Compact entries are `name`/`default`/`usage` plus any `note` (clipped, `note_truncated` when cut); `detail=true` adds each entry's folder paths |
-| `use_workspace(name)` | `name` | Work in that workspace for the rest of the session - every later call reads and writes there. This is how to keep your work out of another agent's namespace rather than sharing the default one. Checked against the server, so a typo fails here rather than scoping every later call to nothing. On a `dw.serve --mcp` endpoint the pin is shared by every connected client (#298, see the note above the table) - a `warning` field appears when this call overwrote a pin already pointed away from `default` |
-| `create_workspace(name, use=False)` | `name`, `use` | Create a workspace. Pass use=true to switch this session to it as well; otherwise the session stays where it was and the result says so. The result names the workspace (`name`, `default`, `current`, `next`), not its folders - `list_workspaces(detail=true)` is the opt-in for those. `use=true` carries the same mounted-server caveat and `warning` field as `use_workspace` |
-| `copy_to_workspace(destination, runs=None, assets=None, source=None)` | `destination`, `runs`, `assets`, `source` | Copy runs and assets from one workspace into another. Each `runs` entry is `<workflow>/<run id>` as `list_gallery` names it, or a bare `<workflow>` for all its runs; each `assets` entry is a path as after `asset:`, or a folder (`proj/`) for every file under it, keeping its path, note and kept shots. `source` defaults to this session's workspace. Free: no `acknowledged_cost`. Media is hard-linked (a copy across devices falls back to a real copy) and JSON sidecars are always copied, so an edit in one workspace never reaches the other. All or nothing; a run or identical asset the destination already has is skipped as `already_present`, a different file at an asset's path refuses the whole request, so a repeat is idempotent, and a `vN` it already uses is renumbered after its highest and reported in `renumbered`. Warnings: `unresolved_output_refs`, `absolute_paths`, `no_jobs`. A copied run has no job record - it shows in `list_gallery` and resolves as an `output:` reference, not in `list_jobs`. A run whose job is still queued or running is refused. To move, copy then `delete_output` the source runs and `delete_asset` the source assets. See [Workspaces](WORKSPACES.md#copying-between-workspaces) |
-| `delete_workspace(name, acknowledged_cost=False)` | `name`, `acknowledged_cost` | Permanently delete a workspace and everything in it. Refuses without the acknowledgement, reporting what it would remove |
-| `list_assets(limit=50, prefix)` | `limit`, `prefix` | The input media on the server, each with the `asset:` reference a workflow argument carries. Look here before asking for a file - what a workflow needs may already be there. `libraries` names the roots searched and which are writable; `shadowed` lists names a nearer library hides. Compact entries are `name`/`reference`/`kind`/`size`/`origin` plus any `note` (clipped, `note_truncated` when cut); `detail=true` adds `folder`, `mtime`, `url`, `writable`, and `absolute_url`/`used_by` when set. Newest first; `prefix` keeps names starting with it (e.g. `cast/`), `limit` bounds the answer, `total` is the count matched before the cut |
-| `keep_output(name, asset_name=None, overwrite=False, shared=False, workspace=None, note=None)` | `name`, optional `asset_name`, `overwrite`, `shared`, `workspace`, `note` | Keep a generated file as an input asset under a stable `asset:` name, so a later workflow can rely on it. The copy happens on the server: nothing is downloaded or re-uploaded. `note` is kept with the asset; left out, the source file's note is copied. `asset_name` may name a folder and takes the kept file's extension when it has none; `shared=true` keeps it in the library every workspace shares, which is where a recurring cast belongs. `workspace` names the workspace for this one call without switching the session to it - the same pin `run_workflow` takes, so a job run into another workspace stays reachable from the session that queued it |
-| `set_note(target, note=None, workspace=None)` | `target` (a run `<workflow>/<run id>`, a gallery file name, `asset:<name>` or `workspace:<name>`), `note`, `workspace` | Set the note on a run, an output file, an asset or a workspace; null or `""` clears it. Plain text up to 4000 characters saying what the thing is for - what a run tested, why a file is the keeper. Free: no `acknowledged_cost`. Returns `{target, note, note_source}`. `run_workflow(note=...)` sets a run's note at submission and it shows on every file the run wrote; `list_gallery`, `list_assets`, `list_workspaces` and `list_jobs` carry each note clipped to 200 characters (`note_truncated: true` when cut), while `get_job` and `get_gallery_metadata` return it whole. `get_job` shows the run's current note, so one set here after submission replaces the submitted one. `workspace` names the workspace a run or file name is read in for this one call without switching the session to it - the same pin `run_workflow` takes |
-| `upload_asset(file_path=None, content=None, asset_name=None, shared=False, note=None)` | exactly one of `file_path` or `content` (base64), optional `asset_name`, `shared`, `note` (kept with the asset) | Push an image, video or audio file, or a `.cube` 3D LUT for `apply_lut`, into the server's asset library and get back its `asset:` reference. `file_path` is read from the machine the MCP server runs on, so this is how an input reaches a dw.serve running somewhere else - but only when the caller shares a filesystem with that machine. `content` is the alternative for an agent that does not: the bytes travel inline, base64-encoded, in the tool call itself, capped at 4MB (well under `file_path`'s 200MB) because inline bytes compete with the calling agent's own context budget rather than being a bulk-transfer path (#203). `asset_name` is required with `content` (there is no file name to infer one from) and, either way, stores it under a readable name (`cast/priya-voice.wav`) instead of a random one; `shared=true` puts it in the library every workspace shares |
-| `delete_asset(name)` | `name` | Permanently remove one file from the asset library, by the name `list_assets` reports. Deletes from whichever library holds it - this workspace's own before the shared one; one from a read-only examples library is refused. Any workflow still carrying that `asset:` reference stops loading |
-| `save_workflow(name, workflow)` | `name`, `workflow` | Save a workflow into the server's writable workflow directory, overwriting any existing workflow of that name there. A name that currently resolves to a read-only source (an examples directory) is not overwritten - the copy lands in the writable directory and shadows it |
-| `delete_workflow(name)` | `name` | Permanently delete a stored workflow |
+A server can hold several workspaces, each with its own `workflows/`,
+`assets/` and `outputs/`, all sharing one prompt library and one shared asset
+library. `use_workspace` sets the one this session reads and writes; a session
+starts in `default`. See
+[Workspaces](WORKSPACES.md#several-workspaces-on-one-server).
+
+The pin is state on `client.py`'s `DwClient`. A stdio `dw-mcp` has one per
+session, but `dw.serve --mcp` builds one client for every agent it serves, so
+on that surface the pin is **shared by every connected caller** (#298): another
+client's `use_workspace` or `create_workspace(use=true)` moves your pin
+without your session calling either. Both tools add a `warning` to their
+result on a mounted server when they overwrite a pin that pointed somewhere
+other than `default`; the other session gets no warning. A caller that needs
+isolation on a mounted server passes `workspace=` on every call that takes it.
+
+| Tool | Purpose |
+| --- | --- |
+| `validate_workflow(workflow=None, name=None, inline_workflow=None, workflow_path=None, workspace=None, arguments=None)` | Free and instant: schema, real pipeline signatures, references and arguments. Exactly one of `workflow` (inline, an object or a JSON-encoded string) or `name` (a stored workflow); `run_workflow`'s spellings `inline_workflow` and `workflow_path` are accepted too. By name, the workflow file's own directory is the base directory, as a run sees it. `errors` lists every violation with its JSON path (a `previous_result:` naming no earlier step among them); `warnings` covers what runs but is probably wrong (a signature mismatch, a list entry key no step reads, a value the workflow rounds up). Pass the `arguments` you will run with: an undeclared name, a value that will not coerce, a value outside a declared bound, and an `asset:`/`prompt:`/`output:` reference that reaches nothing are each reported at `arguments.<name>`; `checked_arguments` says what was covered. Also checked: a reference set the model would refuse (too many images, videos or audio clips; for MiniMax-H3, audio as the only reference), an unwritable `result.subfolder` or `file_base_name`, and each sub-workflow step. A valid answer carries `plan`: `fingerprint`, step count, list lengths, `downloads_required`, `estimate` (with `basis`), `host_memory` (`{projected_mb, basis, runs}`) and `elided_steps` (steps nothing reads and that save no file, which do not run). `plan` is null when it could not be built |
+| `list_workspaces(detail=False)` | The workspaces and which one this session uses. Entries carry `name`, `default`, `usage` and any `note` (clipped, `note_truncated`); `detail=true` adds each entry's folder paths |
+| `use_workspace(name)` | Work in that workspace for the rest of the session. Checked against the server, so a typo fails here. On a mounted server the pin is shared (above) |
+| `create_workspace(name, use=False)` | Create a workspace; `use=true` also switches to it. The result names the workspace (`name`, `default`, `current`, `next`), not its folders |
+| `copy_to_workspace(destination, runs=None, assets=None, source=None)` | Copy runs (`<workflow>/<run id>`, or `<workflow>` for all its runs) and assets (a path as after `asset:`, or a folder `proj/`, keeping path, note and kept shots) into another workspace. `source` defaults to the session's workspace. No `acknowledged_cost`. Media is hard-linked (a real copy across devices), JSON sidecars are copied. All or nothing: what the destination already has is skipped as `already_present`, a different file at an asset's path refuses the whole request, a `vN` in use is renumbered (`renumbered`). Warnings: `unresolved_output_refs`, `absolute_paths`, `no_jobs`. A copied run has no job record. A run whose job is queued or running is refused. To move, copy then `delete_output` / `delete_asset`. See [Workspaces](WORKSPACES.md#copying-between-workspaces) |
+| `delete_workspace(name, acknowledged_cost=False)` | Permanently delete a workspace and everything in it. Without the acknowledgement the server refuses (409), reporting what it would remove |
+| `list_assets(detail=False, workspace=None, limit=50, prefix=None)` | Input media on the server, each with its `asset:` reference, newest first. Entries carry `name`, `reference`, `kind`, `size`, `origin` and any `note`; `detail=true` adds `folder`, `mtime`, `url`, `writable`, and `absolute_url`/`used_by` when set. `prefix` keeps names starting with it (`cast/`); `total` counts all matches. `libraries` and `shadowed` are as in `list_workflows` |
+| `keep_output(name, asset_name=None, overwrite=False, shared=False, workspace=None, note=None)` | Keep a generated file as an input asset under a stable `asset:` name, copied on the server. `asset_name` may name a folder and takes the file's extension when it has none; `shared=true` keeps it in the shared library, where a recurring cast belongs. `note` defaults to the source file's note |
+| `set_note(target, note=None, workspace=None)` | Set the note (plain text, up to 4000 characters) on a run (`<workflow>/<run id>`), an output file, `asset:<name>` or `workspace:<name>`; null or `""` clears it. Returns `{target, note, note_source}`. Listings clip notes to 200 characters (`note_truncated`); `get_job` and `get_gallery_metadata` return them whole, and `get_job` shows the current note |
+| `upload_asset(file_path=None, content=None, asset_name=None, shared=False, workspace=None, note=None)` | Put an image, video, audio file or `.cube` LUT into the asset library and get its `asset:` reference. Exactly one of `file_path` (read on the machine running the MCP server, up to 200MB) or `content` (base64 inline, up to 4MB, #203; `asset_name` required). The file is stored under the library's `uploads/`, named `asset_name` when given (`cast/priya-voice.wav`, folders allowed, the extension matching the file's kind) and randomly otherwise; `shared=true` uses the shared library |
+| `delete_asset(name, workspace=None)` | Permanently remove one asset, by the name `list_assets` reports, from whichever library holds it (the workspace's own before the shared one). One from a read-only examples library is refused. A workflow still carrying the reference stops loading |
+| `save_workflow(name, workflow=None, patch=None, workspace=None)` | Save into the writable workflow directory, overwriting a workflow of that name there; `name` may include folders. Exactly one of `workflow` (the full document) or `patch` (a JSON Merge Patch, RFC 7396, onto the stored definition: `null` deletes a key, a list is replaced whole). Either may be a JSON-encoded string. A name that resolves to a read-only source is not overwritten: the copy lands in the writable directory and shadows it |
+| `delete_workflow(name, workspace=None)` | Permanently delete a stored workflow. One from a read-only examples directory (`writable: false`) is refused |
 
 ### Prompts
 
-The stored prompt library is the other half of authoring: a workflow
-argument written as `"prompt:name"` or `"prompt:folder/name"` resolves
-against it at load time, so a workflow can be authored and the text it
-references written in the same session.
+A workflow argument written `"prompt:name"` or `"prompt:folder/name"` resolves
+against the stored prompt library at load time, so a workflow and the text it
+references can be written in the same session. This group also holds the
+enhancers and the LoRA catalog.
 
-| Tool | Arguments | Purpose |
-| --- | --- | --- |
-| `list_prompts(tag=None, intended_model=None, include_text=False)` | optional `tag`, `intended_model`, `include_text` | List the stored prompts - description, intended model, tags and `text_chars`, bodies left out; `get_prompt` for one body Each entry carries `origin` and `writable`; `libraries` and `shadowed` are as in `list_workflows` |
-| `get_prompt(name)` | `name` | Get one stored prompt's full definition |
-| `get_prompt_schema()` | — | Get the JSON schema every stored prompt must satisfy. Its own route rather than a name under `/api/prompts`, so a prompt called `schema` cannot shadow it |
-| `save_prompt(name, prompt)` | `name`, `prompt` | Save a prompt, overwriting any prompt of that name. The server validates first, and refuses a `text` that itself begins with a reference prefix (`variable:`, `previous_result:`, `constant:`, `asset:`, `output:`, `prompt:`) |
-| `delete_prompt(name)` | `name` | Permanently delete a stored prompt. A workflow still referencing it will fail to load |
-| `list_enhancers()` | — | List the enhancer presets `enhance_prompt` accepts |
-| `enhance_prompt(idea, preset="h3", model_name=None, device=None, acknowledged_cost=False)` | `idea`, `preset`, optional `model_name` and `device`, `acknowledged_cost` | Expand a short idea into a full prompt with a language model. Queued as an ordinary job, so it passes the gate; the enhanced text is the text file in the finished manifest, readable with `get_output_text` |
-| `list_loras(model=None, workflow=None, status=None, tag=None)` | optional `model`, `workflow`, `status`, `tag` | List the LoRAs tried on a base model - proven, trial or rejected - with `use_when`, `trigger` and `scale`; exact match on the base. Guide: `loras` |
-| `save_lora(name, entry)` | `name`, `entry` | Save a catalog entry, overwriting any entry of that name; promote a trial to `proven` with its job in `evidence` |
-| `recommend_loras(model, query, limit=8)` | `model`, `query`, optional `limit` | Opt-in: catalog LoRAs ranked against a style request, then Hugging Face Hub adapters of that exact base. Queries the Hub (the one tool with `open_world_hint`); no download, no GPU. Hub rows are candidates to trial. A failed or busy Hub (another search already running) returns the catalog rows plus `hub_error`; an empty `hub` with `hub_error` means the search failed |
+| Tool | Purpose |
+| --- | --- |
+| `list_prompts(tag=None, intended_model=None, include_text=False)` | Stored prompts: `description`, `intended_model`, `tags`, `text_chars`, `origin`, `writable`; `libraries` and `shadowed` as in `list_workflows`. Bodies only with `include_text=true`, which for the whole library is too large; filter first |
+| `get_prompt(name)` | One stored prompt's full definition |
+| `get_prompt_schema()` | The JSON schema a stored prompt must satisfy, on its own route so a prompt named `schema` cannot shadow it |
+| `save_prompt(name, prompt)` | Save a prompt, overwriting one of that name. The server validates it, and refuses a `text` that begins with a reference prefix (`variable:`, `previous_result:`, `constant:`, `asset:`, `output:`, `prompt:`) |
+| `delete_prompt(name)` | Permanently delete a stored prompt. A workflow referencing it fails to load |
+| `list_enhancers()` | The presets `enhance_prompt` accepts |
+| `enhance_prompt(idea, preset="h3", model_name=None, device=None, acknowledged_cost=False)` | Expand a short idea into a full prompt with a language model. Queued as an ordinary job behind the cost gate; the text is the text file in the finished manifest (`get_output_text`) |
+| `list_loras(model=None, workflow=None, status=None, tag=None)` | LoRAs tried on a base model (proven, trial or rejected) with `use_when`, `trigger` and `scale`; exact match on the base. Guide: `loras` |
+| `save_lora(name, entry)` | Save a catalog entry, overwriting one of that name; promote a trial to `proven` with its job in `evidence` |
+| `recommend_loras(model, query, limit=8)` | Catalog LoRAs ranked against a style request, then Hugging Face Hub adapters of that exact base. The one tool that queries the Hub (`open_world_hint`); no download, no GPU. A failed or busy Hub returns the catalog rows plus `hub_error` |
 
 ### Diagnose
 
-| Tool | Arguments | Purpose |
-| --- | --- | --- |
-| `run_workflow(workflow_path=None, inline_workflow=None, arguments=None, acknowledged_cost=False, workspace=None, wait_seconds=0, note=None)` | exactly one of `workflow_path` (a catalog name from `list_workflows`, with or without `.json`, or a path to a workflow file on the server) or `inline_workflow`, optional `arguments`, `acknowledged_cost`, `workspace`, `wait_seconds`, `note` | Queue a workflow for generation. Returns as soon as the job is queued - unless `wait_seconds` is above 0, in which case the call then waits on the queued job exactly as `wait_for_job(job_id, timeout_seconds=wait_seconds)` would (same per-call cap, clamped not honoured) and the result carries the queued-job fields plus that wait's (`status`, `still_running`, `waited_seconds`, `timeout_requested_seconds`, `timeout_applied_seconds`, `timeout_capped`, the slim `job`); when the cap covers the job's runtime one call is the run and the wait, and a `still_running: true` result is followed with `wait_for_job` as before. `workspace` names the workspace for this one call without switching the session to it - use it to pin a job whose `output:` or `asset:` references live in a workspace other than the session's - `acknowledged_cost` is `true` or the bound `{fingerprint, minutes, downloads}` from the validate plan; a 409 means the plan changed and the message carries the new estimate, and nothing is waited on. `note` says what the run is for (see `set_note`) |
-| `get_job(job_id)` | `job_id` | Get a job's status, warnings, output manifest, error and traceback; each manifest entry's `subfolder` is the in-run subfolder the step declared - by convention `final` for the deliverable, `intermediate` for scratch, `''` for none. A running job also carries `progress` (below); a queued job held for host RAM carries `waiting_for` (#869) |
-| `get_job_workflow(job_id)` | `job_id` | The REST equivalent is `GET /api/jobs/{id}/workflow` (see [SERVER.md](SERVER.md#jobs-api)). The workflow the job actually ran. `realized: true` means every mutable input is pinned (arguments, seed, prompts, `output:latest`); `false` means the job predates run tracking and this is the definition as submitted. Pass it to `save_workflow` to keep it under a name |
-| `export_job(job_id, overwrite=False)` | `job_id`, `overwrite` | Gather one finished job into `<workspace>/exports/<job id>/` on the server: the realized workflow, the run's manifest, the job row, a README, and copies of the assets, earlier-run inputs and outputs. Returns the directory, a zip URL, the file list with sizes and the total. The three JSON files are in the zip, not repeated here - get_job_workflow and get_job serve them individually. **The directory is on the machine running the server**, like `download_output`'s destination. The zip needs no token (`/exports/*.zip` is ungated like `/outputs`, #592), so `auth_required` is false and the agent fetches `open_url` itself (prefixing a relative one with the server address) and unpacks it into `exports/` under the session's working directory (a deliverable, not a temp file) - the archive already unpacks into one folder named after the job id, so do not create that folder first; only an agent without HTTP gives the user `open_url`. `true` is kept as a forward guard for a gated zip, when `open_url` is handed to the person instead |
-| `get_job_events(job_id, after=-1, limit=200)` | `job_id`, `after`, `limit` | Get a page of a job's progress events |
-| `wait_for_job(job_id, timeout_seconds=20)` | `job_id`, `timeout_seconds` | Block until a job reaches a terminal status, or `timeout_seconds` elapses. **One call blocks for at most the server's cap** — 55 seconds unless the deployment sets `DW_MCP_MAX_WAIT_SECONDS` higher, and the tool's description states the live value; a larger `timeout_seconds` is clamped, not honoured. Ask for the job's `plan.estimate` plus a margin: under the cap that is one call for the whole job, over it one call per cap. A deployment raises the cap only as far as its clients (and anything between them and the server) hold one silent HTTP request open - Claude Code's limit is `MCP_TOOL_TIMEOUT`. Every reply carries `waited_seconds`, `timeout_requested_seconds`, `timeout_applied_seconds` and `timeout_capped`, so a capped return is distinguishable from an elapsed one. Use instead of hand-polling `get_job`/`get_job_events` in a loop; if it returns `still_running: true`, call it again. Returns a slim job - status, warnings, error, `run_id` and `run_version` (the run's `v5`, as the gallery labels it), and the manifest once finished - without the arguments; `get_job` has those. A running job also carries `progress` (below); a queued job held for host RAM carries `waiting_for` |
-| `cancel_job(job_id)` | `job_id` | Ask a queued or running job to stop |
-| `clear_memory()` | `device` | Drop every loaded pipeline and the step cache on each idle card - `device`'s alone when named - freeing VRAM/RAM immediately instead of waiting for the next job to evict one model for another. Also drops the step cache, so a seeded workflow that would otherwise reuse cached results regenerates on its next run. A card running a job is skipped (`workers` reports it `job_running`); refused with a 409 when no card asked about is idle, so wait and retry rather than expecting this call to block (#221, #677) |
-| `rerun_job(job_id, acknowledged_cost=False, new_seed=False)` | `job_id`, `acknowledged_cost`, `new_seed` | Queue a fresh job from a previous job's stored specification. Costs GPU time, so it passes the same gate as `run_workflow`. `new_seed=true` draws a fresh seed into the workflow's seed variable — without it a seeded workflow's rerun repeats its arguments exactly and the step cache serves the whole run from the earlier one's files (`reused: true`), generating nothing. `get_job_workflow`'s `seed_variable` says whether there is one - `acknowledged_cost` is `true` or the bound `{fingerprint, minutes, downloads}` from the validate plan; a 409 means the plan changed and the message carries the new estimate |
-| `move_job(job_id, direction)` | `job_id`, `direction` (`up`\|`down`\|`front`\|`back`) | Reorder a queued job |
+| Tool | Purpose |
+| --- | --- |
+| `run_workflow(workflow_path=None, inline_workflow=None, workflow=None, name=None, arguments=None, acknowledged_cost=False, workspace=None, wait_seconds=0, note=None)` | Queue a workflow. Exactly one of `workflow_path` (a catalog name from `list_workflows`, with or without `.json`, or a path on the server) or `inline_workflow` (an object or JSON-encoded string); `validate_workflow`'s spellings `name` and `workflow` are accepted too. `arguments` overrides variables by name and is checked as `validate_workflow` checks it; a bad one is refused before queueing. `acknowledged_cost` is `true` or the bound `{fingerprint, minutes, downloads}` from the validate plan; a 409 means the plan changed and carries the new estimate. Returns once queued, unless `wait_seconds` > 0: then it waits as `wait_for_job(job_id, timeout_seconds=wait_seconds)` would, under the same cap, and adds that wait's fields. `note` says what the run tests (see `set_note`) |
+| `get_job(job_id)` | Status, warnings, output manifest, error and traceback. Each manifest entry's `subfolder` is the one the step declared. A running job carries `progress`; a job held for host RAM carries `waiting_for` (#869) |
+| `get_job_workflow(job_id)` | The workflow the job ran (`GET /api/jobs/{id}/workflow`, [SERVER.md](SERVER.md#jobs-api)). `realized: true` pins every mutable input (arguments, seed, prompts, `output:latest`); `false` is the submitted definition of a job from before run tracking. `seed_variable` names the variable `rerun_job(new_seed=true)` draws into. Pass it to `save_workflow` to keep it |
+| `export_job(job_id, overwrite=False)` | Gather a finished job into `<workspace>/exports/<job id>/` **on the server machine**: the realized workflow, the manifest, the job row, a README, and copies of its assets, earlier-run inputs and outputs (`total_bytes` is what was copied). Returns the directory, a zip URL, the file list and the total. The zip `open_url` needs no token (#592): fetch it (prefixing a relative URL with the server address) and unpack it into `exports/` in the session's working directory; it unpacks into one folder named after the job id. `auth_required: true` would mean a gated zip, to hand to the person. Refuses a running job, and an existing export without `overwrite=true` |
+| `get_job_events(job_id, after=-1, limit=200, kinds=None)` | A page of progress events. `after` is exclusive: pass back `last_seq`. Each event's `at` is seconds since the job started. `kinds` (e.g. `["log", "warning"]`) filters the page, which `memory` events otherwise dominate. A `phase_stall` entry is a watchdog notice every ~30 s of a quiet phase, not evidence of a hang |
+| `wait_for_job(job_id, timeout_seconds=20)` | Block until the job is terminal or `timeout_seconds` elapses. One call blocks for at most the server's cap (`DW_MCP_MAX_WAIT_SECONDS`; the tool's description states the live value); a larger timeout is clamped, and every reply carries `waited_seconds`, `timeout_requested_seconds`, `timeout_applied_seconds` and `timeout_capped`. On `still_running: true`, call again. A deployment raises the cap only as far as its clients hold a silent HTTP request open (Claude Code: `MCP_TOOL_TIMEOUT`). Returns a slim job: status, warnings, error, `run_id`, `run_version`, the manifest once finished, `progress` while running, `waiting_for` when held |
+| `cancel_job(job_id)` | Ask a queued or running job to stop. A running job stops at the next denoise or step boundary |
+| `clear_memory(device=None)` | Drop every loaded pipeline and the step cache on each idle card (`device`'s alone when named), so a seeded workflow regenerates on its next run. A busy card is skipped (`workers` reports `job_running`); a 409 when no card asked about is idle (#221, #677) |
+| `rerun_job(job_id, acknowledged_cost=False, new_seed=False)` | Queue a fresh job from a previous job's stored specification, behind the same gate as `run_workflow`. Without `new_seed=true` a seeded rerun repeats its arguments exactly and the step cache serves it from the earlier files (`reused: true`) |
+| `move_job(job_id, direction)` | Reorder a queued job: `direction` is `up`, `down`, `front` or `back` |
 
 ### Models
 
-| Tool | Arguments | Purpose |
-| --- | --- | --- |
-| `list_models()` | — | (Catalog) List what the Hugging Face cache holds, largest first |
-| `download_model(repo_id, acknowledged_cost=False)` | `repo_id`, `acknowledged_cost` | Fetch a model repo into the cache. Costs disk and bandwidth, so it passes the gate. Returns as soon as the download starts. It fetches the whole repo at `main`, so it does not pre-fetch a single pinned file a task entry names; the task fetches that on its first run |
-| `list_downloads()` | — | List downloads the server is running or recently ran |
-| `cancel_download(download_id)` | `download_id` | Stop a running download; the record shows `cancel_requested: true` until it is `cancelled`. Partial files stay cached and resume on a retry |
-| `delete_model(repo, acknowledged_cost=False)` | `repo`, `acknowledged_cost` | Delete every cached revision of one repo. Not recoverable locally |
-| `get_diffusers_state()` | — | Installed diffusers version, its git commit, and any update in flight |
-| `update_diffusers(acknowledged_cost=False)` | `acknowledged_cost` | Upgrade diffusers to GitHub HEAD in the background |
+`list_models` (above, under Catalog) reports the cache.
+
+| Tool | Purpose |
+| --- | --- |
+| `download_model(repo_id, acknowledged_cost=False)` | Fetch a whole model repo at `main` into the cache; returns once the download starts. It does not pre-fetch a single pinned file a task names; the task fetches that on its first run |
+| `list_downloads()` | Downloads running or recently run |
+| `cancel_download(download_id)` | Stop a running download; the record shows `cancel_requested: true` until `cancelled`. Partial files stay cached and resume on a retry |
+| `delete_model(repo, acknowledged_cost=False)` | Delete every cached revision of one repo. Not recoverable locally |
+| `get_diffusers_state()` | Installed diffusers version, its git commit, and any update in flight |
+| `update_diffusers(acknowledged_cost=False)` | Upgrade diffusers to GitHub HEAD in the background |
 
 The server refuses `delete_model` and `update_diffusers` while a job is
-running or queued, and `delete_model` while a download is active — pulling
-files or package contents out from under a loaded pipeline is the same
-hazard twice. That refusal arrives as the server's own explanation.
+running or queued, and `delete_model` while a download is active, and says
+why.
 
 ## The cost gate
 
-Seven tools refuse unless `acknowledged_cost=true` is passed. Each commits
-the machine to something the user would want to have been asked about first,
-and each says so in its own words — a single shared refusal would be wrong
-for each of them in a different way, and a gate the user learns to wave
-through is not a gate.
+Seven tools refuse unless `acknowledged_cost` is set. Each refuses in its own
+words.
 
 | Tool | What it commits |
 | --- | --- |
-| `run_workflow` | Minutes of GPU time; the engine runs one job per GPU, so a run holds a card the queue could give another job |
-| `rerun_job` | The same run, from a stored spec |
+| `run_workflow` | Minutes of GPU time on a card the queue could give another job |
+| `rerun_job` | The same run, from a stored spec; ungated, any job id would bypass the gate |
 | `download_model` | Tens of gigabytes of network and disk |
-| `delete_model` | Cached weights, unrecoverably — getting them back means downloading again |
+| `delete_model` | Cached weights, unrecoverably |
 | `update_diffusers` | Replacing the installed library with an untagged development build |
-| `enhance_prompt` | A real job on the one-at-a-time engine, delaying any generation behind it |
+| `enhance_prompt` | A real job, delaying any generation behind it |
 | `delete_workspace` | Every workflow, asset and generated file in a workspace, unrecoverably |
 
-`rerun_job` is gated for the same reason as `run_workflow`: it queues the
-identical work, so leaving it open would make the gate worth nothing — any
-job id from `list_jobs` would buy a way around it. `cancel_job` and
-`cancel_download` are deliberately *not* gated: they end a cost rather than
-starting one, and gating them would make the safe direction the harder one.
-
-The acknowledgement can be bound to what was quoted. `validate_workflow`
-answers with a `plan`; pass `acknowledged_cost={"fingerprint":
-plan.fingerprint, "minutes": plan.estimate.minutes, "downloads": [...]}` and
-the server refuses with 409 if the run's shape changed between the quote and
-the call - a longer list, a stored prompt edited meanwhile, weights that now
-have to be downloaded - naming the new plan so the agent re-quotes. The
-`plan` itself, passed unedited, binds the same way: with no `downloads` key,
-its `downloads_required` repos (a file entry by its repo) and `estimate.minutes` stand in. Bare
-`true` still works and is for a `plan` that came back null; the job records
-which form it got (`acknowledged: none | boolean | bound`).
-
-Passing the flag does not make a tool wait. The five that start work return
-as soon as it is queued or started, the same way queuing a job from the web UI
-does not block the browser tab; `delete_model` and `delete_workspace` are
-deletions rather than queued work and complete before they answer.
-
-The intended loop:
-
-1. `validate_workflow` — free, checks schema and pipeline signatures, no GPU
-   time spent. Pass the `arguments` you intend to run with: without them the
-   verdict covers the stored definition and its stock defaults, not the
-   values you wrote, and its `plan` is the number to say out loud:
-   `estimate.minutes` with its `basis`, plus each `downloads_required`
-   entry as a line item of its own (task steps' repos and single files are included)
-2. `run_workflow` with `acknowledged_cost` bound to the plan (`{fingerprint,
-   minutes, downloads}`), or `true` when there was no plan — pass a name straight from
-   `list_workflows` as `workflow_path`; queues the job and returns
-   immediately with a `job_id`
-3. `wait_for_job(job_id, timeout_seconds=...)` to block instead of
-   hand-polling, asking for the plan's estimate plus a margin; one call
-   covers at most the server's cap (`timeout_applied_seconds` and
-   `timeout_capped` say what you got), so call it again if it comes back
-   `still_running: true` — or
-   `get_job_events(job_id)` repeatedly, passing back the previous call's
-   `last_seq` as `after`, for incremental progress instead of just a
-   terminal/not-terminal status. Each event carries `at`, seconds since the
-   job started, so where a step's time went is a subtraction between two
-   events - `step_start` to `generating` is the lead-in a reused pipeline
-   still pays; `generating` is emitted when the pipeline call starts, so
-   `generating` to the first `pipeline_step` is the encoding plus the first
-   denoise pass (`timing.encode_s` on the manifest entry is the encoding alone)
-4. `get_job(job_id)` for the finished manifest (or the error and traceback,
-   if it failed). A pipeline step's entry carries `timing` - load, encode,
-   denoise, decode and save seconds ([WORKSPACES.md](WORKSPACES.md#step-timing))
-5. `get_output_image(name)` to look at a result image
-6. `get_output_frames(name, count=12)` to look at a result video, and
-   `get_output_audio(name, start, duration)` to hear it
-
-While a job runs, `get_job` and `wait_for_job` carry a `progress` block -
-the step being run, the phase (`loading`, `generating`, `decoding`,
-`saving`) with the model in `phase_detail`, `seconds_in_phase`,
-`seconds_since_event`, and `denoise_step`/`denoise_total_steps`, which are
-null until the denoise loop starts. A single-step generation is minutes of
-one phase, so two polls otherwise come back identical: read `denoise_step`
-moving (slow but healthy) against a `denoise_step` that is a number and
-stays put while `seconds_since_event` climbs (nothing is happening). A null
-`denoise_step` under `generating` is neither - it is the lead-in the
-pipeline runs before the loop, encoding the prompt and every reference and
-then the first denoise pass, with nothing emitted, so silence there is
-expected. On MiniMax H3 the first pass dominates the lead-in, not the encode,
-and it varies by card. The reference encode (`vae_encoder`) is about 6 s for
-an image reference and about 50-53 s for a 5 s 960x544 video reference;
-reference tokens make the first pass slower. Measured on lem: an image
-reference's lead-in was 187 s (job `f90a041af26a`: `timing.encode_s` 26 s,
-first pass 161 s; of the encode, `vae_encoder` was 6 s); the same image +
-video job took 508 s on cuda:1 (`f5aefcdd5e26`: `encode_s` 86 s, first pass
-422 s; `vae_encoder` 59 s) and 165 s on cuda:0 (`6961876f11d3`: `encode_s`
-79 s, first pass 86 s; `vae_encoder` 56 s). Plan for several minutes, up to
-~8.5 min measured (508 s, `f5aefcdd5e26`), with a video reference. `get_job_events` names the block it is in
-while that runs - a `log` line per top-level block of a modular pipeline
-(`MiniMaxAI/MiniMax-H3: vae_encoder`), which is the difference between
-silence and knowing it is encoding the reference. And once the counter is a number the gaps
-between steps are uneven wherever a transformer block cache is configured -
-cheap cached steps, then a full one - so a 140 s gap on H3 is a healthy run;
-read liveness as the counter moving between polls minutes apart rather than
-as silence under a threshold. `cancel_job` stops at the next denoise or
-step boundary, which `denoise_step` is also the measure of.
-
-The other frozen-counter stretch is at the end: under `saving`, `denoise_step`
-sits at a completed-looking `8/8` and cannot move again, because the step is
-writing files. `get_job_events` carries a `log` per file there too - named as
-the write starts (`writing shot.mp4 (121 frames)`) and costed as it finishes
-(`wrote shot.mp4 in 1.3s (1.4 MB)`) - so that stretch is attributable rather
-than silent (#97).
+`cancel_job` and `cancel_download` are not gated: they end a cost.
+
+The acknowledgement can be bound to the quote. Pass
+`acknowledged_cost={"fingerprint": plan.fingerprint, "minutes":
+plan.estimate.minutes, "downloads": [...]}` from `validate_workflow`'s `plan`,
+and the server refuses with 409, naming the new plan, if the run's shape
+changed since (a longer list, an edited stored prompt, weights that now need
+downloading). The `plan` passed unedited binds the same way: with no
+`downloads` key, its `downloads_required` repos and `estimate.minutes` stand
+in. Bare `true` is for a `plan` that came back null. The job records the form
+it got (`acknowledged: none | boolean | bound`).
+
+The flag does not make a tool wait. The five that start work return once it is
+queued or started; `delete_model` and `delete_workspace` complete before they
+answer.
+
+The loop ([The loop](WORKFLOW_GUIDE.md#the-loop) is the full version):
+
+1. `validate_workflow` with the `arguments` you will run with. Quote its
+   `plan`: `estimate.minutes` with its `basis`, and each `downloads_required`
+   entry (task repos and single files included) as its own line.
+2. `run_workflow` with `acknowledged_cost` bound to the plan (or `true` when
+   there was none); it returns a `job_id`.
+3. `wait_for_job(job_id, timeout_seconds=...)`, asking for the estimate plus a
+   margin and calling again on `still_running: true`; or `get_job_events`,
+   passing back `last_seq` as `after`, for incremental progress.
+4. `get_job(job_id)` for the manifest, or the error and traceback. A pipeline
+   step's entry carries `timing` (load, encode, denoise, decode and save
+   seconds; [WORKSPACES.md](WORKSPACES.md#step-timing)).
+5. `get_output_image(name)`, `get_output_frames(name, count=12)` and
+   `get_output_audio(name, start, duration)` to judge the result.
+
+While a job runs, `get_job` and `wait_for_job` carry `progress`: the step, the
+phase (`loading`, `generating`, `decoding`, `saving`) with the model in
+`phase_detail`, `seconds_in_phase`, `seconds_since_event`, and
+`denoise_step`/`denoise_total_steps` (null until the denoise loop starts). A
+run is healthy while `denoise_step` moves between polls minutes apart, and
+stuck when it is a number that stays put while `seconds_since_event` climbs.
+Two silent stretches are normal: the lead-in under `generating` with a null
+`denoise_step` (prompt and reference encoding plus the first denoise pass;
+several minutes on MiniMax H3 with a video reference), and `saving`, where the
+counter sits at its total. `get_job_events` carries a `log` line per top-level
+modular block (`MiniMaxAI/MiniMax-H3: vae_encoder`) and per file written
+(`writing shot.mp4 (121 frames)`, `wrote shot.mp4 in 1.3s (1.4 MB)`, #97).
+Gaps between denoise steps are uneven under a transformer block cache.
+Measured lead-in figures and every field are in
+[Progress on a running job](SERVER.md#progress-on-a-running-job) and
+[Progress events](SERVER.md#progress-events).
 
 ## Security
 
-The MCP server adds no authentication of its own — it inherits the REST
-API's posture exactly, described in full in [Security](SECURITY.md):
-localhost binding, no auth, `Origin` header checks, and path confinement in
-`dw/security.py` for every workflow, gallery and prompt path a tool touches.
-Nothing under `dw_mcp/` re-implements or loosens that confinement; it is
-purely a client of the same validated endpoints the web UI uses - except for
-`download_output`, the one tool that writes a local file for the MCP client
-rather than only reading through the API. Over a stdio `dw-mcp` it may write
-anywhere the client's own filesystem lets it (a full path, a directory, or
-the current working directory by default, `~` expanded), the way a shell
-redirect would for the same user; a `..` path segment in `destination` is
-refused, and an existing file is left alone unless the caller passes
-`overwrite=True`.
-
-Over `dw.serve --mcp` the write happens **on the server**, and there the
-destination is confined to that workspace: an absolute or `~` path outside
-it is refused, and a relative one is joined onto the workspace rather than
-onto whatever the server process's working directory happens to be.
-`destination` is required over this transport - an omitted one is refused
-rather than dropped loose in the workspace root, where nothing can find or
-delete it later (#353). The
-transport is what distinguishes the two - on stdio "local disk" is genuinely
-the caller's own machine, over HTTP it is the operator's. Confinement is on
-the resolved real path, not a substring test, because an absolute path needs
-no `..` to reach anywhere the server can write.
-
-`dw-mcp` may be pointed at a `dw.serve` on another machine only when that
-server was started with a token, and the same token is passed here
-(`--token` / `DW_API_TOKEN`); it refuses to start otherwise. The token is
-the only authentication, and the connection is plaintext HTTP - use it on
-a network you control, or through Tailscale or a TLS proxy beyond that.
-[REMOTE.md](REMOTE.md) has the full setup. The same applies to
-`dw.serve --mcp`, which serves this tool surface itself at `/mcp` behind
-the same token; in that setup `download_output` writes on the server
-machine, not the client's.
-
-`save_workflow` and `run_workflow` together let an MCP client write and
-then execute a workflow it authored - and a workflow JSON file can execute
-arbitrary Python (see [Trust model](SECURITY.md#trust-model)). What
-protects a `dw.serve` an MCP client talks to is the server's own
-`--trust-workflows` flag, off by default: run `dw-serve` without it (the
-default) for any server an MCP client can reach.
+`dw_mcp` adds no authentication of its own and inherits the REST API's
+posture, described in [Security](SECURITY.md): an opt-in bearer token
+(`--token` / `DW_API_TOKEN`) on `/api/*` and `/mcp`, `Origin` and `Host`
+header checks, and `dw/security.py` path confinement for every workflow,
+gallery, asset and prompt path a tool sends. Nothing under `dw_mcp/` loosens
+that confinement.
+
+Two tools touch a file outside the API, and whose machine "local" is depends
+on the transport:
+
+- **`download_output`** writes a file. Over stdio `dw-mcp` it writes anywhere
+  the user's own process may, like a shell redirect. Over `dw.serve --mcp` it
+  writes on the server, confined to the workspace: an absolute or `~` path
+  outside it is refused, a relative one is joined onto the workspace, and
+  `destination` is required (#353, #113). Containment is checked on the
+  resolved real path. On both transports a `..` segment is refused and an
+  existing file needs `overwrite=True`.
+- **`upload_asset(file_path=...)`** reads a file. Over stdio it reads what the
+  user may. Over `dw.serve --mcp` `file_path` is confined to the server's
+  workspace, workflows, assets, outputs and prompts directories, and the
+  refusal comes before the file is looked for, so it is not a path-existence
+  oracle (#138). `content` names no path and is not confined.
+
+`dw-mcp` reaches a `dw.serve` on another machine only when that server has a
+token and the same token is passed here; it refuses to start otherwise. The
+token is the only authentication and the connection is plaintext HTTP: use a
+network you control, or Tailscale or a TLS proxy beyond that
+([REMOTE.md](REMOTE.md)). `dw.serve --mcp` serves this tool surface at `/mcp`
+behind the same token, and refuses to start on a non-loopback host without one.
+
+`save_workflow` and `run_workflow` let an MCP client write and then run a
+workflow, and a workflow can execute arbitrary Python
+([Trust model](SECURITY.md#trust-model)). What protects the server is its own
+`--trust-workflows` flag, off by default: leave it off on any server an MCP
+client can reach.
 
 ## Known limits
 
-- **Event history is bounded.** `get_job_events` serves at most the last 200
-  events of a finished job (`MAX_PERSISTED_EVENTS` in the job history store).
-  A job that ran before this feature existed returns an empty event list
-  with a `note` explaining why.
-- **Images, sound and frames.** `get_output_image` returns an image,
-  `get_output_audio` a soundtrack (an audio file's, or the one muxed into a
-  video) whole or as a named excerpt, and `get_output_frames` frames of a
-  video as images - there is no video content type over MCP, so a video is
-  seen as frames and heard as its track. `get_output_audio` refuses a whole
-  clip whose base64 size would exceed the same 4MB budget; ask for an
-  excerpt instead.
-- **Uploads read the MCP server's disk, unless sent inline.**
-  `upload_asset(file_path)` pushes a local file into the asset library, but
-  "local" means the machine `dw-mcp` runs on. Over `dw.serve --mcp` that is
-  the GPU box, so a file sitting on the client's laptop is not reachable that
-  way - put it on the server, give the workflow a URL (the arguments that
-  take a path take a URL too), or use `upload_asset(content=..., asset_name=...)`
-  to send the bytes inline instead, base64-encoded in the call itself, capped
-  at 4MB (#203). On a `--mcp` endpoint `file_path` is also *confined* to the
-  directories the server works in (its workspace, workflows, assets, outputs
-  and prompts), and the refusal comes before the file is looked for, so the
-  tool cannot be used to probe which paths exist on the box (#138); `content`
-  reads no path and is not subject to this confinement, since no file on
-  either machine is ever named.
-  `download_output` has the same asymmetry in the other direction: on a
-  `--mcp` endpoint it writes on the GPU box, not the client's machine, and is
-  confined to the workspace there.
-- **Prompts are not per-workspace.** Switching workspaces changes which
-  workflows, assets and outputs the session sees; the prompt library is one
-  library shared by all of them, because `prompt:` is shared by reference.
+- **Event history is bounded.** A finished job keeps its last 200 events
+  (`MAX_PERSISTED_EVENTS`). A job from before event persistence returns an
+  empty list with a `note`.
+- **No video content type.** A video is seen as frames (`get_output_frames`)
+  and heard as its track (`get_output_audio`); a whole clip over the 4MB
+  budget is refused, so ask for an excerpt.
+- **Files cross machines only by transport.** Over `dw.serve --mcp`,
+  `upload_asset(file_path)` and `download_output` act on the GPU box, not the
+  client. Send a client file with `upload_asset(content=..., asset_name=...)`
+  (4MB cap) or give the workflow a URL; fetch results by their `url`.
+- **Prompts are not per-workspace.** The prompt library is one library shared
+  by every workspace, because `prompt:` is shared by reference.
 
 ## Troubleshooting
 
 | Symptom | Likely cause |
 | --- | --- |
-| `Failed to reconnect to dw: ENOENT` (or the client cannot start the server) | The client cannot find the command. Register the venv's **absolute** path to `dw-mcp`, not the bare name - see [Use the absolute path](#use-the-absolute-path-to-dw-mcp). A bare name works only when the client was launched from an activated venv, so this often appears in a second terminal after the first one worked |
-| The server shows connected, but every tool fails | "Connected" means the `dw-mcp` process launched, not that the engine is reachable. Check `dw.serve` is running |
-| "Cannot reach diffusers-workflow at …" | `dw.serve` is not running. Start it with `dw-serve` (or `python -m dw.serve`) and try again |
-| A config change seems to have no effect | A running session holds the old config. Start a new session |
-| It worked, then broke after rebuilding the venv | Re-run `pip install -e ".[server,mcp]"`. If the repo moved or was renamed, re-register the server with the new absolute path |
-| A tool call times out | Usually a model loading into VRAM/RAM for the first time; retry, or raise `--timeout` |
-| `run_workflow` or `rerun_job` refuses with a cost message | Not an error — it is the `acknowledged_cost` gate. Confirm with the user and call again with `acknowledged_cost` bound to the plan (or `true`); a 409 "shape changed" answer means the run grew since the quote - re-validate, re-quote, pass the new plan |
+| `Failed to reconnect to dw: ENOENT` (or the client cannot start the server) | The client cannot find the command. Register the venv's **absolute** path to `dw-mcp` ([Use the absolute path](#use-the-absolute-path-to-dw-mcp)). A bare name works only when the client was launched from an activated venv |
+| The server shows connected, but every tool fails | "Connected" means `dw-mcp` launched, not that the engine is reachable. Check `dw.serve` is running |
+| "Cannot reach diffusers-workflow at …" | `dw.serve` is not running. Start it with `dw-serve` (or `python -m dw.serve`) |
+| `dw-mcp` exits 2 at startup | A non-loopback `--url` with no token, or the startup probe failed against a remote URL (unreachable, or the token was rejected with a 401) |
+| A config change has no effect | A running session holds the old config. Start a new session |
+| It broke after rebuilding the venv | Re-run `pip install -e ".[server,mcp]"`. If the repo moved, re-register the new absolute path |
+| A tool call times out | One API request exceeded `--timeout` (30 s); raise it. For a long job use `wait_for_job`, which returns at its cap rather than timing out |
+| `run_workflow` or `rerun_job` refuses with a cost message | The `acknowledged_cost` gate. Confirm with the user and call again with `acknowledged_cost` bound to the plan (or `true`); a 409 means the run changed since the quote: re-validate, re-quote, pass the new plan |
diff --git a/docs/PROMPT_WEIGHTING.md b/docs/PROMPT_WEIGHTING.md
index 8ba6912a..5cae47ad 100644
--- a/docs/PROMPT_WEIGHTING.md
+++ b/docs/PROMPT_WEIGHTING.md
@@ -1,6 +1,6 @@
 # Prompt Weighting
 
-Enable A1111-style prompt weighting to control emphasis on individual words or phrases. Also supports prompts longer than the standard 77-token CLIP limit.
+A1111-style prompt weighting sets the emphasis of individual words or phrases, and lifts the 77-token CLIP limit on the prompt (`dw/prompt_weighting.py`, after sd_embed). Flux-family pipelines only.
 
 ## Enabling
 
@@ -39,34 +39,19 @@ Weights are multiplicative when nested: `(((word:1.3)))` = 1.3 x 1.1 x 1.1 = 1.5
 
 ## How It Works
 
-When enabled, prompts containing weighting syntax are intercepted before pipeline execution:
+Before the pipeline call, a `prompt` string containing `(` or `[` is parsed into weighted tokens, run through the CLIP and T5 encoders with the weights applied, and replaced by `prompt_embeds` and `pooled_prompt_embeds`. A prompt with neither character passes through as a plain string, and so does a `prompt` that is not a string.
 
-1. The prompt string is parsed for weight tokens
-2. Tokens are run through the pipeline's text encoders with per-token weights applied
-3. The resulting embedding tensors replace the `prompt` string argument
-4. The pipeline receives `prompt_embeds` and `pooled_prompt_embeds` instead
-
-Prompts without any weighting syntax (`(`, `[`) pass through unchanged as plain strings.
-
-An optional `prompt_2` argument (Flux's T5 prompt, normally defaulting to `prompt`) is also consumed and weighted separately when present.
-
-Because diffusers rejects mixing a string `negative_prompt` with `prompt_embeds`, any `negative_prompt` argument is silently dropped once weighting kicks in.
+- `prompt_2` (Flux's T5 prompt, defaulting to `prompt`) is consumed and weighted separately when present.
+- diffusers rejects a string `negative_prompt` beside `prompt_embeds`, so a `negative_prompt` is dropped once weighting applies, with only a debug log line.
 
 ## Supported Pipelines
 
-Explicitly supports:
-
-- FluxPipeline
-- FluxImg2ImgPipeline
-- FluxInpaintPipeline
-- FluxControlNetPipeline
-
-Any other pipeline whose class name starts with `Flux` (e.g. `FluxKontextPipeline`, `FluxFillPipeline`, a custom subclass) is also supported automatically, provided it carries the same CLIP + T5 encoder stack (`tokenizer`, `tokenizer_2`, `text_encoder`, `text_encoder_2`). Non-Flux pipelines are not currently supported — the prompt is left as a plain string and a warning is logged.
+`FluxPipeline`, `FluxImg2ImgPipeline`, `FluxInpaintPipeline` and `FluxControlNetPipeline` by name, and any other pipeline whose class name starts with `Flux` (`FluxKontextPipeline`, `FluxFillPipeline`, a subclass) that carries the same CLIP + T5 stack: `tokenizer`, `tokenizer_2`, `text_encoder`, `text_encoder_2`. On any other pipeline the prompt stays a plain string and a warning is logged.
 
 ## Requirements
 
-- The pipeline's text encoders must be loaded (not set to `null`)
-- Cannot be used with `remote_text_encoder` (mutually exclusive — `remote_text_encoder` takes precedence if both are set)
+- The pipeline's text encoders must be loaded, not set to `null`.
+- Mutually exclusive with `remote_text_encoder`, which wins if both are set.
 
 ## Example
 
@@ -75,4 +60,4 @@ python -m dw.run workflows/templates/prompt-weighting.json \
     prompt="a (cinematic:1.5) shot of a (dragon:1.3) breathing [smoke] over a (medieval:0.8) castle"
 ```
 
-See [prompt-weighting.json](../workflows/templates/prompt-weighting.json).
+See [prompt-weighting.json](../workflows/templates/prompt-weighting.json) (FLUX.1 schnell, 4 steps).
diff --git a/docs/QUANTIZATION.md b/docs/QUANTIZATION.md
index f2765e16..f54bcdc9 100644
--- a/docs/QUANTIZATION.md
+++ b/docs/QUANTIZATION.md
@@ -1,10 +1,10 @@
 # Quantization
 
-Quantization reduces model memory usage by storing weights at lower precision. diffusers-workflow ships examples for BitsAndBytes, TorchAO, GGUF, and SDNQ, applied per-component in the pipeline - and any other backend with a config class (optimum-quanto, for example) works through the same dynamic `config_type` import.
+Quantization stores a component's weights at lower precision to cut its memory. The catalog has examples for BitsAndBytes, TorchAO, GGUF and SDNQ, applied per component. Any other backend with a config class (optimum-quanto, for example) works through the same dynamic `config_type` import ([Custom Quantization](#custom-quantization)). Which combinations fit a 24 GB card is in [RECIPES_24GB.md](RECIPES_24GB.md).
 
 ## Per-Component Quantization
 
-Quantize individual components (transformer, text encoder, etc.) independently:
+Each component block (transformer, text encoder, ...) takes its own `quantization_config`:
 
 ```json
 {
@@ -30,11 +30,11 @@ Quantize individual components (transformer, text encoder, etc.) independently:
 }
 ```
 
-The component is loaded separately with quantization, then the rest of the pipeline loads around it.
+The component loads on its own, quantized, and the rest of the pipeline loads around it. A modular pipeline declares it differently - see [Modular Pipelines](#modular-pipelines).
 
 ## BitsAndBytes (CUDA only)
 
-4-bit and 8-bit quantization via bitsandbytes:
+4-bit and 8-bit quantization. bitsandbytes is installed on Linux only:
 
 ```json
 "quantization_config": {
@@ -47,7 +47,7 @@ The component is loaded separately with quantization, then the rest of the pipel
 }
 ```
 
-Note: `"{nf4}"` uses braces to keep the string literal. Without braces, the type system would try to load `nf4` as a Python class.
+`"{nf4}"` is brace-escaped so it stays a string: a `*_type` key unbraced is loaded as a Python type and fails at load time, after validation has passed ([WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md#types-and-escaping)).
 
 For 8-bit:
 
@@ -59,10 +59,9 @@ For 8-bit:
 
 ## TorchAO
 
-Quantization via TorchAO. `quant_type` must be an `AOBaseConfig` class (diffusers no
-longer accepts string shorthands like `"int4wo"`). Name the class as a dotted
-`quant_type` and it is instantiated automatically with no arguments before being passed
-to `TorchAoConfig`:
+`quant_type` must be an `AOBaseConfig` instance; diffusers no longer accepts string
+shorthands like `"int4wo"`. Name the class as a dotted `quant_type` and dw instantiates
+it with no arguments before passing it to `TorchAoConfig`:
 
 ```json
 "quantization_config": {
@@ -81,12 +80,12 @@ RTX 40-series/Ada or newer).
 **Example:** [flux-torchao.json](../workflows/models/flux-torchao.json)
 
 **Pair TorchAO with `torch.compile`.** Int8 weight-only and float8 dynamic-activation
-quant types get their fused-kernel speedups only under compilation - uncompiled they are
-a memory win but often a speed *loss*. Compiled is not a guarantee either: int8 weight-only
-on an RTX 3090 measured over a minute per denoising step for Flux dev, compiled, against
-two seconds for bf16 (see [RECIPES_24GB.md](RECIPES_24GB.md#flux-dev-12b)). Measure on the
-card before recording a TorchAO recipe as fast. Add a `compile` block to the quantized component
-(see [ACCELERATION.md](ACCELERATION.md#torchcompile)):
+quant types get their fused-kernel speedups only under compilation; uncompiled they are
+a memory win and often a speed loss. Compiled is no guarantee: int8 weight-only on an
+RTX 3090 measured over a minute per denoising step for Flux dev, compiled, against two
+seconds for bf16 ([RECIPES_24GB.md](RECIPES_24GB.md#flux-dev-12b)). Measure on the card
+before recording a TorchAO recipe as fast. Add a `compile` block to the quantized
+component ([ACCELERATION.md](ACCELERATION.md#torchcompile)):
 
 ```json
 "configuration": {
@@ -100,7 +99,7 @@ card before recording a TorchAO recipe as fast. Add a `compile` block to the qua
 
 ## GGUF
 
-Load GGUF-format checkpoint files:
+Loads a GGUF-format checkpoint file:
 
 ```json
 "quantization_config": {
@@ -111,7 +110,7 @@ Load GGUF-format checkpoint files:
 }
 ```
 
-GGUF models load from single files using `from_single_file`:
+The component's `from_pretrained_arguments` names the file with `from_single_file`:
 
 ```json
 "from_pretrained_arguments": {
@@ -124,13 +123,13 @@ GGUF models load from single files using `from_single_file`:
 
 ## SDNQ (SD.Next Quantization)
 
-SDNQ works two ways: quantize a component on the fly at load time, or load a
-pre-quantized model as a complete pipeline.
+SDNQ quantizes a component on the fly as it loads, or loads a pre-quantized model as a
+complete pipeline.
 
 ### On-the-fly (`sdnq.SDNQConfig`)
 
-Quantizes the component while it loads - the pattern the LTX-2 and MiniMax H3
-examples use for their large transformers and text encoders:
+The pattern the LTX-2.5 and MiniMax-H3 templates use for their transformers and text
+encoders (H3 declares it under `load_components`, see [Modular Pipelines](#modular-pipelines)):
 
 ```json
 "quantization_config": {
@@ -145,14 +144,12 @@ examples use for their large transformers and text encoders:
 }
 ```
 
-- `weights_dtype` — the storage dtype (`uint4`, `int8`, ...; brace-escaped so it stays a string)
-- `quantization_device` / `return_device` — where the quantization pass runs and where the finished component lands; quantizing on `cuda` is much faster than on CPU
-- `use_quantized_matmul` — quantized matmul kernels (CUDA/XPU only)
+- `weights_dtype`: the storage dtype (`uint4`, `int4`, `int8`, ...), brace-escaped so it stays a string
+- `quantization_device` / `return_device`: where the quantization pass runs and where the finished component lands. Quantizing on `cuda` is much faster than on CPU. A `cuda` here becomes the server's device on a Mac
+- `use_quantized_matmul`: quantized matmul kernels (CUDA/XPU). Turned off on MPS with a warning, where it runs through `torch._int_mm` far slower than dequantizing
 
 **Examples:** [text-to-video.json](../workflows/templates/ltx2/text-to-video.json), [video-with-audio.json](../workflows/templates/minimax/video-with-audio.json)
 
-The H3 templates also default to `attention_backend: "sparse"`; the quantization is independent of it ([Attention Backends](ACCELERATION.md#sparse-minimax-h3)).
-
 ### Cached on-the-fly (`DW_QUANTIZED_CACHE`)
 
 An on-the-fly load reads the bf16 weights and quantizes them on every cold load. With `DW_QUANTIZED_CACHE=1` the server keeps the result: the first cold load saves each quantized component to disk with sdnq's `save_sdnq_model` (safetensors plus JSON, no pickle), and a later load of the same key reads it back with `load_sdnq_model`, skipping the bf16 read and the quantize. Any other value leaves it off, with no events. It covers both ways a component is quantized with a `sdnq.SDNQConfig` `quantization_config`: a modular pipeline's `load_components`, one config per component (MiniMax H3), and a component loaded on its own through its class's `from_pretrained` (LTX-2.5's transformer and Gemma text encoder). A hit is placed on the config's `return_device` (LTX's transformer goes straight to `cuda`) and loads with no download.
@@ -160,7 +157,7 @@ An on-the-fly load reads the bf16 weights and quantizes them on every cold load.
 The variables are read by the server and inherited by its workers, so set them in the server's environment (e.g. `.env`) and restart.
 
 - **Where**: `DW_QUANTIZED_CACHE_DIR`, default `<HF_HOME>/dw-quantized`. `dw.serve` resolves it to an absolute path at start and pins it for the workers. Each entry is `<root>/<sha256 key>/` with an `entry.json` (the key's inputs, size, created and last-hit times), written to a `.tmp-*` sibling and renamed into place
-- **The key**: repo, subfolder, snapshot commit, quantizer class and config (minus the devices, the quantized-matmul switches and `non_blocking`, which don't change the stored weights), `torch_dtype`, and the sdnq, diffusers, transformers and torch versions. A key that can't pin the weights isn't cached
+- **The key**: repo, subfolder, snapshot commit, quantizer class and config (minus the devices, the quantized-matmul switches and `non_blocking`, which don't change the stored weights), `torch_dtype`, the sdnq, diffusers, transformers and torch versions, and `KEY_VERSION`. A key that can't pin the weights isn't cached
 - **Not cached** (a `skipped` event with the reason):
 
   | Reason | When |
@@ -200,16 +197,16 @@ Pre-quantized SDNQ repos load as complete pipelines. The `sdnq` module must be i
 }
 ```
 
-- `pre_load_modules` — Imports sdnq before pipeline loading (registers quantization method)
-- `sdnq_optimize` — Applies quantized matmul to listed components (CUDA/XPU only, skipped on MPS/CPU)
+- `pre_load_modules`: imports sdnq before the pipeline loads, which registers its quantization method. `sdnq` is one of the packages an untrusted workflow may import; any other module needs a trusted workflow ([SECURITY.md](SECURITY.md))
+- `sdnq_optimize`: applies quantized matmul to the listed components. CUDA/XPU with Triton only; skipped elsewhere with a log line
 
 **Example:** [z-image-sdnq.json](../workflows/models/z-image-sdnq.json)
 
 ## Modular Pipelines
 
-A modular pipeline pulls its component weights itself via `load_components()` rather than
-through `from_pretrained_arguments`, so quantization is keyed by component name under
-`load_components.quantization_config` instead of living on a separate component block:
+A modular pipeline loads its components itself through `load_components()`, so
+quantization is keyed by component name under `load_components.quantization_config`
+instead of living on a component block:
 
 ```json
 "configuration": {
@@ -230,14 +227,14 @@ through `from_pretrained_arguments`, so quantization is keyed by component name
 }
 ```
 
-A component the map does not name loads unquantized. Note that a transformers-based
-component (a text encoder, for example) takes the `transformers.TorchAoConfig` class,
-not the diffusers one - the `config_type` still resolves either via the dynamic import
-described below.
+A component the map does not name loads unquantized. A transformers-based component
+(a text encoder or language model) takes `transformers.TorchAoConfig`, not the diffusers
+class. The MiniMax-H3 templates quantize `transformer` (or `transformer_ref`),
+`text_encoder` and `vae` this way with `sdnq.SDNQConfig`.
 
 ## Custom Quantization
 
-Any quantization backend that provides a config class works via the `config_type` field with a dotted module path:
+Any backend that provides a config class works through `config_type` with a dotted module path:
 
 ```json
 "quantization_config": {
@@ -246,7 +243,7 @@ Any quantization backend that provides a config class works via the `config_type
 }
 ```
 
-The class is loaded dynamically via importlib.
+The class is imported dynamically (`dw/trust.py` gates it). A config class outside the packages dw already depends on (diffusers, transformers, torchao, sdnq, optimum, gguf, bitsandbytes, ...) needs a trusted workflow ([SECURITY.md](SECURITY.md)).
 
 ## Platform Notes
 
diff --git a/docs/RECIPES_24GB.md b/docs/RECIPES_24GB.md
index ed8824ed..e7883f1f 100644
--- a/docs/RECIPES_24GB.md
+++ b/docs/RECIPES_24GB.md
@@ -1,14 +1,14 @@
 # Fast on 24GB
 
-Recommended configurations per model family for a 24GB consumer GPU (RTX 3090/4090 class). Every knob here is documented individually in [ACCELERATION.md](ACCELERATION.md) and [QUANTIZATION.md](QUANTIZATION.md); this page is about the combinations that work.
+Tested configurations per model family for a 24GB consumer GPU (RTX 3090/4090 class). Each knob is documented in [ACCELERATION.md](ACCELERATION.md) or [QUANTIZATION.md](QUANTIZATION.md); this page is the combinations that work, and what they measured.
 
 The general recipe, in order of impact:
 
-1. **Fit the transformer first.** If it fits in bf16 with room for activations, don't quantize. If it doesn't, prefer float8/int8 quantization (TorchAO, GGUF Q8) over offloading - quantization costs quality once, offloading costs speed every step.
-2. **Compile the transformer** (`"compile": {"repeated_blocks": true}`). 1.3-1.5x, stacks with everything below. The server's persistent worker keeps compiled pipelines loaded, so the compile cost is paid once per session. Add `fullgraph: true` only when no cache is configured - cache hooks need a graph break.
-3. **Cache** (`"cache": {"type": "first_block"}`). Another 1.5-2x at mild quality cost; raise `threshold` to taste.
-4. **Offload only what doesn't fit.** Text encoders and VAE tolerate `offload: "model"` cheaply - they run once per generation, not once per step. In a modular pipeline the same components take `"residency": "on_demand"`, which frees their VRAM for the denoise loop at the cost of one pair of transfers per call.
-5. **Pin the attention backend** on compiled components (`"attention_backend": "flash_hub"` or `"sage_hub"` - fetched from the Hub, no local build).
+1. **Fit the transformer first.** If it fits in bf16 with room for activations, don't quantize. If it doesn't, prefer float8/int8 quantization (TorchAO, GGUF Q8) over offloading: quantization costs quality once, offloading costs speed every step. TorchAO int8 is the exception on Ampere ([Flux dev](#flux-dev-12b)).
+2. **Compile the transformer** (`"compile": {"repeated_blocks": true}`): 1.3-1.5x, paid once per worker per workflow ([torch.compile](ACCELERATION.md#torchcompile)). No `fullgraph: true` alongside a cache.
+3. **Cache** (`"cache": {"type": "first_block"}`) on a schedule of 20 steps or more; it skips nothing on a short or turbo one ([Diffusers Built-in Cache](ACCELERATION.md#diffusers-built-in-cache)). Raise `threshold` to skip more.
+4. **Offload only what doesn't fit.** Text encoders and VAE tolerate `offload: "model"` cheaply: they run once per generation, not once per step. Per component, `"residency": "on_demand"` frees their VRAM for the denoise loop at the cost of one pair of transfers per call.
+5. **Pin the attention backend** on compiled components (`"attention_backend": "flash_hub"` or `"sage_hub"`, Hub kernels with no local build; `sage_hub` needs `DIFFUSERS_TRUST_REMOTE_KERNELS=true` - see [Attention Backends](ACCELERATION.md#attention-backends)).
 
 ## Flux dev (12B)
 
@@ -69,8 +69,7 @@ loads with the least VRAM and is unmeasured for speed.
 
 ## Z-Image Turbo (6B)
 
-Fits a 24 GB card in bf16 with model offload, and that is the configuration to use.
-Measured on an RTX 3090 (960x544, 9 steps, 4 images per prompt, cold load included,
+Fits a 24 GB card in bf16 with model offload, the configuration to use. Measured on an RTX 3090 (960x544, 9 steps, 4 images per prompt, cold load included,
 2026-10-10):
 
 | Offload | Load | Denoise | Decode | Total | Peak VRAM reserved |
@@ -88,22 +87,20 @@ trade it for per-image decodes.
 
 ## Qwen-Image (20B)
 
-Too large for bf16 on 24GB. Quantize the transformer (TorchAO int8/float8 or GGUF Q4/Q5) and model-offload the rest; add `compile` + `first_block` cache as for Flux. The Qwen2.5-VL text encoder is large - quantize it too, or group-offload it (`"text_encoder.model"` with `leaf_level`).
+Too large for bf16 on 24GB. Quantize the transformer (TorchAO float8, or GGUF Q4/Q5) and model-offload the rest; add `compile` + `first_block` cache as for Flux. The Qwen2.5-VL text encoder is large: quantize it too, or group-offload it (`"text_encoder.model"` with `leaf_level`). The catalog ships no Qwen-Image (20B) workflow; author one from [text-to-image.json](../workflows/templates/text-to-image.json).
 
-The catalog no longer ships a Qwen-Image workflow - author one from [text-to-image.json](../workflows/templates/text-to-image.json) with the configuration above.
+Qwen-Image-2.1 is a different, 7B model and fits in bf16: [qwen-image-2.1.json](../workflows/models/qwen-image-2.1.json) runs text-to-image at 2048x2048 under `"offload": "model"`, and [qwen-image-2.1-edit.json](../workflows/models/qwen-image-2.1-edit.json) needs `"offload": "sequential"` because the condition image's latents lengthen the sequence. Both tile and slice the VAE, whose decode is what runs out of memory at that size. Its own `use_kv_cache` must be off for a `first_block` or `mag` cache ([Models diffusers has not registered](ACCELERATION.md#models-diffusers-has-not-registered)).
 
 ## Wan 2.2
 
-- **TI2V-5B**: fits in bf16 on 24GB. No quantization needed - just `compile` + `cache` + VAE tiling for longer clips.
-- **T2V/I2V-A14B**: two 14B transformers (`transformer` + `transformer_2`). Quantize both (GGUF Q4/Q5 or TorchAO int8) and use `offload: "model"`; enable `vae.enable_tiling`.
+- **TI2V-5B**: fits in bf16 on 24GB. No quantization; `compile` + `cache` + VAE tiling for longer clips.
+- **T2V/I2V-A14B**: two 14B transformers (`transformer` + `transformer_2`). Quantize both (GGUF Q4/Q5) and use `offload: "model"`; enable `vae.enable_tiling`.
 
-The catalog no longer ships Wan workflows - author one from [minimax/image-to-video.json](../workflows/templates/minimax/image-to-video.json) with the configuration above.
+Unmeasured here. The catalog ships no Wan workflow; author one from [ltx2/text-to-video.json](../workflows/templates/ltx2/text-to-video.json), the closest standard video pipeline.
 
 ## HunyuanVideo (13B)
 
-Same shape as Flux: quantize the transformer (GGUF Q6/Q8, or TorchAO int8/float8 per the Flux table) + `offload: "model"` + `vae.enable_tiling`. The Llama text encoder benefits from group offload. Use `first_block` cache - video steps are expensive, caching pays off more than on images.
-
-The catalog no longer ships a HunyuanVideo workflow - author one from [ltx2/text-to-video.json](../workflows/templates/ltx2/text-to-video.json) with the configuration above.
+Same shape as Flux: quantize the transformer (GGUF Q6/Q8, or TorchAO float8 on Ada) + `offload: "model"` + `vae.enable_tiling`. The Llama text encoder benefits from group offload. Use a `first_block` cache at 20 steps or more: video steps are expensive, so caching pays off more than on images. Unmeasured here. The catalog ships no HunyuanVideo workflow; author one from [ltx2/text-to-video.json](../workflows/templates/ltx2/text-to-video.json).
 
 ## MiniMax-H3
 
@@ -112,16 +109,16 @@ pipeline-level `offload`. The working 24GB configuration at 960x544:
 
 | Component | Config |
 | --------- | ------ |
-| `transformer` / `transformer_ref` | SDNQ int4 (`quantization_device: "cuda"`, `return_device: "cpu"`), `group_offload` `block_level` with `num_blocks_per_group: 1-2` and `use_stream: true` |
-| `text_encoder` | `remove_modules: ["lm_head"]` - the encoder path never calls it |
-| `text_encoder.model` | SDNQ int4, `truncate_layers: {"language_model.layers": 51}`, `group_offload` `leaf_level` |
-| `vae`, `audio_vae` | SDNQ int8, `device: "cuda"`, `residency: "on_demand"` |
-| pipeline | `cache: first_block` (`threshold: 0.05`) only on a ~30-step base-model run (`lora_model_name: null`): -22% denoise there, identical staging and faces. It skips nothing at 12 steps or on a turbo schedule (consecutive distilled steps differ too much), and every H3 template takes it as a `cache` argument (null by default): `{"type": "first_block", "threshold": 0.05}`. Measured on a 3090 with `sage_hub`; every H3 template now defaults to `attention_backend: "sparse"` ([Attention Backends](ACCELERATION.md#sparse-minimax-h3)), which needs no extra install. See the `minimax-h3` skill's `references/quality-and-speed.md` |
-
-The VAEs are the piece worth calling out. They hold roughly 3GiB, are used only to
-encode references and decode the result, and group offloading them is worse than useless
-because tiled decode restreams the model once per tile. Every H3 workflow adds
-`"residency": "on_demand"` to both. The reference workflows, which carry the most
+| `transformer` / `transformer_ref` | SDNQ `weights_dtype` (`int4` by default; `quantization_device: "cuda"`, `return_device: "cpu"`), `group_offload` `block_level` with `num_blocks_per_group` 2 (`transformer`) or 1 (`transformer_ref`), `use_stream: true`, `low_cpu_mem_usage: true`; `attention_backend: "sparse"` with top-k 0.15 ([sparse](ACCELERATION.md#sparse-minimax-h3)) |
+| `text_encoder` | SDNQ, same `weights_dtype` (the vision tower and `lm_head` unconverted); `remove_modules: ["lm_head"]`, since the encoder path never calls it |
+| `text_encoder.model` | `truncate_layers: {"language_model.layers": 51}`, `group_offload` `leaf_level` |
+| `vae` | SDNQ int8, `device: "cuda"`, `residency: "on_demand"` |
+| `audio_vae` | unquantized, `device: "cuda"`, `residency: "on_demand"` |
+| pipeline | `cache` `null` by default. `first_block` (`threshold: 0.05`) only on a ~30-step base-model run (`lora_model_name: null`): -22% denoise there, identical staging and faces, measured on a 3090 with `sage_hub`. It skips nothing at 12 steps or on a turbo schedule. See the `minimax-h3` skill's `references/quality-and-speed.md` |
+
+The VAEs hold roughly 3GiB, are used only to encode references and decode the result,
+and group offloading them is worse than useless because tiled decode restreams the model
+once per tile. Every H3 workflow sets `"residency": "on_demand"` on both. The reference workflows, which carry the most
 conditioning, go from 23.2GiB peak reserved with 40 allocator retries to 18.9GiB with
 none; the frame-conditioned ones from 22.7GiB with 22 retries to 18.0GiB with none. Both
 for about 1% in wall time.
@@ -133,8 +130,8 @@ The text encoder pruning exists because H3 conditions on `hidden_states[50]` of
 encode. Keeping 51 layers is bit-identical - index 50 of the tuple is recorded before
 layer 50 runs; keeping only 50 would hand back the final-norm output, a different
 tensor - and it returns a few GiB of system RAM on a host that needs every one of them
-(a full t2va run peaks around 63GiB RSS on a 64GiB box). Note H3's VAE constructs with
-tiling already enabled, so a pipeline-level `vae.enable_tiling` adds nothing here.
+(a full t2va run peaks around 63GiB RSS on a 64GiB box). H3's VAE constructs with
+tiling already enabled, so a pipeline-level `vae.enable_tiling` adds nothing.
 
 Two levers measured and *rejected* on a 3090 (A/B, t2va 960x544x124f, 2026-08):
 `low_cpu_mem_usage: false` on the transformer's group offload (pin host copies once
@@ -142,8 +139,7 @@ instead of re-pinning per onload) left step time unchanged at ~15.4s - at int4 t
 step is compute-bound and the transfers already hide under it - while the ~27GiB of
 unswappable pinned memory pushed the host into an OOM kill. `use_stream` on the text
 encoder's leaf offload was backed out with it for the same host-memory reason. On a
-faster GPU (or a host with more RAM) both are worth re-testing; the step budget there
-may actually expose the transfer time.
+faster GPU, or a host with more RAM, both are worth re-testing.
 
 Length costs VRAM but the configuration holds to the model's full range: a single
 345-frame take (14.4s, the `17n+5` maximum) peaks at 23.6GiB reserved at 960x544 -
@@ -156,8 +152,8 @@ model first (Z-Image drawing a subject, Music3 writing a song) must free it with
 `release_pipeline` before H3 loads - with it, the multi-model digital-short
 workflows below fit; without it, the load is an OOM kill, not a slowdown.
 
-Ref2VA reaches 1344x768 too - the canvas isn't tied to the FL2VA 768p checkpoint, only
-the shift/alpha pairing is (above). The trade-off is frame count, not a config change:
+Ref2VA reaches 1344x768 too: the canvas isn't tied to the FL2VA 768p checkpoint, only
+its shift/alpha pairing is. The trade-off is frame count, not a config change:
 VAE decode memory scales with `width * height * num_frames`, so the same 24GB budget
 that holds 345 frames at 960x544 only holds 209 at 1344x768 with 2 references, and each
 reference costs about 1 GB more (175 at 3) - 175 frames with 2 references measured at
@@ -188,13 +184,13 @@ encoder is nearly as large as it is. The working 24GB configuration at 960x544:
 
 | Component | Config |
 | --------- | ------ |
-| `transformer` | SDNQ `uint4` (`quantization_device: "cuda"`, `return_device: "cuda"`, `use_quantized_matmul: true`), resident |
+| `transformer` | SDNQ `uint4` (`quantization_device: "cuda"`, `return_device: "cuda"`, `use_quantized_matmul: true`), `group_offload` `block_level` with `num_blocks_per_group: 1000` (more than its block count, so it moves as one group) and `use_stream: true`. A pipeline-level `offload: "model"` conflicts with the other components' group offload hooks (#266) |
 | `text_encoder` (Gemma 4, 23GB) | SDNQ `int8`, `return_device: "cpu"`, `group_offload` `leaf_level` with `use_stream: true` |
 | `connectors` (12GB) | `group_offload` `leaf_level` with `use_stream: true` |
 | `vae`, `audio_vae`, `vocoder`, `duration_head` | `device: "cuda"` - small, and used once per generation |
 | pipeline | `vae.enable_tiling` for anything above the base resolution |
 
-Two things about the checkpoint are worth knowing before tuning anything:
+Two facts about the checkpoint, before tuning anything:
 
 - **`transformer` is the distilled model.** It runs a fixed 8-step schedule at
   `guidance_scale: 1.0`, with STG and modality guidance off, and the `sigmas` every
@@ -235,7 +231,7 @@ That refine pass is what puts the detail back - the upsampler alone gives a soft
 and it is the flow the model card, Lightricks' pipeline notes and the diffusers docs all
 describe. The base pass keeps its pipeline loaded so the refine pass is served from the
 cache; the refine pass releases it. Since 2026-08 Lightricks route production quality
-through their DFR pipeline instead, which diffusers ships and nothing here uses yet.
+through their DFR pipeline instead, which diffusers ships and the catalog does not use.
 Measured on an RTX 3090 the refined clip is sharper than the 2x upsample alone at the
 same seed: fur, branches and snow texture resolve where the upsample-only frame is a
 soft blur. About eight warm minutes, three and a half of them writing the full-size clip.
@@ -260,7 +256,7 @@ Fits several times over in 24GB. Skip quantization and offloading entirely; `com
 
 ## Multi-model workflows
 
-When a workflow chains two large models (generate → upscale, generate → interpolate), release the first pipeline instead of offloading everything:
+When a workflow chains two large models (generate, then upscale or interpolate), release the first pipeline instead of offloading everything:
 
 ```json
 { "name": "generate", "release_pipeline": true, "pipeline": { ... } }
diff --git a/docs/RELEASING.md b/docs/RELEASING.md
index aaec12a1..07de1bac 100644
--- a/docs/RELEASING.md
+++ b/docs/RELEASING.md
@@ -1,11 +1,53 @@
 # Releasing
 
+## Cutting a release
+
+A release is a `v<semver>` tag on `master`. CI builds and publishes it.
+
+Before merging `develop` into `master`, get `scripts/preflight.sh` passing. It runs the real-model integration tests (`pytest -m integration`), which CI cannot run, and the UI's full `npm run preflight` (see [TESTING.md](TESTING.md)).
+
+```bash
+scripts/release.sh 0.38.0
+scripts/release.sh 0.38.0-alpha.1 "UI front end"   # optional tag message
+scripts/release.sh 0.38.0 --next 0.39.0-alpha.1     # and reopen develop
+```
+
+`scripts/release.sh`, run on `master` with the venv active:
+
+1. Refuses a malformed version, a branch other than `master`, an existing tag, or staged changes. Unstaged changes elsewhere are fine; the release commit is path-limited.
+2. Runs `pytest -m integration -n0` against the working tree, and refuses when they fail or when the machine has no CUDA or MPS device (there they would skip and pass having run nothing).
+3. Sets the version in `pyproject.toml` (the single source; `dw.__version__` reads it at run time) and in `plugins/dw/.claude-plugin/plugin.json` (`tests/test_plugin_skills.py` holds them equal), commits those two files as `release <version>`, and pushes `master`.
+4. Tags the bump commit `v<version>` (annotated with the message, default `release <version>`) and pushes the tag.
+5. With `--next <version>`: in a temporary detached worktree, merges `master` into `develop` (a fast-forward when nothing landed there since the release PR), sets `<version>` in the same two files, commits `chore: open <version> on develop` and pushes `develop`. It works while `develop` is checked out elsewhere. Without `--next`, do this by hand, or `develop` keeps reporting the previous pre-release.
+
+By hand, the equivalent of steps 3-4 is:
+
+```bash
+# Set version = "0.38.0" in pyproject.toml and plugins/dw/.claude-plugin/plugin.json
+git commit -m "release 0.38.0" -- pyproject.toml plugins/dw/.claude-plugin/plugin.json
+git tag -a v0.38.0 -m "release 0.38.0"
+git push origin master v0.38.0
+```
+
+### What CI does with the tag
+
+`.github/workflows/ci.yml` runs on `v<semver>` tags:
+
+- **backend** and **ui**: the same checks as on every push.
+- **wheel** (after both): `scripts/build_dist.sh` builds the SPA, copies it into `dw/server/ui`, copies `docs/*.md` into `dw/docs` (the guides an installed server serves), and runs `python -m build`. It uploads `dist/*` as an artifact.
+- **release** (after wheel): fails unless the tag is `v` + semver and equals the `pyproject.toml` version, then creates a GitHub release named after the tag, with auto-generated notes and the wheel and sdist attached. A tag with a pre-release part (`v0.38.0-rc1`) is marked as a pre-release.
+- **pypi** (after release): publishes the same artifacts to PyPI by [trusted publishing](https://docs.pypi.org/trusted-publishers/) (OIDC; no token is stored). One-time setup on pypi.org under *Publishing*: a trusted publisher for project `diffusers-workflow`, owner `dkackman`, repository `diffusers-workflow`, workflow `ci.yml`, environment `pypi` (a pending publisher before the first release). A pre-release needs `pip install --pre`.
+- **docker** (after release): builds `docker/Dockerfile`, starts the image on the CPU runner and waits for `/api/health`, then pushes `dkackman/diffusers-workflow:<version>` to Docker Hub. A release also moves `latest`; a pre-release does not. One-time setup: the public Docker Hub repository `dkackman/diffusers-workflow`, a read/write access token, and a GitHub environment `dockerhub` holding it as secret `DOCKERHUB_TOKEN`. A failed push leaves the GitHub release and PyPI as they are; re-run the job.
+
+Python packaging normalizes a semver pre-release, so `0.38.0-alpha.1` builds a wheel named `0.38.0a1`. The tag, `pyproject.toml` and the GitHub release keep the semver form.
+
+To build artifacts without releasing, run CI by hand (`workflow_dispatch`); the wheel job uploads `dist/*`.
+
+A PyPI install gets released `diffusers`, which may lag the newest pipelines; install `git+https://github.com/huggingface/diffusers` on top.
+
 ## Unreleased
 
-This project has no standing release-notes file - GitHub auto-generates
-notes from commits at tag time (see below). This section is a scratch pad
-for items a branch's author wants the next release note to name; clear it
-when a release ships.
+GitHub generates notes from commits at tag time. The sections below are the hand-written notes: the newest is drafted at release prep and pasted into the GitHub release body once the tag has published (`gh release edit v<version> --notes-file ...`). Items a branch's author wants the next note to name go in a new section at the top.
 
 ### 0.18.0
 
@@ -1557,104 +1599,3 @@ release body once the tag has published (`gh release edit v0.4.0
   `reference-sheet` (#444) run with their own defaults again.
 - Joined audio and shot maps stay on the frame grid through repeated joins
   (#423, #426, #428, #435).
-
-Releases are cut by pushing a `v<semver>` tag. CI does the rest.
-
-Before merging `develop` into `master`, run `scripts/preflight.sh` and get it
-passing. It covers more than CI: ruff over the whole repo rather than
-`dw dw_mcp tests` and the real-model integration tests (`pytest -m
-integration`), neither of which CI runs. (CI runs the UI's Playwright e2e
-tests on every develop push and on PRs into develop; the release PR from
-develop relies on the push runs.)
-
-```bash
-scripts/release.sh 0.38.0
-scripts/release.sh 0.38.0-alpha.1 "UI front end"   # optional tag message
-scripts/release.sh 0.38.0 --next 0.39.0-alpha.1     # and reopen develop
-```
-
-The script bumps `pyproject.toml` (the single source of the version —
-`dw.__version__` reads it at runtime) and sets the same version in
-`plugins/dw/.claude-plugin/plugin.json`, so an installed plugin names the
-engine it was written against; it commits just those two files, pushes
-master, tags the bump commit `v0.38.0`, and pushes the tag. It refuses
-a malformed version, a branch other than master, an existing tag, or a
-dirty index (unstaged changes elsewhere are fine — the release commit
-is path-limited to those two files).
-
-Before it bumps anything it runs the integration tests, the gate CI's
-accelerator-less runners cannot, and refuses to release when they fail or
-when the machine has no CUDA or MPS device - there they would skip and
-pass having run nothing. Cut a release from the Mac or lem, with the venv
-active. The tests run against the working tree, so unstaged changes are
-part of what they check.
-
-`--next <version>` finishes the release on the other branch: it merges
-`master` back into `develop` (a fast-forward when nothing landed there
-since the release PR), sets `<version>` in the same two files, commits
-`chore: open <version> on develop` and pushes `develop`, all in a temporary
-worktree, so it works while `develop` is checked out elsewhere. Without it, do that by hand, or `develop` goes on reporting the
-previous pre-release.
-
-CI runs on every push to `develop` as well as `master` - the agent loop
-pushes `develop` directly, with no PR - so a failure shows up against the
-commit that caused it, not first on the release PR.
-
-By hand, the equivalent is:
-
-```bash
-# 1. Bump the version in pyproject.toml:
-#    version = "0.38.0"
-# 2. Set the same version in plugins/dw/.claude-plugin/plugin.json
-git commit -m "release 0.38.0" -- pyproject.toml plugins/dw/.claude-plugin/plugin.json
-
-# 3. Tag the bump commit and push
-git tag -a v0.38.0 -m "release 0.38.0"
-git push origin master v0.38.0
-```
-
-The tag must point at a commit whose pyproject already declares the
-same version — the release job checks and refuses a mismatch.
-
-The tag triggers the full CI chain: backend tests, UI lint/type-check/
-unit tests, then the wheel build (SPA compiled into the package via
-`scripts/build_dist.sh`). Only if all of that passes does the `release`
-job run — it verifies the tag matches the pyproject version, then
-creates a GitHub release named after the tag with auto-generated notes
-and the wheel + sdist attached.
-
-Note on pre-release numbering: Python packaging normalizes semver-style
-pre-releases, so a `0.38.0-alpha.1` version builds a wheel named
-`0.38.0a1`. The tag, pyproject, and release stay in the semver form;
-only the wheel filename and pip metadata show the normalized one.
-
-A pre-release tag like `v0.38.0-rc1` is marked as a pre-release on
-GitHub. Tags that aren't `v` + semver (or that don't match the declared
-versions) fail the release job before anything is published.
-
-After the GitHub release, the `pypi` job publishes the same artifacts to
-PyPI via [trusted publishing](https://docs.pypi.org/trusted-publishers/)
-(OIDC — no token stored anywhere). One-time setup on pypi.org under
-*Publishing*: add a trusted publisher for project `diffusers-workflow`
-with owner `dkackman`, repository `diffusers-workflow`, workflow
-`ci.yml`, environment `pypi` (use "add a pending publisher" before the
-first release, since the project won't exist yet). Pre-release versions
-are hidden from plain `pip install`; they need `pip install --pre`.
-
-The `docker` job, also after the GitHub release, builds
-`docker/Dockerfile` from the tag, starts the image on the CPU runner and
-waits for `/api/health`, then pushes `dkackman/diffusers-workflow:<version>`
-to Docker Hub. A release also moves `latest`; a pre-release does not.
-One-time setup: on Docker Hub, create the public repository
-`dkackman/diffusers-workflow` and an access token with read/write scope.
-On GitHub, under *Settings > Environments*, create an environment named
-`dockerhub` with that token as secret `DOCKERHUB_TOKEN`. A failed push
-leaves the GitHub release and PyPI as they are; re-run the job.
-
-Note: released `diffusers` from PyPI may lag the newest model pipelines
-this project targets — a PyPI install can need
-`pip install git+https://github.com/huggingface/diffusers` on top.
-
-To rebuild artifacts without releasing, run the CI workflow manually
-(`workflow_dispatch`) — the wheel job uploads `dist/*` as a workflow
-artifact.
diff --git a/docs/REMOTE.md b/docs/REMOTE.md
index a4833f17..cee3c1da 100644
--- a/docs/REMOTE.md
+++ b/docs/REMOTE.md
@@ -5,93 +5,90 @@ your laptop use it over the network. Nothing is installed on the laptop.
 
 ## On the GPU box
 
-1. Install as usual: `git clone …`, `bash ./install.sh` - or run the
-   container instead ([DOCKER.md](DOCKER.md), `DW_BIND=0.0.0.0`), which
-   makes the token for you; skip to the clients.
-2. Make a token: `openssl rand -hex 32`. This one string is the only thing
-   between "on your network" and "can run workflows on your GPU" - keep it
-   long and random.
+1. Install as usual (`git clone …`, `bash ./install.sh`), or run the
+   container ([DOCKER.md](DOCKER.md), with `DW_BIND=0.0.0.0`), which makes
+   the token for you; then skip to the clients.
+2. Make a token: `openssl rand -hex 32`. It is the only thing between "on
+   your network" and "can run workflows on your GPU".
 3. Run it bound to the network, with the token, with MCP:
 
        DW_API_TOKEN=<token> dw-serve --host 0.0.0.0 --mcp
 
-   `--mcp` is refused outright (exit 2) on a non-loopback bind with no
-   token: an MCP endpoint can author and run workflows and has no
-   token-entry page in front of it the way the web UI does.
+   `--mcp` on a non-loopback bind with no token is refused (exit 2): an MCP
+   endpoint can author and run workflows and has no token-entry page in
+   front of it. Without `--mcp`, a non-loopback bind with no token only logs
+   a warning.
 
-   To keep it running across logouts and reboots, install the systemd unit
-   in [contrib/systemd](../contrib/systemd/README.md) instead.
+   To keep it running across logouts and reboots, use the systemd unit in
+   [contrib/systemd](../contrib/systemd/README.md).
 4. Open port 8765 in the box's firewall for your LAN only (for example
    `sudo ufw allow from 192.168.1.0/24 to any port 8765`).
 
-Never pass `--trust-workflows` on a server other machines can reach: it
-lets any workflow - including one an agent authored - execute arbitrary
-Python. See [SECURITY.md](SECURITY.md#trust-model).
+Never pass `--trust-workflows` on a server other machines can reach: it lets
+any workflow, including one an agent wrote, execute arbitrary Python. See
+[SECURITY.md](SECURITY.md#trust-model).
 
 Check it from the laptop:
 
     curl http://<box>:8765/api/health -H "Authorization: Bearer <token>"
     {"status":"ok","version":"…","hostname":"gpu-box","device":"cuda","mcp":true,…}
 
-`hostname` and `device` are there so you can tell which machine answered.
+`hostname` and `device` say which machine answered.
 
-If an agent on this box will ever export a job or list gallery/asset URLs to
-hand to a person who isn't at a terminal on the box itself, set
-`DW_API_TOKEN` and also set `DW_PUBLIC_URL` to this server's origin (for
-example `https://dw.example.com`, or `http://<box>:8765` with no proxy):
+### URLs handed to a person
 
-    DW_API_TOKEN=<token> DW_PUBLIC_URL=https://dw.example.com dw-serve --host 0.0.0.0 --mcp
+`export_job`, `list_gallery` and the upload and asset routes return paths
+relative to the server (`/exports/job-1.zip`): right for a browser already
+pointed at the box, useless to someone who is not. Set `DW_PUBLIC_URL` (or
+the `public_url` setting) to the server's origin and those responses add
+`absolute_url` / `absolute_zip_url` built from it:
 
-Without it, `export_job`, `list_gallery` and the upload/asset routes only
-return paths relative to this server (`/exports/job-1.zip`) - correct for a
-browser already pointed at the box, useless handed to someone who isn't.
-With `DW_PUBLIC_URL` set (or the equivalent `public_url` setting), those
-responses add an `absolute_url` / `absolute_zip_url` built from it; nothing
-guesses this from request headers, so an unconfigured server omits the
-field rather than composing a wrong origin.
+    DW_API_TOKEN=<token> DW_PUBLIC_URL=https://dw.example.com dw-serve --host 0.0.0.0 --mcp
 
-The export zip, like `/outputs` and `/inputs` files, needs no token (#592), so
-an agent with HTTP fetches `export_job`'s `open_url` itself, prefixing a
-relative one with the address it reaches the server at; `DW_PUBLIC_URL` is for
-the URLs handed to a person.
+Nothing guesses the origin from request headers, so an unconfigured server
+omits the field. The export zip, like `/outputs` and `/inputs`, needs no
+token (#592), so an agent with HTTP fetches `export_job`'s `open_url`
+itself, prefixing a relative one with the address it reaches the server at.
 
 ## Browser
 
-Open `http://<box>:8765`. Click the key icon next to the theme toggle,
-paste the token once; it is kept in the browser's `localStorage` and sent
-with every request.
+Open `http://<box>:8765`. Click the key icon in the header and paste the
+token once; it is kept in the browser's `localStorage` and sent with every
+request. The Server → Status page shows the server's addresses and the
+`claude mcp add` line below, ready to copy.
+
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="img/ui-connect-dark.png">
+  <img alt="Status page, Connect from another machine: an address picker, the browser URL, and the claude mcp add line for that address with a token placeholder" src="img/ui-connect.png">
+</picture>
 
 ## Claude Code, no local install
 
     claude mcp add --transport http dw http://<box>:8765/mcp \
       --header "Authorization: Bearer <token>"
 
-Start a new Claude Code session; `/mcp` should list `dw` as connected.
-Pick the scope with `-s user` to have it in every project.
+Start a new Claude Code session; `/mcp` should list `dw` as connected. Add
+`-s user` to have it in every project.
 
-`http://<box>:8765/mcp` and `http://<box>:8765/mcp/` are both answered, and
-both require the token in an `Authorization: Bearer` header - the
-`?token=...` allowance a few browser GET routes have does not extend to
-`/mcp`.
+`/mcp` and `/mcp/` are both answered, and both take the token only as an
+`Authorization: Bearer` header; the `?token=` allowance a few browser GET
+routes have does not extend to `/mcp`.
 
-Two things differ from the local stdio setup:
+What differs from the local stdio setup:
 
-- `download_output` writes on the GPU box (where the MCP server runs), not
-  on your laptop, so an omitted destination is refused rather than dropped
-  loose in the workspace root - nothing on your laptop would find or
-  delete it there (#353). Pass an explicit destination inside the
-  workspace to save one anyway, or use `get_output_image` /
+- `download_output` writes on the GPU box, where the MCP server runs, so an
+  omitted destination is refused rather than dropped in the workspace root
+  where nothing on your laptop would find it (#353). Pass an explicit
+  destination inside the workspace, use `get_output_image` /
   `get_output_text` to see a result, or open
   `http://<box>:8765/outputs/<name>` in the browser.
-- The connection is a plain HTTP call per tool invocation; there is no
-  subprocess to restart.
-- `use_workspace`/`create_workspace` pin *this box's* one MCP client, shared
-  by every agent connected to it - this server is single-user, so there is
-  no per-session isolation. If you and another agent are both against the
-  same box, either one's `use_workspace` call can move what the other reads
-  and writes next (#298). Pass `workspace=` on each call that takes it
-  (`validate_workflow`, `run_workflow`, most read/media tools) instead of
-  relying on the session pin when that matters.
+- Each tool call is a plain HTTP request; there is no subprocess to restart.
+- The mounted MCP server is one client shared by every agent connected to
+  the box, so `use_workspace` (or `create_workspace(use=true)`) moves the
+  workspace for all of them (#298). When two agents share a box, pass
+  `workspace=` on each call that takes it (`validate_workflow`,
+  `run_workflow`, most read and media tools) instead of relying on the
+  session pin.
 
 ## Claude Code with a local install (stdio)
 
@@ -102,48 +99,42 @@ remote box too:
       --url http://<box>:8765 --token <token>
 
 `dw-mcp` refuses to start (exit 2) against a non-loopback URL without a
-token, and makes one `GET /api/health` at startup so a wrong URL or token
-is reported once, with a message, instead of as a 401 on every tool call.
-Against a remote URL a failed probe is fatal - it is a misconfiguration you
-have to fix. Against a loopback URL it only warns and serves anyway, since
-there it usually means "dw.serve is not up yet", which the next tool call
-reports for itself. `--no-probe` skips the check entirely.
+token. At startup it makes one `GET /api/health`, so a wrong URL or token is
+reported once instead of as a 401 on every tool call: against a remote URL a
+failed probe is fatal, against a loopback URL it only warns (the server may
+not be up yet). `--no-probe` skips it.
 
 ## Beyond your LAN
 
 Everything above is plaintext HTTP: the token and every prompt and result
-are readable by anything on the network path. That is acceptable on a
-network you control and nowhere else. **Do not port-forward 8765 on your
+are readable on the network path. **Do not port-forward 8765 on your
 router.** Two ways to reach the box from outside:
 
-**Tailscale (or another WireGuard overlay).** Install it on the box and the
-laptop; use the box's Tailscale IP or MagicDNS name in every URL above.
-Traffic is encrypted end to end, no certificates to manage, and `dw-serve`
-can stay bound to the Tailscale interface (`--host 100.x.y.z`) rather than
-`0.0.0.0`. This is the recommended option.
+**Tailscale (or another WireGuard overlay)**, the recommended option. Install
+it on the box and the laptop and use the box's Tailscale IP or MagicDNS name
+in every URL above. Traffic is encrypted end to end with no certificates to
+manage, and `dw-serve` can bind to the Tailscale interface
+(`--host 100.x.y.z`) rather than `0.0.0.0`.
 
-**A TLS-terminating reverse proxy.** Bind `dw-serve` back to loopback and
-put Caddy in front of it with a real hostname:
+**A TLS-terminating reverse proxy.** Bind `dw-serve` to loopback and put
+Caddy in front of it with a real hostname:
 
     # /etc/caddy/Caddyfile
     dw.example.com {
         reverse_proxy 127.0.0.1:8765
     }
 
-Caddy obtains and renews a certificate automatically. Use
-`https://dw.example.com` (no port) in every URL above. `dw-serve`'s Origin
-and Host checks work unchanged behind the proxy because Caddy forwards the
-`Host` header as-is. nginx works the same way with `proxy_pass` and
-`proxy_set_header Host $host;` plus your own certificate.
-
-A token holder can also empty dw's disk caches (`DELETE /api/caches/{name}`, or one entry via `/entries/{entry}`), as they can delete models.
+Caddy obtains and renews the certificate. Use `https://dw.example.com` (no
+port) in every URL above. The Origin and Host checks work unchanged because
+Caddy forwards `Host` as-is; nginx works the same way with `proxy_pass`,
+`proxy_set_header Host $host;` and your own certificate.
 
-The token is still the only authentication in either setup; a proxy or a
-VPN protects the transport, not the door.
+Either way the token is still the only authentication: a proxy or a VPN
+protects the transport, not the door. A token holder can do anything the API
+does, including deleting outputs, models and dw's disk caches.
 
 ## What is not here
 
 - TLS inside `dw-serve` itself: a reverse proxy does it better.
 - More than one token, or users: one shared secret, deliberately
   ([SERVER.md](SERVER.md#authentication)).
-- Docker: `install.sh` on the box is the supported install.
diff --git a/docs/SECURITY.md b/docs/SECURITY.md
index 55a93e4e..1e74108b 100644
--- a/docs/SECURITY.md
+++ b/docs/SECURITY.md
@@ -2,374 +2,394 @@
 
 ## Overview
 
-diffusers-workflow validates all file paths, user inputs, and URLs to protect against path traversal, injection, and resource exhaustion. A command-argument sanitizer (`sanitize_command_args()`) is available for any future subprocess use, though `dw/` currently invokes no subprocess/shell commands.
+diffusers-workflow validates file paths, names, user input and URLs against
+path traversal, injection, server-side request forgery and resource
+exhaustion. The validators are in `dw/security.py`, the trust gate for what a
+workflow may import or execute is `dw/trust.py`, the policy for where a
+workflow may read and fetch is `dw/locations.py`, and the HTTP layer is
+`dw/server/http_security.py`. [SECURITY_QUICKREF.md](SECURITY_QUICKREF.md) is
+the one-page digest.
 
 ## Security Module (`dw/security.py`)
 
+Every validator raises `PathTraversalError` or `InvalidInputError` (both
+`SecurityError`) on refusal.
+
 ### Path Validation
 
-- `validate_path()` — Blocks `../` (anywhere in the path), `~/` (or `~\`), and paths rooted at `/dev/`, `/proc/`, `/sys/`. Rejects null bytes and overlong paths (> 4096 chars). Resolves to an absolute, realpath'd path. If `base_dir` is given, raises `PathTraversalError` when the resolved path falls outside it.
-- `validate_workflow_path()` — `validate_path()` plus a required `.json` extension (via `validate_file_extension()`)
-- `validate_lora_path()` / `validate_lora_name()` — Confine a LoRA catalog entry to the `loras/` root: a plain name or one family folder deep, `.json` only, no traversal
-- `validate_output_path()` — `validate_path()` with `allow_create=True`, for directories/files that don't need to exist yet
-- `validate_file_extension()` — Checks a path's extension against an allowed set (used internally by `validate_workflow_path()` and by `arguments.py` for media files)
+- `validate_path(path, base_dir=None, allow_create=True)` - Refuses an empty
+  path, a null byte, a path over 4096 characters or a file name over 255,
+  `..` anywhere, `~/` (or `~\`), and a path rooted at `/dev/`, `/proc/` or
+  `/sys/`. Returns the absolute, realpath'd path. With `base_dir`, the
+  resolved path must equal it or lie under it (`PathTraversalError`
+  otherwise); symlinks are resolved on both sides first. Without `base_dir` it
+  confines nothing, and CodeQL still reports the path. `allow_create=False`
+  also requires the path to exist.
+- `validate_workflow_path(path, workflow_dir=None)`,
+  `validate_prompt_path(path, prompt_dir)`, `validate_lora_path(path, lora_dir)`
+  - `validate_path` with `allow_create=False` plus a required `.json`
+  extension.
+- `validate_output_path(path, output_dir)` - `validate_path` with
+  `allow_create=True`, for files and directories that need not exist yet.
+- `validate_file_extension(path, allowed)` - The extension against an allowed
+  set (`ALLOWED_*_EXTENSIONS`).
+- `contained(path, root)` - Whether `path`, symlinks resolved, lies inside
+  `root`, for directory walks that must not follow a link out.
+- `validate_weight_name(name, ...)` - A file name inside a Hub repo: no
+  leading `/` or `~`, no backslash or drive, no empty, `.` or `..` segment.
+
+### Name Validation
+
+Each name is checked before it is joined onto a directory; containment is
+then checked by the `validate_path` call that joins it. Every segment starts
+with a letter, digit or underscore, which excludes `..`, hidden names and
+absolute paths. Patterns end in `\Z`, so a trailing newline is refused.
+
+| Validator | Shape | Max |
+| --- | --- | --- |
+| `validate_variable_name` | `^[a-zA-Z_][a-zA-Z0-9_-]*$` | 100 |
+| `validate_prompt_reference`, `validate_lora_name` | a name, at most one folder deep | 200 |
+| `validate_asset_reference` | a file name with its extension, at most four folders deep; `@` allowed | 400 |
+| `validate_output_reference` | `<workflow>/<run>/<file>`, 2 to 7 segments; `@` allowed | 500 |
+| `validate_output_prefix` | `<workflow>/<run id>` or `<workflow>` | 500 |
+| `validate_subfolder` | relative segments under the run directory; no backslash | 200 |
+| `validate_workspace_name` | one segment, not one of the workspace root's own folder names | 100 |
+| `validate_constant_name` | a dotted Python name, identifiers and dots only | 200 |
+| `validate_commit_hash` | 7 to 40 hex characters (the diffusers updater's install pin) | 40 |
+| `validate_file_base_name` | no `/` or `\` | - |
+| `validate_content_type` | a MIME type with exactly one `/` | - |
 
 ### Input Validation
 
-- `validate_variable_name()` — Alphanumeric, underscore, hyphen only (pattern: `^[a-zA-Z_][a-zA-Z0-9_-]*$`), max 100 chars
-- `validate_string_input()` — Max length, no null bytes, no control characters other than tab/newline/CR. Every caller that checks a caller-supplied variable value (`dw/variables.py`, `dw/run.py`) passes `MAX_VARIABLE_VALUE_LENGTH`, 20,000 characters; file names and paths pass their own, shorter caps, so the function's bare default of 1000 is not the limit anything is held to. A variable's *default*, written in the definition, is not capped separately: the author controls the file, and the whole file is capped at 50MB
-- `validate_json_size()` — Limits JSON files to 50MB
-- `validate_url()` — Scheme must be `http` or `https`; must have a non-empty domain (`netloc`); may not contain a backslash. `urllib.parse` and the HTTP client disagree on which host `http://169.254.169.254\@example.com/` names, so the host the check approved need not be the one dialed; a `\` that belongs in a path is written `%5C`
-- `validate_constant_name()` — Guards `constant:` references before import: dotted-name pattern only, module must already be importable, and anything callable is refused
-- `safe_join_path()` — Joins path components after rejecting any that contain `..`, `/`, or `\\`. Defined in `security.py` but not currently called elsewhere in `dw/`.
+- `validate_string_input(value, max_length=1000)` - Refuses an over-long
+  value, a null byte, and any control character other than tab, newline and
+  CR. A caller-supplied variable value (`dw/variables.py`, `dw/run.py`) is held
+  to `MAX_VARIABLE_VALUE_LENGTH`, 20,000 characters. A variable's *default* is
+  not capped separately; the whole workflow file is.
+- `validate_json_size(path)` - A JSON file over 50MB is refused before it is
+  read.
+- `validate_url(url)` - Scheme `http` or `https`, a non-empty host, and no
+  backslash anywhere: `urllib.parse` and the HTTP client disagree on which
+  host `http://169.254.169.254\@example.com/` names, so a `\` that belongs in
+  a path is written `%5C`. It checks shape only; the host policy is
+  `dw/locations.py`'s.
+- `safe_join_path(*parts)` - Joins components after refusing any containing
+  `..`, `/` or `\`. Not called anywhere in `dw/`.
 
 ### Command Sanitization
 
-- `sanitize_command_args()` — Rejects arguments containing shell metacharacters ( `` ` `` `$` `|` `&` `;` `>` `<` and newline/CR). It does **not** call `shlex.quote()` — with `shell=False`, argument list separation is handled safely by Python/the OS, so this function is a defense-in-depth check, not an escaping step.
-- As of this writing, `dw/` does not invoke `subprocess` anywhere — the worker process (`dw/worker_manager.py`, `dw/worker.py`) is a `multiprocessing.Process` communicating over `multiprocessing.Queue`, not a shelled-out command. `sanitize_command_args()` is exercised by `tests/test_security.py` but is otherwise unused; it exists for any future code path that shells out.
+- `sanitize_command_args(args)` - Refuses an argument containing `` ` ``,
+  `$`, `|`, `&`, `;`, `>`, `<`, newline or CR. It does not quote: with
+  `shell=False` the OS separates arguments, so this is defense in depth.
+- `dw/` runs three subprocesses, all `shell=False` with fixed argument lists:
+  `pip` in the diffusers updater (`dw/server/updater.py`, through
+  `sanitize_command_args`, its commit pinned by `validate_commit_hash`),
+  `sysctl` for the Apple chip name (`dw/__init__.py`) and `nvidia-smi` for the
+  driver version (`dw/server/sysinfo.py`). The worker is a
+  `multiprocessing.Process`, not a shelled-out command.
 
 ## Trust model
 
-**A workflow JSON file can execute arbitrary Python.** This is a deliberate
-design choice, in the same spirit as ComfyUI custom nodes - the engine's
-dynamic-import machinery is what lets a workflow name any diffusers
-pipeline, scheduler, or quantization backend without dw shipping a bespoke
-adapter for each one. But the same machinery means loading a workflow file
-is not a passive data-load. Three things in a workflow JSON run
-`importlib.import_module()` on a name the file supplies, which executes
-that module's top-level code:
-
-- **`pre_load_modules`** (`dw/pipeline_processors/pipeline.py`) - a list of
-  module names imported before the pipeline loads, for their import-time
-  registration side effects (`sdnq` registering its quantization method
-  with diffusers, for instance)
+**A workflow JSON file can execute arbitrary Python.** The dynamic-import
+machinery that lets a workflow name any diffusers pipeline, scheduler or
+quantization backend also makes loading a workflow more than a data-load.
+Three things in a workflow run `importlib.import_module()` on a name the file
+supplies, which executes that module's top-level code:
+
+- **`pre_load_modules`** (`dw/pipeline_processors/pipeline.py`) - modules
+  imported before the pipeline loads, for their registration side effects
+  (`sdnq` registering its quantization method).
 - **A dotted `*_type`/`*_dtype`/`dtype`/`config_type` value**
   (`dw/type_helpers.py`, reached from `dw/arguments.py` and
   `dw/pipeline_processors/config_objects.py`) - `"sdnq.SDNQConfig"` imports
-  `sdnq` and reads `SDNQConfig` off it; nothing stops the module part from
-  naming something with no legitimate reason to appear in a workflow
-- **A `constant:`-prefixed reference** (`dw/type_helpers.py`'s
-  `load_constant_from_name`, reached from `dw/arguments.py`) - imports the
-  module the constant is declared in the same way, before reading the
-  attribute off it. `fetch_constant` refuses anything callable it finds,
-  but the import itself has already run by that point
-
-**Treat an untrusted workflow file exactly like an untrusted Python
-script.** Don't run one from a source you would not run a `.py` file from -
-a random download, a link in an issue, an LLM-authored file you have not
-read.
+  `sdnq` and reads `SDNQConfig` off it.
+- **A `constant:` reference** (`dw/type_helpers.py`'s
+  `load_constant_from_name`) - imports the module the constant is declared in
+  before reading the attribute.
+
+**Treat an untrusted workflow file exactly like an untrusted Python script.**
+Do not run one from a source you would not run a `.py` file from: a random
+download, a link in an issue, an LLM-authored file you have not read.
 
 ### `--trust-workflows`
 
-`dw-serve` takes a `--trust-workflows` flag (as does the in-process
-`dw-validate`), **off by default**. `dw-run` is a client of `dw-serve`, so
-the server's setting is the one its jobs run under. Untrusted (the default), `pre_load_modules` and any
-dotted `*_type`/`*_dtype`/`dtype`/`config_type` value are refused unless
-they resolve under a top-level package the tool already depends on for
-exactly this purpose - the framework packages (`diffusers`, `torch`,
-`torchvision`, `transformers`, `accelerate`, `peft`) and the quantization
-backends `pyproject.toml` declares for `config_objects.py`'s dynamic
-loading (`sdnq`, `torchao`, `optimum` for optimum-quanto, `gguf`,
-`bitsandbytes`), plus `dw` itself (a workflow's `component_type` can name a
-pipeline under `dw.community_pipelines`, which ships in this repo, not a
-third party one). The refusal names exactly what triggered it and points
-back at `--trust-workflows`. The bundled examples under `workflows/` all
-stay inside this set and load untrusted; a
-workflow that needs to reach outside it - a community pipeline module from
-somewhere else, a custom scheduler package - needs `--trust-workflows`.
-
-The package is not the whole check, because an allowed package holds
-things other than classes and re-exports modules outside itself. Untrusted,
-two more rules apply (`dw/type_helpers.py`):
-
-- **A type reference must resolve to a class.** A `*_type`/`config_type`
-  value is constructed with the workflow's own arguments, so
-  `"torch.hub.load"` - in `torch`, and a function that fetches and runs a
-  GitHub repo's code - is refused as "not a class", as is a module or any
-  other object. Under a `dtype` or `*_dtype` key a `torch.dtype`
-  (`"torch.bfloat16"`) is accepted too, since that is data rather than
-  something called. A bare name (`"FluxPipeline"`) resolves against
-  `diffusers` and is held to the same rule. `validate_workflow` reports the
-  refusal at the key's path, for every key the run loads as a type
-  (`from_pretrained_arguments.torch_dtype` as much as `config_type`).
-- **A `constant:` walk stays inside the package.** A dotted `constant:`
-  reference is gated like a type (the module it names is imported before
-  `fetch_constant` gets to refuse a callable, so the import itself is what
-  the gate has to stop), and then every step of the walk is checked: no
-  segment may start with `_`, checked before anything imports, and no module
-  the walk passes through may sit outside the allowed packages -
-  `constant:torch.os.environ` starts in `torch` and ends in the server's
-  environment, and is refused at `torch.os`. Reading a field off a value
-  declared in an allowed module still works
-  (`...ltx2.utils.GEMMA4_PROMPT_ENHANCEMENT_CONFIG.max_new_tokens`). A bare
-  name (`constant:SOME_NAME`) reads from `diffusers`. `validate_workflow`
-  resolves every literal `constant:` in a step the way the run does and
-  reports a refusal at its path (a variable's default at `variables.<name>`),
-  so nothing is queued to find out.
-
-Every `*_type` and `constant:` in the bundled catalog satisfies both rules,
-pinned by `tests/test_workflow_trust.py`'s catalog sweep. `--trust-workflows`
-lifts both, as it lifts the package check.
-
-Two `from_pretrained_arguments` keys are refused untrusted as well, for
-every component and pipeline: `trust_remote_code` (runs the model repo's
-own modeling code) and `custom_pipeline` (fetches and imports a pipeline
-module from the Hub or a local path). Neither goes through an
-`importlib` call of ours, so the dotted-name gate alone would leave
-diffusers' remote-code paths open. No bundled catalog entry sets either
-(`tests/test_catalog_structure.py` refuses one that does); a workflow that
-needs them needs `--trust-workflows`.
-
-`text_generation`'s `generate_kwargs` reach the model's `generate()`, which
-has a remote-code path of its own: `custom_generate` names a Hub repo whose
-`custom_generate/generate.py` transformers downloads and runs, and
-`trust_remote_code` lets it. Both are refused untrusted
-(`require_trusted_generate_arguments` in `dw/trust.py`), before the model
-loads. Its other keys must be scalars or lists of them, and none may be one of
-the pipeline call's own arguments - the same filter `chat_template_kwargs`
-gets, so neither can pass `tokenizer_encode_kwargs`, `images` or a template
-of its own around the task's checks.
-
-`speech_generation` has the same path twice: the text-to-audio pipeline hands
-both its `generate_kwargs` and its `forward_params` to `generate()` for a
-generative model (Bark, VibeVoice). Each must be an object, and
-`custom_generate` / `trust_remote_code` in either are refused untrusted, before
-the model loads (released versions through 0.14.0 passed them through).
-`trust_remote_code`, `custom_pipeline` or `custom_generate` given as a step
-argument of their own, outside those two objects, is refused too, for every
-model: untrusted by the trust gate, trusted as an argument the task doesn't
-take. The task never read them there, but it used to drop them silently, so a
-step setting one ran as if it had taken effect.
-
-Untrusted, every task step is checked the same way at validate:
-`trust_remote_code`, `custom_pipeline` or `custom_generate` set anywhere in its
-`arguments`, top level or nested, is an error naming the key and its path, so
-the run is refused before it queues. `Task.run` repeats the check for every
-command before its handler runs (GHSA-hhfj-p9xj-xjmr).
-
-`speech_generation`'s Audio8-TTS branch (`Edge0/Audio8-TTS-Realistic-0.6b`,
-`dw/vendored/audio8_tts_model.py`) runs no code from its repo: the model is
-vendored, so there is no `trust_remote_code` load. Its five files are fetched
-with `hf_hub_download` at one pinned 40-hex revision, never a branch, and the
-weights are safetensors. The audio codec, `codec.pth`, is a pickle checkpoint;
-it is read with `torch.load(weights_only=True)`, which refuses any object that
-isn't a tensor or a plain container, and only at that pinned revision. Its
-`generate_kwargs` / `forward_params` pass the same trust gate as above, then an
-allowlist (`temperature`, `top_p`, `top_k`, `do_sample`, `max_new_tokens`):
-any other key is refused by name before the model loads.
-
-MMAudio (`dw/vendored/mmaudio_model.py`, `dw/vendored/mmaudio_sync_model.py`,
-`dw/tasks/mmaudio_decoder.py`) runs no code from its repo either: the network
-and the Synchformer visual encoder are vendored at upstream commit
-`974010a0`. Their two checkpoints, `hkchengrex/MMAudio`'s
-`weights/mmaudio_large_44k_v2.pth` and `ext_weights/synchformer_state_dict.pth`,
-are pickles, pinned to one 40-hex revision; each is read with
-`torch.load(weights_only=True)` and loaded strictly, so any object that isn't a
-tensor or a plain container is refused and an unexpected key fails the load.
-The decoder's VAE and vocoder are Kandinsky 6's safetensors at a pinned full
-sha, built from their `config.json` read as JSON (only the class's own
-constructor arguments are taken), with no pickle and no `trust_remote_code`.
-The modules take local paths and never fetch: tests pin that none of them
-calls the Hub or writes a checkpoint.
-
-`text_generation`'s `system_prompt` may be read from a Hub repo file
-(`{"from_hub": {...}}`). That fetch is anonymous only: the Hub is asked
-without credentials (`token=False`) whether it serves the file publicly at the
-pinned 40-hex commit before every use, and the download carries no token
-either, so the server's own login never reaches a gated or private file on a
-workflow's say-so. It fails closed - any error from that ask, being offline,
-or `HF_HUB_OFFLINE` refuses the run rather than fall back to the cache - and
-it is capped at 1 MiB, checked against the Hub's reported size before the
-download (no reported size is a refusal) and against the file after it.
+`dw-serve` and `dw-validate` take `--trust-workflows`, **off by default**.
+`dw-run` is a client of `dw-serve`, so its jobs run under the server's
+setting. The choice is process-wide, reaches the spawned worker through
+`DW_TRUST_WORKFLOWS`, and `GET /api/server` reports it as `trust_workflows`.
+It is not scoped per workflow or per request: a server that accepts jobs from
+anything other than yourself, an MCP client included, should run without it.
+
+Untrusted (the default), these are refused, each with an error naming what
+triggered it and pointing at `--trust-workflows`:
+
+- **A `pre_load_modules` entry or dotted type outside the allowed packages**
+  (`require_trusted_dotted_name`). Allowed: `diffusers`, `torch`,
+  `torchvision`, `transformers`, `accelerate`, `peft`, the quantization
+  backends `sdnq`, `torchao`, `optimum` (optimum-quanto), `gguf`,
+  `bitsandbytes`, and `dw` itself (for `dw.community_pipelines`). This is
+  checked before anything is imported.
+- **A type reference that is not a constructible class**
+  (`require_loadable_type`, `require_constructible_class`). A `*_type` value
+  is constructed with the workflow's own arguments, so it must be a class
+  defined inside an allowed package (not merely re-exported by one) and one
+  of: a subclass of a diffusers or transformers model, pipeline, modular
+  pipeline, scheduler, tokenizer, processor, feature extractor, image
+  processor or quantization config, or of torchao's `AOBaseConfig`; a class in
+  an auto-pipeline/auto-model factory module; a dataclass in
+  `diffusers.pipelines` or `diffusers.modular_pipelines` with no unreviewed
+  `__post_init__`; or a diffusers attention processor. `"torch.hub.load"` is in
+  `torch` and is refused as not a class. Under a `dtype` or `*_dtype` key a
+  `torch.dtype` (`"torch.bfloat16"`) is accepted too. A bare name
+  (`"FluxPipeline"`) resolves against `diffusers` under the same rules.
+- **A `constant:` walk that leaves the allowed packages.** The module is gated
+  like a type before it imports; no segment may start with `_` (checked before
+  any import); no module the walk passes through may sit outside the allowed
+  packages (`constant:torch.os.environ` is refused at `torch.os`); and
+  `fetch_constant` refuses anything callable. Reading a field off a value
+  declared in an allowed module works. A bare `constant:SOME_NAME` reads from
+  `diffusers`.
+- **Remote code in `from_pretrained_arguments`**: `trust_remote_code` and
+  `custom_pipeline`, for every component and pipeline
+  (`require_trusted_from_pretrained_arguments`). Neither goes through an
+  import of ours, so the dotted-name gate alone would leave them open.
+- **Remote code in a model's `generate()`**: `custom_generate` (a Hub repo
+  whose `custom_generate/generate.py` transformers downloads and runs) and
+  `trust_remote_code`, in `text_generation`'s `generate_kwargs` and in
+  `speech_generation`'s `generate_kwargs` and `forward_params`
+  (`require_trusted_generate_arguments`), before the model loads.
+  `text_generation`'s `generate_kwargs` and `chat_template_kwargs` hold only
+  scalars or lists of them, and none may be one of the pipeline call's own
+  arguments (`tokenizer_encode_kwargs`, `images`, a template).
+- **Remote-code keys anywhere in a task's arguments**: `trust_remote_code`,
+  `custom_pipeline` or `custom_generate`, top level or nested, is a validation
+  error naming the key and its path, and `Task.run` repeats the check for
+  every command before its handler runs (`require_trusted_task_arguments`,
+  GHSA-hhfj-p9xj-xjmr). Trusted, the same keys given as a step argument
+  outside `generate_kwargs`/`forward_params` are still refused, as arguments
+  the task does not take.
+
+`validate_workflow` reports each refusal at the key's path (a variable's
+default at `variables.<name>`), so nothing is queued to find out. Every
+bundled workflow loads untrusted: `tests/test_workflow_trust.py` sweeps the
+catalog, and `tests/test_catalog_structure.py` refuses a catalog entry that
+sets `trust_remote_code` or `custom_pipeline`. `--trust-workflows` lifts all
+of the above except the trusted-mode argument check.
+
+Model code that dw vendors runs no code from its repo, trusted or not:
+
+- **Audio8-TTS** (`speech_generation`, `dw/vendored/audio8_tts_model.py`):
+  five files fetched by `hf_hub_download` at one pinned 40-hex revision;
+  safetensors weights; the `codec.pth` pickle read with
+  `torch.load(weights_only=True)`. Its `generate_kwargs` / `forward_params`
+  pass the trust gate, then an allowlist (`temperature`, `top_p`, `top_k`,
+  `do_sample`, `max_new_tokens`); any other key is refused before the model
+  loads.
+- **MMAudio** (`dw/vendored/mmaudio_model.py`,
+  `dw/vendored/mmaudio_sync_model.py`, `dw/tasks/mmaudio_decoder.py`): the
+  network and Synchformer encoder vendored at upstream commit `974010a0`. The
+  two pickle checkpoints (`weights/mmaudio_large_44k_v2.pth`,
+  `ext_weights/synchformer_state_dict.pth`) are pinned to one 40-hex revision
+  and read with `torch.load(weights_only=True)` and a strict load. The VAE and
+  vocoder are Kandinsky 6 safetensors at a pinned sha, built from
+  `config.json` read as JSON. The modules take local paths and never fetch.
+
+`text_generation`'s `system_prompt` may be a Hub file (`{"from_hub": {...}}`),
+pinned to a 40-hex commit. Before every use the Hub is asked, without
+credentials (`token=False`), whether it serves the file publicly, and the
+download carries no token, so the server's login never reaches a gated or
+private file on a workflow's say-so. It fails closed: any error from that
+ask, being offline, or `HF_HUB_OFFLINE` refuses the run. It is capped at 1
+MiB, checked against the Hub's reported size before the download (no
+reported size is a refusal) and against the file after it.
 
 ### Where a workflow may read and reach (`dw/locations.py`)
 
-Code execution is not the only thing a workflow file chooses. Its arguments
-choose *locations* - which image to open, which URL to fetch, which
-directory a glob expands over - and until 2026-09-13 each loader trusted the
-one it was handed. A workflow could name `/usr/share/pixmaps/debian-logo.png`
-as its `image` and get it decoded, glob `/usr/share/pixmaps/*.png` and get
-every match republished verbatim as an output, or point an `image` at
-`http://127.0.0.1:8765` and have the server fetch its own loopback.
-`remote_text_encoder.url` was the worst of them: the request carries this
-machine's HuggingFace token.
-
-One policy now answers all of it, untrusted:
-
-- **A path** must resolve inside a root this installation already works in -
-  the workflow file's own directory, the asset libraries on the search path,
-  the output root. `validate_path` already refuses `..`, so in practice this
-  closes the absolute path that pointed somewhere else entirely; a relative
-  one that climbs out is refused on its `..` segments, at validation time as
-  well as at the loader, so both spellings are answered at the same moment
-  rather than one of them three seconds into a queued job (#124). The remedy
-  for a file outside is to put it in the asset library and use an `asset:`
-  reference. Containment is checked **before** existence, so the refusal
-  cannot be used as a file-existence oracle. Validation finds a path by its
-  argument's name (`image`, `*_video`, `location`, ...); a task argument
-  that reads a file under a generic name, like `join_windows`' `source`, is
-  declared on its registration (`@register_command(media_arguments=...)`,
-  read through `TASK_MEDIA_ARGUMENTS`, #692) so it is refused at the same moment
-  (#630), as are the finishing tasks' `media`, `apply_lut`'s `lut` (#635),
-  and the face-track and fit tasks' `clip`, `repaired`, `track` and `fit`
-  (#773). `lut`, `track` and `fit` are only ever read from a file on the
-  server, so an http(s) URL there is refused as a URL
-  (`LOCAL_ONLY_TASK_ARGUMENTS`).
-- **A URL with any other scheme** (`file://`, `s3://`, ...) is refused at
-  validation and at the loader, whatever the trust posture. No loader opens
-  one, but as a relative path it joined onto the workflow directory and
-  passed containment, so validation called it clean (#618).
-- **A glob** is contained the same way, on the fixed directory its pattern
-  starts from, and every match is re-checked on its real path so a symlink
-  cannot carry the expansion out.
-- **An `http(s)` URL** must not resolve to an address inside the deployment -
-  loopback, link-local (`169.254.0.0/16`, the cloud metadata address),
-  private ranges, and anything else that is not globally routable
-  (`is_global`). Checked after DNS resolution, not on the literal string.
-  That last rule covers `100.64.0.0/10`, the shared address space, which is
-  **Tailscale's tailnet range** (and Alibaba's metadata address): a workflow
-  that fetches media from another machine on your tailnet is refused unless
-  it runs under `--trust-workflows`.
-- **Every redirect is re-checked.** A media fetch (`safe_get` in `dw/outbound.py`) never lets the
-  HTTP client follow a redirect on its own: it follows at most 5 hops
-  (`MAX_MEDIA_REDIRECTS`), and each `Location` passes the same scheme and
-  host policy before it is dialed. A public URL answering `302` to
-  `http://127.0.0.1:8765/api/server` is refused with the target named, and
-  nothing is fetched from it. A refused hop's error always begins
-  `Refusing a redirect to '<target>' (from '<the URL asked for>'): ` and
-  then gives the rule that refused it (an internal address, a scheme other
-  than http(s), an unresolvable host), whereas an error status from a URL
-  itself stays the HTTP client's own `403 Client Error: Forbidden for url:
-  ...` - so a refused redirect and an upstream failure read differently
-  (#1055). Images are decoded from the fetched bytes and
-  still go through diffusers' `load_image`, so EXIF orientation and RGB
-  conversion are unchanged. Each hop's host is resolved once, by the policy,
-  and dialed at the address it checked - so a name that answers differently
-  to the lookup and the connect (DNS rebinding) reaches only what was
-  checked - and refused when it does not resolve or is percent-encoded (the
-  host is read with the HTTP client's own parser, so `127%2e0%2e0%2e1` is
-  127.0.0.1 to the policy too). An untrusted fetch ignores proxy and
-  `~/.netrc` settings from the environment. The body is capped at
-  `MAX_MEDIA_BYTES` (1 GiB) and the whole fetch at `MEDIA_TOTAL_TIMEOUT`
-  (600 s) - the connection is closed at the deadline, so a server
-  trickling its response headers or its body cannot hold the worker; a 303 (or a
-  301/302 to a POST) is followed as a GET, as requests itself does; and
-  `remote_text_encoder` POSTs through the same path with a timeout. Two limits
-  to know: a trusted run behind a SOCKS proxy is not deadline-bounded (SOCKS
-  brings its own connection classes), and the TLS handshake is bounded by the
-  per-operation timeout only.
-- **`remote_text_encoder.url`** is https-only, and the HuggingFace token is
-  attached only for `huggingface.co`, `huggingface.cloud` and `hf.space`. An
-  endpoint elsewhere is still reachable; it just does not get the credential.
-  A redirect that leaves the host, or drops from https to http, is followed
-  without the token.
-- **`model_name`** must be a Hub repo id or a path inside a root - the same
-  shape check `download_model` has always applied to `repo_id`. A URL is
-  neither, and is refused as such rather than resolving into the workflow's
-  own directory as a path-shaped name (#117).
-
-Enforced twice: `location_errors` runs inside `validation_errors`, so
-`validate_workflow` refuses before a model load is spent on the run, and the
-loaders call the same functions for a location that arrives through a
-variable or a previous result. All of it yields to `--trust-workflows`.
-
-`GET /api/server` reports `trust_workflows`, so a client can read the posture
-it is running against rather than infer it.
-
-`--trust-workflows` is a blanket, process-wide choice - it is not scoped
-per-workflow or per-request. A `dw-serve` instance that accepts jobs from
-anything other than yourself (including an MCP client - see below) should
-be run without it.
-
-### MCP-authored workflows (M3)
-
-The MCP server's `save_workflow` and `run_workflow` tools let an LLM write
-and then execute a workflow through `dw.serve` - `save_workflow` writes a
-JSON file into the workflow directory, `run_workflow` queues it (or an
-inline definition) as a job. Nothing in `dw_mcp/` inspects what a workflow
-it saves or runs actually contains. What protects a server used this way
-is exactly the mechanism above: `dw-serve`'s own `--trust-workflows`
-default is untrusted, so an MCP-authored or MCP-submitted workflow gets
-the same code-execution gate a workflow from any other untrusted source
-does, with no code change needed in `dw_mcp` itself. Running `dw-serve
---trust-workflows` removes that gate for every job the server accepts,
-MCP-submitted or not - see the blanket-choice note just above.
+A workflow's arguments also choose *locations*: which file to open, which URL
+to fetch, which directory a glob expands over. Untrusted:
+
+- **A path** must resolve inside a root this installation works in: the
+  workflow file's own directory, the asset libraries on the search path, or
+  the output root. A relative path that climbs out is refused on its `..`
+  segments, at validation as well as at the loader (#124). Containment is
+  checked **before** existence, so a refusal is not a file-existence oracle.
+  The remedy for a file outside is an `asset:` reference. Validation finds a
+  path by its argument's name (`image`, `*_video`, `location`, ...) or by a
+  task's declaration (`@register_command(media_arguments=...)`, read through
+  `TASK_MEDIA_ARGUMENTS`, #692). `apply_lut`'s `lut`, `paste_face_track`'s
+  `track` and `restore_to_source`'s `fit` are read only from a server file,
+  so a URL there is refused (`LOCAL_ONLY_TASK_ARGUMENTS`).
+- **A URL with any other scheme than http(s)** (`file://`, `s3://`, ...) is
+  refused at validation and at the loader, trusted or not (#618).
+- **A glob** is contained on the fixed directory its pattern starts from, and
+  every match is re-checked on its real path, so a symlink cannot carry the
+  expansion out.
+- **An http(s) URL** must not resolve to an address inside the deployment:
+  loopback, link-local (`169.254.0.0/16`, cloud metadata), private ranges, and
+  anything else not globally routable (`is_global`), checked after DNS
+  resolution. That includes `100.64.0.0/10`, **Tailscale's tailnet range**: a
+  workflow that fetches media from another tailnet machine needs
+  `--trust-workflows`.
+- **Every redirect is re-checked.** A media fetch (`safe_get` in
+  `dw/outbound.py`) follows at most 5 hops (`MAX_MEDIA_REDIRECTS`), each
+  `Location` passing the same scheme and host policy before it is dialed. A
+  refused hop is never fetched, and its error begins `Refusing a redirect to
+  '<target>' (from '<url>'): ` followed by the rule; an upstream error status
+  keeps the HTTP client's own message (#1055). Each hop's host is resolved
+  once, by the policy, and dialed at the checked address, so DNS rebinding
+  reaches only what was checked; a host that does not resolve or is
+  percent-encoded is refused. An untrusted fetch ignores proxy and `~/.netrc`
+  settings. The body is capped at `MAX_MEDIA_BYTES` (1 GiB) and the fetch at
+  `MEDIA_TOTAL_TIMEOUT` (600 s), closing the connection at the deadline.
+  `remote_text_encoder` POSTs through the same path. Two limits: a trusted
+  fetch through a SOCKS proxy is not deadline-bounded, and the TLS handshake
+  is bounded only by the per-operation timeout.
+- **`remote_text_encoder.url`** is https-only, and the Hugging Face token is
+  attached only for `huggingface.co`, `huggingface.cloud` and `hf.space`. A
+  redirect that leaves the host, or drops to http, is followed without the
+  token.
+- **`model_name`** must be a Hub repo id or a contained path; a URL is refused
+  (#117).
+
+`location_errors` runs inside `validation_errors`, so `validate_workflow`
+refuses before a model load is spent, and the loaders call the same functions
+for a location that arrives through a variable or a previous result. All of
+it yields to `--trust-workflows`, except the scheme rule.
+
+## HTTP authentication and headers (`dw/server/http_security.py`)
+
+- **Bearer token, opt-in.** With `--token` / `DW_API_TOKEN` set, every
+  `/api/*` and `/mcp` request needs `Authorization: Bearer <token>`, compared
+  in constant time; anything else is a 401. Without a token there is no
+  authentication.
+- **Query token.** Only a GET or HEAD route marked `query_token_ok`
+  (EventSource streams, thumbnails, download links, which a browser loads
+  without headers) accepts `?token=`. The marker is matched on the real route,
+  not a path suffix.
+- **Not gated by the token:** the web UI's static files, `/outputs`, `/inputs`
+  and `/exports/{id}.zip` (#592). Anyone who can reach the port can read
+  generated files and input assets by name.
+- **Binding.** The default bind is `127.0.0.1`. A non-loopback bind without a
+  token logs a warning and starts; `--mcp` on a non-loopback host without a
+  token exits 2. The connection is plaintext HTTP; use a network you control,
+  Tailscale, or a TLS proxy ([REMOTE.md](REMOTE.md)).
+- **Origin check.** A request carrying an `Origin` is refused (403) unless
+  its host is a loopback name, the bind host, or the host the request was
+  addressed to. This stops a drive-by web page queuing jobs, and DNS
+  rebinding, where Origin and Host differ. A request without `Origin` passes.
+- **Host check.** Unless bound to a wildcard address, a `Host` that is not a
+  loopback name or the bind host is a 400.
+- **Response headers.** Every response carries `X-Content-Type-Options:
+  nosniff` and `X-Frame-Options: DENY` (#407).
 
 ## Integration Points
 
-| Entry Point | What's Validated |
-|-------------|-----------------|
+| Entry point | What's validated |
+| --- | --- |
 | `workflow.py` | Workflow file paths, JSON size, output directories, sub-workflow paths |
 | `run.py`, `validate.py` | CLI arguments, variable names and values |
-| `arguments.py`, `argument_media.py` | Image/video/audio URLs, file paths, file extensions (`validate_media_location`; `fetch_image`, `fetch_video` in `argument_media.py`) |
+| `arguments.py`, `argument_media.py` | Media locations, file paths and extensions (`validate_media_location`; `fetch_image`, `fetch_video`) |
+| `locations.py`, `outbound.py` | Every location a workflow supplies, and every outbound media fetch |
 | `tasks/gather.py` | URLs passed to the `gather` task |
-| `result.py`, `writers.py` | Output directories and filenames (`output_file_path` in `writers.py`) |
-| `server/app.py`, `server/jobs.py` | Every HTTP-supplied path — workflow files confined to the workflow directory, gallery files to the output directory, inline-workflow `base_dir`, `Origin`-header guard on every request |
+| `result.py`, `writers.py`, `content_types.py` | Output directories, file names (`output_file_path`), result content types |
+| `server/http_security.py` | Token, Origin and Host on every request |
+| `server/jobs.py`, `server/outputs.py`, `server/routes/*.py` | Every HTTP-supplied path: workflows confined to the workflow directories, gallery files to the output directory, assets to their library, an inline workflow's `base_dir` |
 
 ## MCP Server
 
-`dw_mcp/` introduces no new file access and no authentication of its own. It
-is an HTTP client of a running `dw.serve`: every path a tool touches (a
-workflow name, a gallery file, a job id) is sent to the REST API as-is and
-validated there, exactly as it would be for a browser request from the web
-UI. A remote `dw.serve` is allowed only with a token — see
-[MCP Server](MCP.md#security) and [REMOTE.md](REMOTE.md).
-
-The two exceptions are `download_output`, which writes a local file for the
-MCP client, and `upload_asset(file_path=...)`, which reads one — neither goes
-through the API for that half of its work. Both turn on whose machine "local"
-is. Over a **stdio `dw-mcp`** it is the user's own, so both act for the local
-user the way a shell redirect would: `download_output` writes anywhere the
-process may (a full path, a directory, or the working directory by default,
-`~` expanded) and `upload_asset` reads anything it may. Over **`dw.serve
---mcp`** it is the operator's box, which the caller never chose, so both are
-confined there: `download_output`'s `destination` to the workspace (#113) and
-`upload_asset`'s `file_path` to the directories the server works in — its
-workspace, workflows, assets, outputs and prompts (#138). `upload_asset`'s
-refusal is ordered ahead of the existence and extension checks so it cannot
-be used as a path-existence oracle. A `..` path segment in `destination` is
-refused regardless, and an existing file at the resolved path is left alone
-unless the caller passes `overwrite=True`.
+`dw_mcp/` adds no authentication and no file access of its own, with two
+exceptions below. It is an HTTP client of `dw.serve`: every path a tool sends
+(a workflow name, a gallery file, a job id) is validated by the REST API
+exactly as a browser request from the web UI is. A remote `dw.serve` is
+allowed only with a token ([MCP Server](MCP.md#security),
+[REMOTE.md](REMOTE.md)).
+
+`download_output` writes a local file and `upload_asset(file_path=...)` reads
+one, outside the API. Over a **stdio `dw-mcp`**, "local" is the user's own
+machine, and both act as the user would with a shell redirect. Over
+**`dw.serve --mcp`** it is the operator's box, so both are confined:
+`download_output`'s `destination` to the workspace (#113), and
+`upload_asset`'s `file_path` to the server's workspace, workflows, assets,
+outputs and prompts directories (#138), with the refusal ordered before the
+existence and extension checks so it is not a path-existence oracle. On both
+transports a `..` segment in `destination` is refused and an existing file is
+kept unless `overwrite=True`.
+
+Nothing in `dw_mcp/` inspects what a workflow it saves (`save_workflow`) or
+runs (`run_workflow`) contains. An MCP-authored or MCP-submitted workflow is
+protected by `dw-serve`'s untrusted default, the same gate as any other
+untrusted workflow. `dw-serve --trust-workflows` removes that gate for every
+job the server accepts.
 
 ## Exception Hierarchy
 
 ```text
 SecurityError
-  PathTraversalError — path traversal attempt
-  InvalidInputError  — input validation failure
+  PathTraversalError     - path traversal attempt
+  InvalidInputError      - input validation failure
+  UntrustedWorkflowError - an untrusted workflow reached the code-execution surface
 ```
 
 ### Disk caches
 
-`DELETE /api/caches/{name}` (`dw/disk_caches.py`) empties a directory whose path comes from an environment variable, so it never trusts the root: only a root carrying a `.dw-cache` marker is cleared. `dw.serve` writes the marker at start into a root that is missing or empty (a missing `triton` root is never created), or into a populated root equal to the cache's default exactly (`~/.cache/dw/torchinductor` or its `$XDG_CACHE_HOME` form; `~/.triton/cache` only - a populated `$TRITON_HOME/.triton/cache` is not grandfathered). Any other populated root is listed with `clearable: false` ("root not created by dw (no .dw-cache marker)") and its clear is a 400, until someone creates the marker by hand; deleting the marker stops clearing. `quantized` (`DW_QUANTIZED_CACHE_DIR`) follows the same marker rule, and `DELETE /api/caches/{name}/entries/{entry}` deletes one entry only if the id matches `^[0-9a-f]{64}$`, checked before any path is built. The clear refuses while a job is running or queued (for `quantized`, also while a model download is active), and renames the root's children into `.clearing-*` before deleting them. Like model deletion, it needs only the bearer token.
+`DELETE /api/caches/{name}` (`dw/disk_caches.py`) empties a directory whose
+path comes from an environment variable, so it clears only a root carrying a
+`.dw-cache` marker. `dw.serve` writes the marker at start into a root that is
+missing or empty (a missing `triton` root is never created), or into a
+populated root equal to the cache's default exactly (`~/.cache/dw/torchinductor`
+or its `$XDG_CACHE_HOME` form; `~/.triton/cache` only, not a populated
+`$TRITON_HOME/.triton/cache`). Any other populated root is listed with
+`clearable: false` and its clear is a 400 until someone creates the marker by
+hand; deleting the marker stops clearing. `quantized`
+(`DW_QUANTIZED_CACHE_DIR`) follows the same rule, and
+`DELETE /api/caches/{name}/entries/{entry}` deletes one entry only if the id
+matches `^[0-9a-f]{64}$`, checked before any path is built. A clear refuses
+while a job is running or queued (for `quantized`, also while a model download
+is active), and renames the root's children into `.clearing-*` before deleting
+them. Like model deletion, it needs only the bearer token.
 
 ## Rules
 
-- Always validate paths before file operations
-- Use `validate_url()` before loading remote resources
-- If a subprocess is ever introduced, use `shell=False` and pass args through `sanitize_command_args()`
-- Never use dynamic code execution (`eval`/`exec`) or shell interpretation
+- Validate a path before any file operation, with a base directory.
+- Validate a URL with `validate_url()`, and a workflow-supplied location with
+  `dw/locations.py`, before loading a remote resource.
+- Run a subprocess with `shell=False` and its arguments through
+  `sanitize_command_args()`.
+- Never use `eval`, `exec` or `shell=True`.
 
 ## Protected Against
 
-- **Path traversal** — Cannot access files outside allowed directories
-- **Command injection** — No shell interpretation is used anywhere in `dw/`; `sanitize_command_args()` is available as a guard should a subprocess call be added
-- **Resource exhaustion** — File size limits prevent memory exhaustion
-- **Decompression bombs** — an image a caller names is decoded at no more than `MAX_DECODE_PIXELS` (50M; an 8K frame is 33M), checked after `Image.open` and before any decode: `get_output_image` (crops included) refuses it, the gallery thumbnail answers 413, and embedded metadata is read from the PNG header chunks without decoding. Video and audio decode are not limited
-- **Malicious URLs** — Only http/https schemes allowed, and an untrusted
-  workflow may not name a host inside the deployment (SSRF)
-- **Arbitrary file read through a media argument** — a location a workflow
-  supplies is confined to the roots it may read (`dw/locations.py`)
-- **Script on the UI's origin through an output** — a step's
-  `result.content_type` may not be an active document type - `text/html`,
-  `application/xhtml+xml`, `text/xml`, `application/xml` or `image/svg+xml`
-  (compared without case or parameters): validation refuses it at
-  `steps[i].result.content_type` and the writer refuses it again
-  (`dw/content_types.py`). A file of an active type that reaches `/outputs`
-  or `/inputs` anyway is served with `Content-Security-Policy: sandbox`
+- **Path traversal**: no file outside the allowed directories.
+- **Command injection**: no shell interpretation anywhere in `dw/`.
+- **Arbitrary code from a workflow**: the trust gate, unless
+  `--trust-workflows`.
+- **Arbitrary file read through a media argument**: a workflow-supplied
+  location is confined to the roots it may read (`dw/locations.py`).
+- **Malicious URLs and SSRF**: http/https only, and an untrusted workflow may
+  not reach a host inside the deployment, including through a redirect.
+- **Resource exhaustion**: 50MB workflow JSON, 1 GiB media fetch with a 600 s
+  deadline.
+- **Decompression bombs**: an image a caller names is decoded at no more than
+  `MAX_DECODE_PIXELS` (50M; an 8K frame is 33M), checked after `Image.open`
+  and before any decode. `get_output_image` (crops included) refuses one, the
+  gallery thumbnail answers 413, `compare_outputs` refuses a video whose frame
+  exceeds it (413), and embedded metadata is read from PNG header chunks
+  without decoding. Other video decodes and all audio decodes are not limited.
+- **Script on the UI's origin through an output**: a result `content_type`
+  may not be an active document type (`text/html`, `application/xhtml+xml`,
+  `text/xml`, `application/xml`, `image/svg+xml`, compared without case or
+  parameters). Validation refuses it at `steps[i].result.content_type`, the
+  writer refuses it again (`dw/content_types.py`), and a file of an active type
+  that reaches `/outputs` or `/inputs` anyway is served with
+  `Content-Security-Policy: sandbox`.
+
+Not protected: generated files and input assets are readable without the
+token by anyone who can reach the port, and traffic is plaintext.
 
 ## Testing
 
 ```bash
-pytest tests/test_security.py tests/test_locations.py tests/test_outbound.py tests/test_workflow_trust.py -v
+pytest tests/test_security.py tests/test_security_auth.py tests/test_security_trust_gate.py \
+  tests/test_security_symlinks.py tests/test_locations.py tests/test_outbound.py \
+  tests/test_workflow_trust.py -v
 ```
diff --git a/docs/SECURITY_QUICKREF.md b/docs/SECURITY_QUICKREF.md
index 350a1232..e48e499b 100644
--- a/docs/SECURITY_QUICKREF.md
+++ b/docs/SECURITY_QUICKREF.md
@@ -1,78 +1,77 @@
 # Security Quick Reference
 
-## MCP Server
-
-`dw_mcp/` adds no new file access and no authentication — it is an HTTP
-client of a running `dw.serve`, and every path a tool sends goes through the
-same validation the REST API already applies. A remote `dw.serve` is
-allowed only with a token, and `dw.serve --mcp` serves the same tool
-surface at `/mcp` behind that token. See [MCP Server](MCP.md#security)
-and [REMOTE.md](REMOTE.md).
+The one-page digest of [SECURITY.md](SECURITY.md), which has the detail and
+the reasons.
 
 ## Imports
 
 ```python
 from dw.security import (
-    validate_path,
-    validate_workflow_path,
-    validate_output_path,
-    validate_url,
-    validate_variable_name,
-    validate_string_input,
-    sanitize_command_args,
-    SecurityError,
+    validate_path,           # no .., ~/, /dev/, /proc/, /sys/; confined only with a base_dir
+    validate_workflow_path,  # validate_path + must exist + .json
+    validate_output_path,    # validate_path, may not exist yet
+    validate_url,            # http/https, a host, no backslash
+    validate_variable_name,  # ^[a-zA-Z_][a-zA-Z0-9_-]*$, max 100
+    validate_string_input,   # length, no null or control characters
+    sanitize_command_args,   # no ` $ | & ; > < newline CR
+    SecurityError,           # base of the three below
     PathTraversalError,
     InvalidInputError,
+    UntrustedWorkflowError,
 )
 ```
 
+Names joined onto a directory have their own validators
+(`validate_asset_reference`, `validate_output_reference`,
+`validate_prompt_reference`, `validate_subfolder`, `validate_workspace_name`,
+...): see SECURITY.md's *Name Validation* table.
+
 ## Locations a workflow supplies
 
-A media path, glob or URL that comes out of a workflow's arguments is not
-just a path - it is untrusted input choosing where the server reads. Use
-`dw/locations.py`, never `validate_path`/`validate_url` directly, for
-anything a workflow names:
+A path, glob or URL from a workflow's arguments is untrusted input choosing
+where the server reads. Use `dw/locations.py`, never
+`validate_path`/`validate_url` alone:
 
 ```python
 from dw.locations import (
-    validate_media_path,  # confined to the workflow dir / assets / outputs
-    validate_media_glob,  # the same, on a pattern's fixed prefix
-    contained_matches,  # each match re-checked on its real path
-    validate_media_url,  # no loopback / link-local / private host
+    validate_media_path,  # confined to the workflow dir / asset libraries / output root
+    validate_media_glob,  # the same, on the pattern's fixed prefix
+    contained_matches,    # each match re-checked on its real path
+    validate_media_url,   # no loopback / link-local / private / non-global host
     validate_model_name,  # a Hub repo id, or a contained path
 )
 ```
 
-Containment is checked before existence, so a refusal never discloses
-whether the file is there. All of it yields to `--trust-workflows`.
+Containment is checked before existence, so a refusal never discloses whether
+the file is there. Redirects are re-checked hop by hop (at most 5). A scheme
+other than http(s) is always refused; the rest yields to `--trust-workflows`.
 
 ## Common Patterns
 
 ```python
-# File paths
-safe_path = validate_path(user_path, allow_create=False)
+# File paths - pass a base directory, or nothing is confined
 safe_path = validate_path(user_path, base_dir="/allowed/dir")
-workflow_path = validate_workflow_path("workflow.json")
+workflow_path = validate_workflow_path("workflow.json", workflow_dir)
 output_path = validate_output_path(user_path, base_output_dir)
 
 # User input
-var_name = validate_variable_name("prompt")  # OK
+var_name = validate_variable_name("prompt")    # OK
 var_name = validate_variable_name("bad;name")  # raises InvalidInputError
 value = validate_string_input(user_input, max_length=1000)
-url = validate_url(user_url)  # http/https only
+url = validate_url(user_url)                   # http/https only
 
-# Subprocess (dw/ doesn't currently shell out anywhere - pattern for if/when it does)
+# Subprocess - shell=False, a fixed argument list
 import subprocess
 
-cmd = sanitize_command_args(["python", "-m", "dw.run", validated_path])
-subprocess.Popen(cmd, shell=False)
+cmd = sanitize_command_args(["python", "-m", "pip", "install", validated_spec])
+subprocess.run(cmd, shell=False)
 ```
 
 ## Error Handling
 
 ```python
 try:
-    path = validate_path(user_path)
+    path = validate_path(user_path, base_dir=root)
 except PathTraversalError as e:
     logger.error(f"Path traversal detected: {e}")
 except InvalidInputError as e:
@@ -81,33 +80,70 @@ except InvalidInputError as e:
 
 ## Input Constraints
 
-**Variable names:** Letters, numbers, underscores, hyphens. Must start with letter or underscore.
+**Variable names:** letters, digits, underscores, hyphens; must start with a
+letter or underscore.
 
 ```text
 OK:      prompt, num_images, my-variable, _private
 Invalid: my.var, var;name, $var, var name
 ```
 
-**File paths:** No `../` traversal. No `~/`, `/dev/`, `/proc/`, `/sys/`.
+**File paths:** no `..`, no `~/`, nothing under `/dev/`, `/proc/`, `/sys/`.
 
 ```text
 OK:      ./subdir/workflow.json, /full/path/workflow.json, builtin:h3_context_ir.json
 Invalid: ../../../etc/passwd, ~/secret.json
 ```
 
-**URLs:** http and https only.
+**URLs:** http and https only, with a host and no backslash.
 
 ```text
 OK:      https://example.com/image.jpg
 Invalid: file:///etc/passwd, ftp://server/file
 ```
 
+## Trust gate (`dw/trust.py`)
+
+Off by default; `dw-serve --trust-workflows` (and `dw-validate`) lifts it for
+the whole process. Untrusted, a workflow may not:
+
+- import outside `diffusers`, `torch`, `torchvision`, `transformers`,
+  `accelerate`, `peft`, `sdnq`, `torchao`, `optimum`, `gguf`, `bitsandbytes`,
+  `dw` (`pre_load_modules`, dotted `*_type`/`*_dtype`/`dtype`/`config_type`,
+  `constant:`);
+- name a type that is not a constructible class (a model, pipeline,
+  scheduler, tokenizer, processor, quantization config, auto factory,
+  attention processor or reference dataclass), or a `dtype` key that is not
+  a `torch.dtype`;
+- walk a `constant:` through `_` segments or out of those packages, or reach
+  a callable;
+- set `trust_remote_code`, `custom_pipeline` or `custom_generate` anywhere in
+  `from_pretrained_arguments`, `generate_kwargs`, `forward_params` or a task's
+  arguments.
+
+`validate_workflow` reports each refusal at its JSON path.
+
+## HTTP (`dw/server/http_security.py`)
+
+- `--token` / `DW_API_TOKEN` gates `/api/*` and `/mcp` (401 without it).
+  `?token=` works only on GET routes marked `query_token_ok`.
+- `/outputs`, `/inputs`, `/exports/*.zip` and the UI are **not** gated.
+- A foreign `Origin` is a 403; an unknown `Host` is a 400 unless bound to a
+  wildcard address.
+- A non-loopback bind without a token warns; `--mcp` there refuses to start.
+- `dw_mcp/` adds no auth; a remote `--url` needs a token. Over `dw.serve
+  --mcp`, `download_output` and `upload_asset(file_path)` are confined to the
+  server's workspace directories. See [MCP.md](MCP.md#security) and
+  [REMOTE.md](REMOTE.md).
+
 ## Common Errors
 
 | Error | Cause | Fix |
 | ----- | ----- | --- |
-| `Path contains dangerous pattern matching \.\.` | Path traversal | Use absolute or `./` relative paths |
-| `Invalid variable name: my.var` | Special characters | Use `my_var` instead |
-| `Invalid URL: URL scheme not allowed: file` | Non-http scheme | Use https or local file path |
+| `Path contains dangerous pattern matching \.\.` | Path traversal | Use an absolute or `./` relative path |
+| `Path outside allowed directory: ...` | Resolved path left `base_dir` | Put the file inside the root; for media use an `asset:` reference |
+| `Invalid variable name: my.var` | Special characters | Use `my_var` |
+| `Invalid URL: URL scheme not allowed: file` | Non-http scheme | Use https, or a local path |
 | `Refusing a redirect to '<target>' (from '<url>'): ...` | A media URL redirected to an internal host or a non-http scheme | Host the media where it is served directly; a refused hop is never fetched |
+| `Refusing to load ...`, ending with a pointer to `--trust-workflows` | The trust gate | Stay inside the allowed packages, or trust the workflow's source |
 | `Argument contains dangerous characters: ...` | Shell metacharacters | Remove `` ` `` `$` `\|` `&` `;` `>` `<` |
diff --git a/docs/SERVER.md b/docs/SERVER.md
index b176c63b..aeee053b 100644
--- a/docs/SERVER.md
+++ b/docs/SERVER.md
@@ -1,13 +1,13 @@
 # Server & Web UI
 
-`dw.serve` runs the workflow engine as a local HTTP server with a full web
-UI: browse and run workflows, build them in a form-based editor with
-introspection-driven autocomplete, watch jobs stream live progress, review
-past generations in a gallery, and manage the models on disk.
+`dw.serve` runs the workflow engine as an HTTP server with a web UI: browse
+and run workflows, edit them in a form-based editor driven by the live
+pipeline signatures, watch jobs stream progress, review and compare outputs,
+manage input assets and the models on disk.
 
 ```bash
 python -m dw.serve                       # http://127.0.0.1:8765
-python -m dw.serve --port 8000 --workflow-dir ./workflows --output-dir ./outputs --prompt-dir ./prompts
+python -m dw.serve --port 8000 --workflow-dir ./workflows --output-dir ./outputs --prompt-dir ./prompts --asset-dir ./assets
 
 # or point it at a workspace, which supplies all four directories
 python -m dw.serve --workspace ~/studio
@@ -21,7 +21,12 @@ python -m dw.serve --devices cuda:0,cuda:1   # one worker per card, two jobs at
 python -m dw.serve --trust-workflows      # only if nothing untrusted can reach POST /api/jobs - see Security model
 ```
 
-Installed as a package, the same server is `dw-serve`.
+Installed as a package, the same server is `dw-serve`. The other flags are
+`--output-layout {run,flat}` ([WORKSPACES.md](WORKSPACES.md#runs)) and
+`-l/--log_level`; `--help` gives each flag's default. Which directories a
+server uses, and in what order they are resolved, is in
+[WORKSPACES.md](WORKSPACES.md). Running it for other machines is
+[REMOTE.md](REMOTE.md); in a container, [DOCKER.md](DOCKER.md).
 
 ### The HTTP API
 
@@ -45,9 +50,9 @@ see [Authentication](#authentication). The sections below cover the
 [Jobs API](#jobs-api), the [Introspection API](#introspection-api) and
 [Files and models](#files-and-models) in prose.
 
-The server keeps a persistent GPU worker underneath: models stay
-loaded between runs, so re-running a workflow with a new prompt skips the
-load entirely.
+Each job runs in a persistent GPU worker ([WORKER_GUIDE.md](WORKER_GUIDE.md)):
+models stay loaded between runs, so re-running a workflow with a new prompt
+skips the load.
 
 ### The command line
 
@@ -78,170 +83,186 @@ The server reads these from its environment (e.g. `.env`) and its workers inheri
 
 | Variable | Default | Effect |
 | -------- | ------- | ------ |
+| `DW_API_TOKEN` | none | The bearer token, when `--token` is not given ([Authentication](#authentication)) |
+| `DW_WORKSPACE`, `DW_PROMPT_DIR`, `DW_OUTPUT_LAYOUT` | unset | The workspace, prompt library and output layout when no flag names them ([WORKSPACES.md](WORKSPACES.md)) |
+| `DW_PUBLIC_URL` | unset | The origin absolute URLs in responses are built from ([REMOTE.md](REMOTE.md#urls-handed-to-a-person)) |
 | `DW_QUANTIZED_CACHE` | off | `1` caches SDNQ on-the-fly-quantized components on disk, so a later cold load skips the quantize. Anything else is off. See [QUANTIZATION.md](QUANTIZATION.md#cached-on-the-fly-dw_quantized_cache) |
 | `DW_QUANTIZED_CACHE_DIR` | `<HF_HOME>/dw-quantized` | Where the cache lives; resolved to an absolute path at start and pinned for the workers |
 
 ### Choosing the card
 
-`--devices cuda:1` runs the worker on that card. The worker is pinned to it
-with `CUDA_VISIBLE_DEVICES` (and `DW_DEVICE=cuda`), so inside the worker the
-card is the only one it can see; a bare `cuda`, or no flag at all, pins
-nothing and behaves as before. The `devices` setting is the standing form of
-the same choice, and the flag wins over it. A list (`--devices cuda:0,cuda:1`)
-runs one worker per card, each running one job at a time. A card named twice,
-or a bare `cuda` in a list of several, is refused at startup, as is a card the
-machine lacks, with the message naming the cards that are present
-(`cuda:0 (NVIDIA GeForce RTX 4090)`, ...). A list survives a card being
-pulled: an entry the machine lacks is dropped with a warning naming it, and
-the server starts on the rest. Only a list none of whose cards is present
-(or a single absent card) is refused.
-
-Each job's record carries `device` - the card it ran on, as the server
-addresses it (`"cuda:1 NVIDIA GeForce RTX 3090"`), set when the job starts
-running and null for a job still queued and for jobs recorded before the
-field existed. Observed cost figures are bucketed by it: a figure counts the
-runs from cards of the same name as the one the server runs on now, so a
-3090's runs never inform a 4090's estimate. Rows from before jobs carried a
-`device` count only while the server is on the box's default card.
+`--devices cuda:1` runs the worker on that card, pinned with
+`CUDA_VISIBLE_DEVICES` (and `DW_DEVICE=cuda`), so inside the worker it is the
+only card visible. A bare `cuda`, or no flag, pins nothing. The `devices`
+setting is the standing form of the flag; the flag wins. A list
+(`--devices cuda:0,cuda:1`) runs one worker per card, each running one job at
+a time. Refused at startup: a card named twice, a bare `cuda` in a list, and
+a single card the machine lacks or a list none of whose cards is present -
+the message names the cards that are (`cuda:0 (NVIDIA GeForce RTX 4090)`,
+...). A list entry the machine lacks is otherwise dropped with a warning
+naming it, and the server starts on the rest.
+
+Each job's record carries `device`, the card it ran on as the server
+addresses it (`"cuda:1 NVIDIA GeForce RTX 3090"`): set when the job starts,
+null while queued and for jobs recorded before the field existed. Observed
+cost figures are bucketed by it: only runs from cards of the same name as
+the one the server runs on now count, so a 3090's runs never inform a
+4090's estimate. Rows with no `device` count only while the server is on the
+box's default card.
 
 ## The pages
 
-The UI is organised by workspace. A sidebar lists every workspace on the
-server; the selected one opens into **Overview, Workflows, Jobs, Gallery,
-Assets, Editor**, and the hash carries the workspace (`#/ws/studio/gallery`),
-so a link names where it points and an old `#/gallery` bookmark redirects to
-the last workspace you were in. Below the workspaces, **Shared** holds what
-every workspace sees — the prompt library, the `common` asset library and
-the read-only example workflows — and **Server** holds Models, Schema and
-Status.
-
-- **Overview** — a glance at the workspace: its recent outputs, its recent
-  jobs, its workflows (ones with a proof first, plus a filled **New
-  workflow** button), its asset count, and disk usage. Each panel loads and
-  fails independently, so a slow gallery does not hold the jobs list back
-  and a panel's own error shows in place of its content rather than reading
-  as an empty workspace. **Manage** on this page is where a workspace is
-  deleted (disabled for `default`); a workspace is created from the sidebar.
-- **Workflows** — every workflow on the workspace's own search path
-  (`--workflow-dir`), as cards with descriptions, output kinds, and variable
-  counts; the read-only ones from an `--examples-dir` are under Shared →
-  Examples instead, and opening one's Editor there saves a copy into the
-  current workspace. Folders one level deep become sections. Click through
-  to a run form generated from the workflow's variables, with the raw JSON
-  alongside.
-- **Prompts** — under Shared: the prompt library under `--prompt-dir`
-  (default: discovered
-  the way a CLI run discovers it, then pinned for every job, so the page and
-  `prompt:` resolution always agree), plus the read-only `prompts/` beside
-  each `--examples-dir`, so an example's `prompt:` references resolve: stored
-  prompts as
-  cards with descriptions, intended-model badges, and tags, foldered the
-  same way workflows are. Each opens in an editor with form, split, and
-  schema-aware JSON views, and an **Enhance with AI** panel that expands an
+The sidebar lists every workspace on the server (`+ new` creates one; names
+sharing a prefix fold into one group). The selected workspace opens into
+**Overview, Workflows, Jobs, Gallery, Assets, Editor**, and the hash names
+it (`#/ws/studio/gallery`), so a link and a reload land where they say; a
+pre-workspace bookmark (`#/gallery`) redirects to the last workspace used.
+Below the workspaces, **Shared** holds what every workspace sees - Prompts,
+the `common` Assets library and the read-only Examples - and **Server**
+holds Models, Schema and Status. The header carries the breadcrumb, a VRAM
+meter per card with a status popover (workers, queue), the API-token key
+icon, a finish-alert toggle, the theme toggle, and links to these docs and
+`/docs`. `?` lists the keyboard shortcuts.
+
+- **Overview** - the workspace at a glance: its note, recent outputs, the
+  running and last finished jobs, its workflows (ones with a proof image
+  first, and a **New workflow** button), its asset count and disk usage. Each
+  panel loads and fails on its own, so a slow gallery does not hold the jobs
+  list back and an error shows in place of that panel. **Manage** deletes
+  the workspace (disabled for `default`).
+- **Workflows** - every workflow on the workspace's own `--workflow-dir`, as
+  cards with the last image it made, its summary, the server's observed run
+  time, and `configures` / read-only marks. Shape buttons and trait chips
+  filter by what a workflow makes (the `shape`/`traits` vocabulary of
+  `GET /api/workflows`); folders become collapsible groups. **Shared →
+  Examples** is the same page over the `--examples-dir` trees. A card opens
+  the workflow's page: its recent outputs, a run form generated from its
+  variables (with `asset:`/`prompt:`/`output:` pickers), the raw JSON on
+  request, **Edit**, **Duplicate**, **Run** and delete. Opening an example in
+  the editor saves a copy into the current workspace.
+
+  ![Shared → Examples: workflow cards with their last image, shape and trait filters, and each card's summary and observed run time](img/ui-examples.jpg)
+
+- **Editor** - build or modify a workflow without writing JSON by hand.
+  Forms come from the live pipeline signatures
+  ([Introspection API](#introspection-api)), and references (`variable:`,
+  `previous_result:`, `asset:`, `prompt:`, `output:`, `builtin:`) autocomplete
+  or open a picker. Four views: **form**, **split** (form beside JSON, both
+  editable, applied when a side loses focus), **json** (Monaco with the
+  workflow schema), and **flow**, a read-only data-flow graph - a box per
+  step, an arrow per `previous_result` labelled with the argument it feeds,
+  entry steps marked, and fan-in steps flagged with their cartesian-product
+  multiplier where it is known statically. Clicking a step in the flow jumps
+  to it in the form. Validate, save and run from the same screen; a valid
+  verdict shows the run's plan - step and list counts, the estimate with its
+  basis, how many steps the step cache would serve, and the weights to
+  download first (`POST /api/validate`'s `plan`, rendered by
+  `ui/src/lib/plan.ts`).
+
+  ![The editor in split view: the variables as a form beside the workflow JSON](img/ui-editor.png)
+
+- **Jobs** - the workspace's queue and run history (history persists in
+  `~/.diffusers_helper/jobs.sqlite`), filterable by status; a waiting job
+  shows its place in line, can be moved, and shows `waiting_for` while held
+  for host RAM. A job's page streams step progress, denoise ticks, the phase
+  between them (loading, decoding, saving), its log, and its results as they
+  land - finals first, intermediates folded, and a section per
+  `result.subfolder` when a step chose one. **Cancel**, **Run again**,
+  **New seed**, and **Export** (gathers the run into the workspace's `exports/` with
+  `POST /api/jobs/{id}/export` and downloads the zip - the same bundle as
+  MCP's `export_job`; replacing an existing export asks first).
+
+  ![A finished job: a warning about a level jump at a cut, then the workflow as a data-flow graph from the edit and soundtrack through mixing and normalizing to the film](img/ui-job.png)
+
+- **Gallery** - everything in the workspace's output directory
+  (`<workflow>/<run id>/`, see [WORKSPACES.md](WORKSPACES.md#runs)), grouped
+  by workflow, with a text filter and **Final** / **Intermediate** /
+  **Starred** chips (it opens on finals). A file's detail panel shows its
+  settings, the job that made it, what it was made from, its run and file
+  notes, a star (`S`), **Keep as asset** (promotes it into the asset library
+  under a stable name, so a later workflow carries `asset:<name>` rather than
+  a run id that pruning would break), and **open as workflow**, which loads
+  an `embed_metadata` image's embedded definition into the editor with its
+  seed pinned. Each tile has a checkbox (shift-click extends a range,
+  **Select all** takes what the filter shows); a selection downloads as one
+  zip, is deleted in bulk (anything that fails to delete stays selected), or,
+  for two to four files, opens in Compare.
+
+  ![A gallery file's detail panel: the poster, its run and settings, a note, Keep as asset and open as workflow](img/ui-gallery-detail.jpg)
+
+- **Compare** - two to four outputs side by side, with the arguments that
+  differ between their runs called out, each run's time, and every video on
+  one transport so a difference in motion is seen at the same frame.
+
+  ![Compare: four takes of a musical's poster side by side, with each run's seed and time](img/ui-compare.jpg)
+
+- **Assets** - the input side of the gallery: the asset library as folder
+  groups of a contact-sheet grid with a detail panel, notes, upload (button
+  or drop; the library it lands in is named) and delete. A workspace's page
+  shows its own library and only the shared and examples assets its
+  workflows use (`used_by`); **Shared → Assets** shows the whole `common`
+  library. Entries a nearer library shadows are listed by name under their
+  library; read-only tiles have no checkbox.
+- **Prompts** (Shared) - the prompt library (resolved as in
+  [WORKSPACES.md](WORKSPACES.md#the-prompt-library) and pinned for every job,
+  so the page and `prompt:` resolution agree), plus the read-only `prompts/`
+  beside each `--examples-dir`: cards with descriptions, intended-model
+  badges and tags, foldered like workflows. Each opens in an editor with
+  form, split and JSON views and an **Enhance with AI** panel that expands an
   idea into a full prompt with a local language model (a preset per target
-  model family; the model runs as an ordinary queued job). A workflow
-  argument written as `prompt:name` loads the stored text at run time,
-  and deleting a prompt warns which workflows reference it.
-- **Jobs** — the queue and full run history (persisted in
-  `~/.diffusers_helper/jobs.sqlite`). A workspace's Jobs page lists its own;
-  Server → Status lists every workspace's with a filter. A running job streams step-by-step
-  progress, per-step denoising ticks, what each step is doing when it is not
-  denoising (loading a model, decoding, saving), and its result files as
-  they land.
-  A run whose steps chose a `result.subfolder` shows its results under
-  `final/` and `intermediate/` headings (or whatever the step named), the
-  deliverable first; one that chose none shows them as before.
-  Jobs can be cancelled mid-denoise and re-run with one click. A finished
-  job's **Export** button gathers the run into the workspace's `exports/`
-  on the server (`POST /api/jobs/{id}/export`) and downloads it as one zip -
-  the same bundle MCP's `export_job` makes; an export that already exists
-  asks before it is replaced.
-- **Editor** — build or modify workflows without writing JSON by hand.
-  Forms are generated from the live pipeline signatures (see
-  [introspection](#introspection)), references
-  (`variable:` / `previous_result:`) autocomplete from the workflow itself,
-  and three views — form, split, and raw JSON — edit the same definition.
-  The split view puts the form beside the JSON with both sides editable;
-  changes apply when a side loses focus. Validate, save, and run from the
-  same screen; a valid verdict is followed by the run's plan - the step
-  and list counts, the minutes from the workflow's `cost` block with its
-  basis, how many steps the step cache would serve, and the weights this
-  server would download first (`describePlan`, `ui/src/lib/plan.ts`,
-  reading `POST /api/validate`'s `plan`). A Monaco editor with the workflow JSON schema backs the
-  JSON views. A fourth view, **flow**, renders the workflow's data-flow
-  graph read-only: one box per step, arrows for each `previous_result`
-  reference labeled with the argument it feeds, entry-point steps marked
-  apart from steps that depend on earlier ones, and fan-in points -
-  steps combining more than one upstream producer - flagged with the
-  cartesian-product multiplier where it's known statically (e.g. a
-  literal `num_images_per_prompt` on both producers). It's a diagram of
-  the JSON, not a second way to edit it; clicking a step jumps to it in
-  the form view.
-- **Gallery** — everything in the selected workspace's output directory,
-  which the engine lays out as `<workflow>/<run id>/`. The folder filter groups a workflow's runs
-  together rather than listing each run separately, and a **subfolder** pick
-  beside the text filter - offered once any output landed in one - narrows
-  the grid to `final/`, `intermediate/` or whatever a step's
-  `result.subfolder` named; each run directory
-  also holds a `manifest.json` describing what produced it (see
-  [Workspaces](WORKSPACES.md#runs)). Images generated with
-  `embed_metadata` carry their full workflow definition and seed; **open as
-  workflow** loads that definition into the editor with the seed pinned, so
-  any image can be reproduced or riffed on. Each tile carries a checkbox
-  (shift-click extends a range, **Select all** takes whatever the filter
-  leaves showing); a selection can be downloaded as one zip or deleted in
-  bulk, which is how a directory that fills up over a few hundred runs gets
-  cleared out. Anything that fails to delete stays selected. **Keep as
-  asset** promotes one generated file into the workspace's asset library
-  under a stable name, so a later workflow can reference it as
-  `asset:<name>` instead of a run id that pruning would break.
-- **Models** — the Hugging Face hub cache: every cached repo with sizes,
-  revisions, and last-used dates, plus free disk space. Download a repo by
-  id with live progress (cancellable; partial files resume on retry), and
-  delete to free disk (refused while a job is running; the next workflow
-  that needs the model downloads it again). The page also shows the
-  installed diffusers version (with its commit for a git install) and can
-  upgrade it to GitHub HEAD - new model pipelines usually land there
-  before a PyPI release. The idle worker restarts on success so the next
-  job imports the new version; the upgrade is refused while a job runs.
-- **Schema** — the workflow JSON schema the running server validates
-  against, as a browsable tree: the document root plus every definition,
-  with types, required markers, defaults, enums, and descriptions.
-  `$ref` labels jump to their definition; a filter narrows the list.
-- **Server → Status** — what this server is and how to reach it: device,
-  version, bind address and LAN addresses, whether a token is required,
-  whether `/mcp` is mounted (with the `claude mcp add` line to connect to
-  it), and the directories in use — and, below, the queue across every
-  workspace. Workspaces are created from the sidebar and deleted from their
-  Overview.
+  model family, run as an ordinary queued job). Deleting a prompt warns which
+  workflows reference it.
+
+  ![The prompt editor: a stored H3 shot prompt with its description and tags, and the Enhance with AI panel](img/ui-prompt-editor.png)
+
+- **Models** (Server) - the Hugging Face hub cache: each cached repo with
+  sizes, revisions and last-used dates, plus free disk. Download a repo by id
+  with live progress (cancellable; partial files resume on retry), and delete
+  one to free disk (refused while a job runs). It also shows the installed
+  diffusers version (with its commit for a git install) and **Update from
+  GitHub** installs HEAD - new pipelines usually land there before a PyPI
+  release. Refused while a job is running or queued; on success the idle
+  worker restarts so the next job imports the new version. Pinning a commit
+  or reverting is the API's (`POST /api/system/diffusers/update`, below).
+
+  ![The model manager: each cached repo with its size, file count and last use](img/ui-models.png)
+
+- **Schema** (Server) - the workflow JSON schema the server validates
+  against, as a browsable tree: the root and every definition, with types,
+  required markers, defaults, enums and descriptions. `$ref` labels jump to
+  their definition; a filter narrows the list.
+- **Status** (Server) - what this server is and how to reach it: workers per
+  card, bind address and LAN addresses, whether a token is required, whether
+  `/mcp` is mounted, a **Connect from another machine** panel with the
+  browser URL and the `claude mcp add` line for a chosen address, the
+  resolved directories, and the Caches panel (below) - then the queue and
+  history across every workspace, with a workspace filter.
+
+  ![The Status page: host, version, a worker per RTX 3090 with its memory, the queue, and the bind address](img/ui-status-dark.png)
 
 ## Workspaces
 
-One server can hold several workspaces — each with its own `workflows/`,
-`assets/` and `outputs/`, all sharing the root's one prompt library. The
-root's own folders are the workspace named `default`.
-
-Every scoped route takes an optional `?workspace=<name>`; omitting it means
-`default`, so nothing written against a single-workspace server changes
-meaning. `POST /api/jobs` also accepts `"workspace"` in the body, and a job
-holds onto the directories it was submitted with — through the run, a rerun,
-and when history serves its files back. `GET /api/jobs` spans every workspace
-unless one is named.
-
-A workspace is a namespace, not a security boundary: the API token is
-all-or-nothing. See [Workspaces](WORKSPACES.md#several-workspaces-on-one-server).
-
-The sidebar lists every workspace, and `+ new` there creates one; a
-workspace's own Overview page is where it is deleted. Server → Status shows
-the resolved directories and the `claude mcp add` line for connecting an
-agent from another machine, plus the queue across every workspace. Its
-Caches panel lists dw's disk caches (`GET /api/caches`: size, files, last
-used, the volume's free space; the quantized cache also expands into a table
-of its entries with a delete button each) with a Clear button per cache that asks first,
-quoting what clearing costs, and shows the server's refusal when a job is
-running:
-
-![The Server page before the sidebar: address picker, generated claude mcp add line, resolved directories — the workspace list it shows now lives in the sidebar](img/ui-server-dark.png)
+One server can hold several workspaces, each with its own `workflows/`,
+`assets/` and `outputs/`, sharing the root's prompt library; the root's own
+folders are the workspace named `default`. Every scoped route takes an
+optional `?workspace=<name>` (omitted means `default`), `POST /api/jobs` also
+accepts `"workspace"` in the body, and a job keeps the directories it was
+submitted with through the run, a rerun, and when history serves its files.
+`GET /api/jobs` spans every workspace unless one is named. A workspace is a
+namespace, not a security boundary: the API token is all-or-nothing. The
+layout, the reserved names and how each client picks one are in
+[WORKSPACES.md](WORKSPACES.md#several-workspaces-on-one-server).
+
+The Status page's Caches panel lists dw's disk caches (`GET /api/caches`:
+size, files, last used, the volume's free space; the quantized cache expands
+into a table of its entries, each with a delete button) with a Clear button
+per cache that asks first, quoting what clearing costs, and shows the
+server's refusal when a job is running:
+
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="img/ui-caches-dark.png">
+  <img alt="The Caches panel: the inductor, triton and quantized caches with size, file count, last use and free disk, each with a clear button" src="img/ui-caches.png">
+</picture>
 
 ## Jobs API
 
@@ -254,66 +275,68 @@ running:
 | `GET /api/jobs/{id}/workflow` | The workflow the job ran: `{id, definition, realized, seed_variable}`. `seed_variable` names the variable a `new_seed` rerun would draw into (null when the workflow has none), read from the workflow as written rather than the realized copy, whose seed is pinned. `realized: true` is the copy the run itself wrote (`workflow.json` in its run directory), with arguments, seed, prompts and `output:latest` pinned; `false` falls back to the submitted definition, which is what a job from before run tracking has. 404 means neither is readable - the job itself still is. The equivalent MCP tool is `get_job_workflow` (see [MCP.md](MCP.md#diagnose)) |
 | `POST /api/jobs/{id}/export?workspace=&overwrite=` | Gather one finished job into `<workspace>/exports/<job id>/`: `workflow.json`, `manifest.json`, `job.json`, `README.md`, `assets/`, `inputs/`, `outputs/`. 201 with the file list, total bytes, anything it could not find, a `zip_url`, and the three JSON files inline. 404 unknown job, 409 for a job still running or an existing export without `overwrite` |
 | `GET /exports/{id}.zip?workspace=` | The same tree as one archive, built on request rather than kept as a second copy. Entries are named `<job id>/<relative path>`. Ungated exactly as `/outputs` is |
-| `GET /api/jobs/{id}/events` | Server-sent events stream; `?after=N` / `Last-Event-ID` replay missed events, so reconnects are lossless. Every event carries `seq` and `at` - seconds since the job started (since it was queued, for the events before that) - so a step's cost is a subtraction: `step_start` to `generating` is what a reused pipeline still pays before it runs; `generating` is emitted when the pipeline call starts, so `generating` to the first `pipeline_step` is the prompt and reference encoding plus the first denoise pass (`timing.encode_s` in the manifest is the encoding alone; see [Step timing](WORKSPACES.md#step-timing)). The server keeps only the last 200 events, so on a long job the early ones are gone |
-| `GET /api/jobs/{id}/event-log?after=-1&limit=200` | The same events as the SSE stream, as one JSON page: `{id, status, events, last_seq, truncated, note}`. `after` is exclusive; page by passing back the previous `last_seq`. A job restored from history serves the bounded event tail persisted with it; a job that finished before events were retained returns an empty list and a `note` saying so. |
-| `POST /api/jobs/{id}/cancel` | Cooperative cancel (takes effect at the next step boundary or denoise step) |
+| `GET /api/jobs/{id}/events` | Server-sent events stream; `?after=N` / `Last-Event-ID` replay missed events, so reconnects are lossless. Every event carries `seq` and `at` - seconds since the job started (since it was queued, for the events before that) - so a step's cost is a subtraction: `step_start` to `generating` is what a reused pipeline still pays before it runs; `generating` is emitted when the pipeline call starts, so `generating` to the first `pipeline_step` is the prompt and reference encoding plus the first denoise pass (`timing.encode_s` in the manifest is the encoding alone; see [Step timing](WORKSPACES.md#step-timing)). A job in memory keeps every event; one served from history (after a restart, or once it is older than the 20 most recent finished jobs) keeps only its last 200 |
+| `GET /api/jobs/{id}/event-log?after=-1&limit=200&kinds=` | The same events as the SSE stream, as one JSON page: `{id, status, events, last_seq, truncated, note}`. `after` is exclusive; page by passing back the previous `last_seq`. `limit` is capped at 1000. `kinds` (repeatable) keeps events whose `event` or `kind` is one of those named (`log`, `warning`, `phase_stall`, ...). A job restored from history serves the bounded event tail persisted with it; a job that finished before events were retained returns an empty list and a `note` saying so. |
+| `POST /api/jobs/{id}/cancel` | Cooperative cancel: takes effect at the next step boundary or denoise step. During a model load or a task step, which cannot be interrupted, a `cancel_pending` event says it waits for that to finish |
 | `POST /api/jobs/{id}/rerun` | Re-queue a finished job's spec. Body `{"new_seed": true}` draws a fresh seed into the workflow's seed variable instead of repeating the original arguments; 400 when the workflow pins its seed to a literal or names none. A plain rerun of a seeded workflow is served whole from the step cache — the earlier run's files, republished with `reused: true`, generating nothing. The new job's summary and detail carry `rerun_of`: the id of the job it reran (absent on a fresh submit) |
 | `POST /api/jobs/{id}/move` | Reorder a queued job: `{"direction": "up"\|"down"\|"front"\|"back"}`. Job listings carry each waiting job's `queue_position`, and `waiting_for` while one is held for host RAM. |
 
+### Dispatch
+
 Each worker runs one job at a time, so with one device (the default) one job
-runs at a time; submissions queue in order, and the waiting portion of the
+runs at a time; submissions queue in order, and the waiting part of the
 queue can be reordered. With several `--devices` the dispatcher walks the
 queue in order and gives each job the first free card, in `--devices` order,
 that fits it. A job that fits no free card keeps its place while a smaller
 one behind it takes the card (backfill).
 
-What a job needs: the workflow's declared `vram_estimate` projected over the
-job's arguments (hard: a job bigger than every card is refused at submit with
-a 400 naming the largest card); else the smallest `cost` entry's `vram_gb` for
-this device type (soft: if no card is that big, the job runs on any card, as
-on one card); else any card. A card's size is its GiB rounded up (a 3090
-counts as 24), which a `cost` figure is compared to; a declared estimate is
-held to the card's GiB to one decimal (a 3090's 23.6), the same ceiling
-admission checks it against. Cancel reaches only the worker running that job, and a worker
-crash fails only its own job. Each dispatch sets its worker's Linux
-`oom_score_adj` to 1000 and every other live worker's back to the value it was
-spawned with (the server's own, e.g. 200 under a systemd user unit - the kernel
-refuses an unprivileged write below that), so a host-RAM squeeze kills the
-newest job whatever the workers' sizes; nothing is written with one worker, and
-a write the kernel refuses is a warning on the job. Under systemd the unit needs
-`OOMPolicy=continue` (`scripts/dw-serve.service` sets it): the default `stop`
-answers one worker's OOM kill by stopping the whole server, every other job
-with it. A
-queued job may also carry a projected host-RAM peak (see
-`host_ram_need`, #869). When it fits a free card, dispatch checks host RAM
-too: available is `MemAvailable` less, for each running job, what that job's
-projected peak still has to add beyond its worker's resident size now, and
-the job is held while its peak exceeds available plus the resident size of
-the idle worker it would reuse. A running job with no figure is charged
-nothing extra, since `MemAvailable` already reflects what it holds. Backfill
-applies: a smaller job behind a held one may start. With no other job
-running a job is never held, so it waits only for running jobs, never
-forever; a job with no figure is never held; and where the host's RAM cannot
-be read (macOS without psutil) there is no gate. With one card only one job
-runs, so the gate is never exercised. A held job is re-checked every 10 s,
-since memory can free outside dw. The hold emits one `queue_wait` event
-(`reason: "host_ram"`, message `waiting for host RAM: needs ~N GB, ~M GB
-free`; not a `warning`, so nothing lands in the job's `warnings`), and job
-detail and summaries carry `waiting_for` - that message - only while the job
-is queued and held. The web UI shows it beside "#n in line". The gate never
-refuses a submit (#243); it only delays a start, and the OOM ranking above
-(#881) stays the backstop for a wrong projection.
-Among the free cards a job fits, dispatch prefers the one it has an affinity
-for: a rerun goes to the card that ran the original, and any job to the card
-whose worker last ran the same workflow (the same file, or an inline
-definition's `id`) - that worker's pipelines and step cache are warm, and a
-worker's step cache and any model the next workflow does not load are freed
-when the workflow changes, while a model both workflows load (same weights
-identity) stays warm. `clear_memory` still empties the card by hand. A busy preferred card is not
-waited for. A validation's cache probe and its `plan.estimate` ask about the
-card the job would be dispatched to now, and the estimate names it in
-`priced_for`. Memory is per card: `GET /api/memory` and `POST
-/api/memory/clear` take an optional `device`.
+What a job needs, in order:
+
+- the workflow's declared `vram_estimate` projected over the job's
+  arguments - hard: a job bigger than every card is refused at submit with a
+  400 naming the largest card;
+- else the smallest `cost` entry's `vram_gb` for this device type - soft: if
+  no card is that big, the job runs on any card;
+- else any card.
+
+A card's size is its GiB rounded up (a 3090 counts as 24) when compared to a
+`cost` figure; a declared estimate is held to the card's GiB to one decimal
+(a 3090's 23.6), the ceiling admission uses. Cancel reaches only the worker
+running that job, and a worker crash fails only its own job.
+
+**OOM ranking (#881).** With more than one worker, each dispatch sets its
+worker's Linux `oom_score_adj` to 1000 and every other live worker's back to
+its spawn-time value (the server's own, e.g. 200 under a systemd user unit;
+the kernel refuses an unprivileged write below that), so a host-RAM squeeze
+kills the newest job. A refused write is a warning on the job. Under systemd
+the unit needs `OOMPolicy=continue` (`scripts/dw-serve.service` sets it): the
+default `stop` answers one worker's OOM kill by stopping the whole server.
+
+**Host-RAM hold (#869, #243).** A queued job may carry a projected host-RAM
+peak (`host_ram_need`; see
+[Host-memory history](WORKSPACES.md#host-memory-history)). When it fits a
+free card, dispatch also checks host RAM: available is `MemAvailable` less
+what each running job's projected peak still has to add beyond its worker's
+resident size now (nothing for a running job with no figure), and the job is
+held while its peak exceeds available plus the resident size of the idle
+worker it would reuse. Backfill applies. A job is never held when no other
+job is running, nor when it has no figure, nor where host RAM cannot be read
+(macOS without psutil), and with one card the gate is never exercised. A held
+job is re-checked every 10 s. The hold emits one `queue_wait` event (message
+`waiting for host RAM: needs ~N GB, ~M GB free`; not a `warning`), and job
+detail and summaries carry `waiting_for` - that message - only while held.
+The gate never refuses a submit; it only delays a start, and the OOM ranking
+is the backstop for a wrong projection.
+
+**Affinity.** Among the free cards a job fits, dispatch prefers a rerun's
+original card, else the card whose worker last ran the same workflow (the
+same file, or an inline definition's `id`), whose pipelines and step cache
+are warm. A busy preferred card is not waited for. When a worker's workflow
+changes, its step cache and any model the next workflow does not load are
+freed ([WORKER_GUIDE.md](WORKER_GUIDE.md)). A validation's cache probe and
+its `plan.estimate` ask about the card the job would go to now, named in
+`priced_for`. `GET /api/memory` and `POST /api/memory/clear` take an
+optional `device`.
 
 ### Step timing
 
@@ -341,6 +364,14 @@ Every event in the stream carries a `seq` and an `event` name:
 | `warning` | a step finds something wrong with what it is about to write | `message`, plus a `kind` and the figures behind it (`level_spread`: `spread_db`, `measure`, `command`; `fps_mismatch`: `declared_fps`, `source_fps`; `audio_no_headroom`: `file`, `peak_dbfs` - a deliverable at or above -0.5 dBFS, which an mp3 or AAC encode decodes over full scale; `step_elided`: `step`, `overridden_by` when a supplied argument is what made it unreferenced; `cache_stats_unavailable`: `cache_type` - the installed diffusers moved the private hook state dw reads, so the step's cache counts are null; the run is unaffected). Also appended to the job's `warnings`, prefixed with the step it fired in - the event keeps the moment, `warnings` keeps it where a caller polling the finished job will look, since a warning about the artifact outlives the run that noticed it |
 | `cache_stats` | a pipeline call that set a `cache` finishes its denoise, once per call | `step`, `segment` (only on a chain segment, e.g. `segment 1/3`), and the fields of `timing.cache`: `type`, `threshold`, `forwards`, `computed`, `skipped`, `hit_rate`. Counts are null for a cache diffusers keeps no per-forward decision for. For watching live (`get_job_events(kinds=["cache_stats"])`); the manifest entry's `timing.cache` is the record |
 | `workflow_end` | the run finishes | `manifest` |
+| `queue_wait` | a queued job is held for host RAM (below) | `reason: "host_ram"`, `message` |
+| `cancel_pending` | a cancel arrives during a phase that cannot be interrupted | `phase` |
+
+A `warning` with `kind: "phase_stall"` is emitted when a phase has gone 30 s
+without an event, and every 30 s while it stays silent (`phase`,
+`seconds_since_phase_start`, `seconds_since_last_progress`). It is
+informational - long silent stretches are normal for some models - and,
+unlike other warnings, never lands in the job's `warnings`.
 
 A step spends most of its wall clock outside the denoise loop, and
 `pipeline_step` cannot see any of it. `phase` is what fills that silence:
@@ -369,34 +400,25 @@ to learn where a long render is:
 | `seconds_since_event` | how long since anything at all happened - the number that separates a slow run from a stuck one |
 | `denoise_step`, `denoise_total_steps` | the denoise loop's counter, `null` until it starts |
 
-A null `denoise_step` under `generating` is the pipeline's lead-in - encoding
-the prompt and every reference - which emits nothing and runs well over a
-minute on a large video model. It is the encode plus the first denoise
-pass, and on MiniMax H3 the first pass dominates and varies by card. The
-reference encode (`vae_encoder`) is about 6 s for an image reference and
-about 50-53 s for a 5 s 960x544 video reference. Measured on lem: 187 s for
-an image reference (job `f90a041af26a`), and 508 s on cuda:1
-(`f5aefcdd5e26`, first pass 422 s) against 165 s on cuda:0
-(`6961876f11d3`, first pass 86 s) for the same image + video job. The keys
-are always present so that lead-in can be told from a loop that has stopped
-advancing: `seconds_since_event` is a stall signal once `denoise_step` is a
-number, or in any phase other than `generating`.
-
-The lead-in is no longer silent, though: each of a modular pipeline's
-top-level blocks emits a `log` naming it as it starts (`before_encode`,
-`text_encoder`, `vae_encoder`, `denoise`, `decode` on H3), so the last event
-says which one the run is inside. Only a `SequentialPipelineBlocks` is
-narrated this way - a conditional container picks one branch rather than
-running them all, and walking its sub-blocks would be a wrong answer bought
-with a progress message.
-
-It is a coarse one even then. A step's cost is not uniform when the pipeline
-configures a transformer block cache (`"cache": {"type": "first_block"}`):
-most steps are served from it in seconds and every few steps one is computed
-in full, so the same healthy run emits four `pipeline_step` events in 20 s
-and then nothing for 133 s. Liveness is `denoise_step` having moved between
-polls minutes apart, not silence measured against a fixed threshold - on H3
-that threshold would have to exceed ~140 s to mean anything.
+A null `denoise_step` under `generating` is the pipeline's lead-in: prompt
+and reference encoding plus the first denoise pass, which runs well over a
+minute on a large video model. On MiniMax H3 the first pass dominates and
+varies by card (measured on one box: 86 s on one card, 422 s on another, for
+the same image + video job); the reference encode (`vae_encoder`) is about
+6 s for an image and 50-53 s for a 5 s 960x544 video. Each top-level block of
+a `SequentialPipelineBlocks` modular pipeline emits a `log` as it starts
+(`before_encode`, `text_encoder`, `vae_encoder`, `denoise`, `decode` on H3),
+so the last event says which block the run is in; a conditional container
+is not narrated, since it runs one branch. `seconds_since_event` is a stall
+signal once `denoise_step` is a number, or in any phase other than
+`generating`.
+
+Even then it is coarse. With a transformer block cache
+(`"cache": {"type": "first_block"}`) most steps are served in seconds and
+every few one is computed in full, so a healthy run can emit four
+`pipeline_step` events in 20 s and then nothing for 133 s. Liveness is
+`denoise_step` having moved between polls minutes apart, not silence against
+a fixed threshold; on H3 that threshold would have to exceed ~140 s.
 
 ## Introspection API
 
@@ -404,9 +426,10 @@ The editor's forms come from these; they are just as usable from scripts:
 
 - `GET /api/pipelines`, `GET /api/pipelines/{name}` — diffusers pipeline
   classes and their call signatures
-- `GET /api/classes?kind=...`, `GET /api/classes/{name}?target=call|init|load` —
-  any allowed class (diffusers + registered extension modules), described
-  for calling, constructing, or `from_pretrained` loading
+- `GET /api/classes?kind=pipelines|models|schedulers|quantization`,
+  `GET /api/classes/{name}?target=call|init|load` (default `init`) — any
+  allowed class (diffusers + registered extension modules), described for
+  calling, constructing, or `from_pretrained` loading
 - `GET /api/tasks` — the task commands and processors
 - `GET /api/tasks/{command}` — a task's argument schema, read from its
   registered implementation's real signature
@@ -420,11 +443,10 @@ The editor's forms come from these; they are just as usable from scripts:
 - `GET /api/guides/{name}?section=` — one section of a guide; section names
   match loosely. Without a `section` the answer is the guide's index - its
   opening, its first section, and `sections`/`withheld` naming the rest -
-  rather than the whole file, which for WORKFLOW_GUIDE.md is ~19.6k tokens
-  in one call (#101). Served by the engine so an MCP client
-  at another version reads the guides for the server it is driving, not
-  its own. A checkout serves the repo's `docs/`; an install the copy
-  `build_dist.sh` puts under `dw/docs/`
+  rather than the whole file (#101). Served by the engine, so an MCP client
+  at another version reads the guides for the server it drives. A checkout
+  serves the repo's `docs/`; an install the copy `build_dist.sh` puts under
+  `dw/docs/`
 - `POST /api/validate` — schema validation plus signature-level argument
   warnings for pipeline and task steps (catches the typo before the model
   loads); `warnings` also names an entry key of a list-driven variable that
@@ -584,25 +606,20 @@ The editor's forms come from these; they are just as usable from scripts:
   already resident), with the `drivers` the figure is for, `since`, and
   `unclassified_runs` when a run's persisted events were trimmed past its
   `loading` phase. Runs are bucketed by the workflow's declared
-  `cost_drivers` - the variables that move its cost - so a 345-frame run
-  never informs a 124-frame figure; a list driver buckets on its length. A
-  workflow declaring no drivers falls back to runs that overrode nothing at
-  all, and a run whose every step was a step-cache hit is excluded. The
-  compact view carries only `observed_minutes` (cold) and `observed_runs`;
-  `GET /api/workflows/{name}/variables` carries the whole block beside the
-  defaults. Derived from the job rows in one query - so the figures outlive
-  a pruned run directory - and cached against the jobs table's high-water
-  mark rather than a file mtime, because a job landing changes every figure
-  and changes no file. `observed` never replaces `cost`: a maintainer's
-  claim on a named card and this machine's last week are different things.
+  `cost_drivers`, so a 345-frame run never informs a 124-frame figure; a
+  list driver buckets on its length. A workflow declaring no drivers counts
+  only runs that overrode nothing, and a run whose every step was a
+  step-cache hit is excluded. The compact view carries only
+  `observed_minutes` (cold) and `observed_runs`;
+  `GET /api/workflows/{name}/variables` carries the whole block. Derived from
+  the job rows, so the figures outlive a pruned run directory, and cached
+  against the jobs table's high-water mark. `observed` never replaces `cost`.
   A `models/` entry
   takes its `shape` and `traits` from the template it configures and keeps
   its own `cost`. A list-driven workflow (one with a `for_each` step) also
   carries `lists`: per list variable, the fields an entry takes, the steps
-  run over it and the default's length. Enough to choose a workflow and know
-  what to pass it without reading each one; the variable defaults are
-  deliberately left out, being an order of magnitude more payload on a
-  listing the UI reloads. Cached by file mtime
+  run over it and the default's length. Variable defaults are left out (an
+  order of magnitude more payload); cached by file mtime
 
   Optional query params narrow and shrink it:
   `?shape=&traits=&configures=&include_models=&view=compact`. `shape` keeps
@@ -614,8 +631,7 @@ The editor's forms come from these; they are just as usable from scripts:
   `shape`, `traits`, `cost`, `kinds`, `variable_names` and `lists` (carried
   only when the workflow has a list-driven step, like `configures`), and
   lists templates only unless `include_models=true` or a `configures` asks
-  otherwise. With no params the response is what it always was, plus the new
-  fields
+  otherwise
 - `GET/PUT/DELETE /api/workflows/{name}` — read, save, delete workflow files
   (confined to `--workflow-dir`); `PATCH` applies a JSON merge patch (RFC 7396,
   `application/merge-patch+json` or JSON) to the stored version and saves the
@@ -651,7 +667,9 @@ The editor's forms come from these; they are just as usable from scripts:
   queue an enhancement as an ordinary job whose saved text file is the
   result
 - `GET /api/gallery`, `GET /api/gallery/{name}/metadata`,
-  `DELETE /api/gallery/{name}` — outputs and their embedded metadata. Each
+  `DELETE /api/gallery/{name}` — outputs and their embedded metadata. Delete
+  takes a file, or a run directory (`<workflow>/<run id>`) to remove the whole
+  run; a run's directory goes with its last media file. Each
   gallery entry carries `folder` (the workflow identity, the run id dropped)
   and `subfolder` (what followed the run id - the `final`/`intermediate` a
   step's `result.subfolder` chose, `''` when it chose none); `?folder=` and
@@ -665,7 +683,10 @@ The editor's forms come from these; they are just as usable from scripts:
   the file, unless the step's `result.role` declared it), and `GET /api/gallery?role=final` lists only
   those (repeatable; a null role counts as final). See `dw/roles.py` for how
   a role is decided;
-  the metadata route reports the file's `role` and the run's `inputs` too
+  the metadata route reports the file's `role` and the run's `inputs` too,
+  and `findings`: each level problem the probed soundtrack shows
+  (`full_scale`, `near_silent`), measured against `dw/audio_qc.py`'s
+  thresholds, in the assess route's finding shape
 - `GET /api/gallery/{name:path}/download` — download an output file
 - `GET /api/builtin-workflows` — the packaged fragments a sub-workflow step
   names as `builtin:<name>.json` (`dw/workflows/`), which no workflow listing
@@ -673,16 +694,33 @@ The editor's forms come from these; they are just as usable from scripts:
 - `GET /api/gallery/{name:path}/thumbnail` — a small JPEG of an image, or of
   a video's first frame; 404 for other kinds. `name` may be an `asset:`
   reference, which the editors' asset picker uses for its grid
-- `GET /api/gallery/{name}/metadata` also carries `findings`: each level
-  problem the probed soundtrack shows (`full_scale`, `near_silent`), measured
-  against `dw/audio_qc.py`'s thresholds, in the assess route's finding shape
 - `GET /api/gallery/{name}/image?max_dimension=768&crop=x,y,w,h&max_bytes=&format=auto`
   — an image output or `asset:` sized for an inline answer: cropped (clamped),
   fitted to `max_dimension`, and with `max_bytes` (a base64 budget) halved
   until it fits. Headers `X-DW-Original-Size`, `X-DW-Returned-Size`, and when
   they apply `X-DW-Crop` and `X-DW-Downscaled-To`. Takes `?token=`
-- `GET /api/gallery/{name}/frames` takes `max_total_bytes`: over it, every
-  tile shrinks to one shared size and `downscaled_to` says which
+- `GET /api/gallery/{name}/frames` — frames of a video output or `asset:`
+  as PNG tiles, with exactly one selector: `at` (comma list of seconds or
+  `frame:N`), `count` (an evenly spaced contact sheet) or `seams` (`true` or
+  1-based seam numbers: the frames either side of each shot boundary).
+  `boundaries` and `names` give the shots; without them, the shots the run's
+  manifest (or a kept asset's sidecar) recorded. `max_dimension` (default
+  512), `crop=x,y,w,h` in source pixels, and `max_total_bytes`: over it every
+  tile shrinks to one shared size and `downscaled_to` says which (MCP
+  `get_output_frames`)
+- `GET /api/gallery/{name}/audio?start=&duration=` — the soundtrack of an
+  output or `asset:` as WAV, or an excerpt; headers `X-DW-Duration` and, for
+  an excerpt, `X-DW-Excerpt-Start` / `X-DW-Excerpt-Duration`. A whole
+  audio-only file is served in its own encoding. 404 for a file with no
+  soundtrack (MCP `get_output_audio`)
+- `GET /api/gallery/{name}/assess?probe=&detail=` — every assessment probe
+  that applies to a finished cut (`analyze_shots`, `analyze_seams`,
+  `analyze_sync_drift`), run in the server process on one decode: merged
+  `findings`, `rules_applied`, `rules_skipped`, and `not_applicable` per probe
+  the file cannot feed. `detail=true` adds each probe's answer under
+  `probes`; `probe` names one and answers with its full body. Findings are
+  places to look, not verdicts (`dw/assessment_rules.py`; MCP
+  `assess_output`)
 - `POST /api/gallery/compare` — two to eight outputs side by side, and how far each is
   from a baseline; read-only, queues nothing. Body: `names` (2-8 gallery names or
   `asset:` references, one row each), `at` (1-8 moments, seconds or `"frame:N"`;
@@ -722,26 +760,18 @@ The editor's forms come from these; they are just as usable from scripts:
   pixel limit; 422 undecodable file
 - `POST /api/gallery/archive` — `{"names": [...]}` (1-1000) bundles a
   multi-file selection into one zip, named by each file's gallery-relative
-  path so output subfolders survive. A browser cannot zip on its own and
-  throttles a burst of single downloads, so the gallery's bulk download
-  goes through here; an unknown or out-of-directory name 404s the whole
-  request rather than yielding a partial archive
+  path so output subfolders survive; the gallery's bulk download. An unknown
+  or out-of-directory name 404s the whole request rather than yielding a
+  partial archive
 - `GET /api/workspaces`, `POST /api/workspaces` (`{"name": ...}`),
   `DELETE /api/workspaces/{name}?acknowledged=true` — the workspaces on this
-  server. The workspace root's own `workflows/assets/outputs` are the
-  `default` workspace and a named one is a subdirectory beside them, sharing
-  the root's one prompt library. Delete answers with what it would remove and
-  refuses until acknowledged, refuses the default, and refuses a workspace
-  with jobs still queued. Each listed workspace carries a `usage`
-  (`{files, bytes}`) — roughly how much disk its own folders hold, walked at
-  most once a minute per workspace and deliberately approximate; the shared
-  prompt library counts against the `default` workspace alone rather than
-  once per workspace. A workspace is a namespace, **not** a security
-  boundary: the API token is all-or-nothing
-
-  `exports/` sits beside the workspace's own folders, holding one directory per
-  exported job. It is a reserved name: no workspace can be called `exports`, and
-  the folder is never listed as one.
+  server ([layout](WORKSPACES.md#several-workspaces-on-one-server)). Delete
+  answers with what it would remove and refuses until acknowledged, refuses
+  `default`, and refuses a workspace with jobs still queued. Each listed
+  workspace carries `usage` (`{files, bytes}`), an approximate walk of its
+  own folders at most once a minute; the shared prompt library counts
+  against `default` alone. `exports/` beside the workspace folders holds one
+  directory per exported job and is never listed as a workspace
 - `POST /api/workspaces/{destination}/copy` (`{"source": ..., "runs": [...],
   "assets": [...]}`) — copy runs and assets from one workspace into another.
   A `runs` entry is `<workflow>/<run id>`, or a bare `<workflow>` for all its
@@ -776,56 +806,43 @@ The editor's forms come from these; they are just as usable from scripts:
   none
 - `POST /api/assets/keep` (`{"name": ..., "asset_name": ..., "overwrite": false, "shared": false}`)
   — keep a generated file as an input asset under a stable name, returning
-  its `asset:` reference. A run's files are named by the run that made them,
-  which is the wrong thing for a later workflow to depend on: `latest` moves
-  and a pinned run id breaks when outputs are pruned. The copy happens inside
-  the workspace and is a hard link where the filesystem allows one, so
-  keeping one frame of a large render costs no second copy of it. Refuses an
+  its `asset:` reference, which survives the run's outputs being pruned. The
+  copy stays inside the workspace and is a hard link where the filesystem
+  allows one. Refuses an
   existing name unless `overwrite`. `asset_name` may name a folder and takes
   the kept file's extension when it carries none (a contradicting one is a
   400) — the same rule the upload route follows, and what keeps a kept asset
   from landing under an extensionless name the library listing never shows.
   `"shared": true` keeps it in
-  `<root>/common/assets` instead — the library every workspace under the root
-  shares, which is where a recurring cast belongs
+  `<root>/common/assets` instead, the library every workspace shares
 - `DELETE /api/assets/{name}` — remove one file from the asset library,
   deleting from whichever library on the search path holds it (the
   workspace's own before the shared one, the order `asset:` resolves in).
   An asset from a read-only examples library answers 403, the same as a
   read-only prompt or workflow; a name nothing holds answers 404
 - `POST /api/assets/archive` — `{"names": [...]}` (1-1000) bundles a
-  multi-file asset selection into one zip, named by each file's
-  library-relative path, which is the name its `asset:` reference carries.
-  The gallery archive's counterpart on the input side; it resolves down the
-  same search path a run does, so a selection spanning this workspace's
-  library, the shared one and an examples tree downloads as one archive, and
-  an unknown or out-of-library name 404s the whole request rather than
-  yielding a partial one. A duplicate name (repeated in the selection, or
-  differing only by leading/trailing whitespace) collapses onto the one zip
-  entry. Media (image/video/audio) stores rather than deflates, unless it's
-  a raw format that still compresses (`.bmp`, `.wav`) - everything else the
-  libraries hold is an already-compressed container, and the response does
-  not start until the archive is complete, so deflating it is latency the
-  caller waits through for nothing. Everything else - `.json`, `.md`,
-  `.txt`, an unrecognized extension - deflates; so does the export zip's
-  text files (`workflow.json`, `manifest.json`, `job.json`, the README)
-- `POST /api/uploads?filename=...` — the raw bytes of one image, video or audio file,
-  or a `.cube` 3D LUT for `apply_lut`
-  (200MB ceiling, checked from `Content-Length` before a byte is read, and
-  again on the body; extension held to the allowed image, video, audio and
-  `.cube` list), saved
-  into the asset library's `uploads/` subfolder - the shared library at
-  `<root>/common/assets` when `shared=true`, this workspace's own otherwise -
-  under a generated name, or under `asset_name` when one is given (`cast/priya-voice.wav`, folders allowed,
-  the uploaded file's extension assumed, confined to the library the way
-  `keep_output`'s name is).
-  Answers 201 with `reference` - `asset:uploads/<name>`, the reference a saved
-  workflow can carry and still resolve on a later run - and `url`, the same
-  file under the `/inputs` mount, for the editor's preview. A server started
-  without an asset library falls back to the output directory's `uploads/`
-  and an absolute path. This is how the UI's file pickers get a local file
-  onto the machine that will run the workflow. The body is the file itself,
-  so no multipart parser is needed for a single-file upload
+  multi-file asset selection into one zip, each entry named by its
+  library-relative path (the name its `asset:` reference carries). It
+  resolves down the same search path a run does, so a selection spanning the
+  workspace's library, the shared one and an examples tree is one archive;
+  an unknown or out-of-library name 404s the whole request, and a duplicate
+  name (repeated, or differing only by surrounding whitespace) collapses to
+  one entry. Media is stored rather than deflated, since it is already
+  compressed, except raw formats that still compress (`.bmp`, `.wav`);
+  everything else deflates, as do the export zip's text files
+- `POST /api/uploads?filename=&asset_name=&shared=&note=` — the raw bytes of
+  one image, video or audio file, or a `.cube` 3D LUT for `apply_lut`, as the
+  body (no multipart). 200MB ceiling, checked from `Content-Length` before a
+  byte is read and again on the body; the extension is held to the allowed
+  image, video, audio and `.cube` list. Saved under the asset library's
+  `uploads/` - the shared library at `<root>/common/assets` when
+  `shared=true`, else this workspace's own - with a generated name, or as
+  `uploads/<asset_name>` (folders allowed, the file's extension assumed,
+  confined the way `keep_output`'s name is). Answers 201 with `reference`
+  (`asset:uploads/<name>`, which a saved workflow can carry and still resolve
+  later) and `url`, the same file under `/inputs` for previews. 409 when the
+  workspace has no asset library. This is how the UI's file pickers get a
+  local file onto the machine that runs the workflow
 - `GET /api/models`, `DELETE /api/models?repo={repo_id}` — hub cache
   inventory and deletion
 - `GET /api/caches`, `DELETE /api/caches/{name}`, `DELETE /api/caches/{name}/entries/{entry}` - dw's disk caches of derived
@@ -1038,30 +1055,27 @@ export DW_API_TOKEN="some-long-random-string"
 python -m dw.serve
 ```
 
-When a token is configured, every `/api/*` request must carry
-`Authorization: Bearer <token>` or gets a 401. The UI's own static files and
-`/outputs` (generated media) stay reachable without it - the page has to
-load far enough for a user to enter the token, and an `<img>`/`<script>`
-tag cannot attach a header anyway. That is why an active document served
-from `/outputs` or `/inputs` is sandboxed (Security model, above). A few GET API routes additionally accept the
-token as a `?token=...` query parameter, because the browser loads them
-without being able to set headers: the SSE stream,
-`GET /api/jobs/{id}/events` (`EventSource`), and the gallery grid's
-`GET /api/gallery/{name}/thumbnail` (an `<img>` tag). The three `/download`
-routes (gallery output, workflow, prompt) accept `?token=...` the same way,
-since a download button is a plain `<a href download>` navigation that
-cannot set a header either. That is a deliberate,
-narrower trade-off (a token that can leak into logs or browser history for
-those URLs) rather than a general alternative to the header - every other
-route accepts the header only.
-
-The web UI has a one-time token field (next to the theme toggle) that
-stores the token in `localStorage` and attaches it to every API call,
-including the two query-parameter routes above. The MCP server reads the
-same `DW_API_TOKEN` variable (or `dw-mcp --token`), so one export
-configures both ends - see [MCP.md](MCP.md). It is a convenience, not a
-credential vault - anyone with access to the browser profile can read it
-back out of `localStorage`.
+When a token is configured, every `/api/*` and `/mcp` request must carry
+`Authorization: Bearer <token>` or gets a 401. The UI's static files,
+`/outputs`, `/inputs` and `/exports/{id}.zip` stay reachable without it: the
+page has to load far enough for a user to enter the token, and an
+`<img>`/`<script>` tag cannot attach a header. That is why an active document
+served from `/outputs` or `/inputs` is sandboxed (Security model, above).
+
+Six GET routes also accept the token as `?token=`, because a browser loads
+them without being able to set a header: the SSE stream
+`GET /api/jobs/{id}/events` (`EventSource`), `GET /api/gallery/{name}/thumbnail`
+and `GET /api/gallery/{name}/image` (`<img>` tags), and the three `/download`
+routes (gallery output, workflow, prompt; `<a download>`). The allowance is
+bound to those routes, not to a path suffix. It is a narrower trade-off - a
+token in those URLs can leak into logs or browser history - and every other
+route, `/mcp` included, takes the header only.
+
+The web UI's key icon in the header stores the token in `localStorage` and
+attaches it to every API call, as a query parameter on the routes above; a
+`/?token=...` link saves it the same way. Anyone with the browser profile can
+read it back. The MCP server reads the same `DW_API_TOKEN` (or
+`dw-mcp --token`), so one export configures both ends - see [MCP.md](MCP.md).
 
 A token configured this way is a single shared static secret, not a login
 system: there is one token, checked with a constant-time comparison, and no
@@ -1070,6 +1084,5 @@ server on a LAN or beyond; it is not a substitute for a real network
 boundary (a firewall, a VPN, or simply binding to `127.0.0.1`) for anything
 more exposed than that.
 
-Running on another machine: [REMOTE.md](REMOTE.md) is the end-to-end recipe
-- token, firewall, systemd unit, the browser, `--mcp`, and what to do beyond
-the LAN.
+Running on another machine - token, firewall, systemd unit, the browser,
+`--mcp`, and beyond the LAN - is [REMOTE.md](REMOTE.md).
diff --git a/docs/TASKS.md b/docs/TASKS.md
index 75163ed8..ac404664 100644
--- a/docs/TASKS.md
+++ b/docs/TASKS.md
@@ -13,52 +13,39 @@ Tasks are utility operations that run outside of a pipeline step. Use them for p
 }
 ```
 
-Any task that runs a model accepts a `"device"` argument to pin where it runs -
-useful for keeping a helper model (a captioner, an upscaler) off the accelerator a
-loaded pipeline is using, or on a second one. `"device"` is listed on every task's
-schema, since it is always safe to pass: a task that runs no model - `slice_audio`,
-`compose_text`, and the like - just ignores it.
-
-Task argument schemas are discoverable: `GET /api/tasks/{command}` on the
-[server](SERVER.md) returns each command's arguments read from its registered
-implementation's real signature, the web editor builds task forms from them,
-and workflow validation flags task-argument typos the same way it flags
-pipeline ones.
-
-A signature carries no domain, though, so the numbers whose domain is not a
-judgement call are declared separately (`dw/task_domains.py`) and validation
-reports one outside it as an error at its JSON path: a count of frames or
-seconds to cut, and a sample rate or frame rate, have to be above zero, and an
-offset to start at zero or above. Those are refused rather than interpreted -
-`num_frames: -10` used to answer with the track minus its last ten frames and
-`target_sample_rate: 0` with the original samples under a 44100 Hz header, both
-reported as clean successes. The commands refuse the same values at run time,
-which is what catches one that arrived from a `variable:` or an earlier step
-rather than being written in the file.
-
-A numeric argument is read the same way by every command and by validation
-(`whole_number` and `real_number` in `dw/task_domains.py`, the only numeric
-coercion in `dw/tasks/`). A number may arrive as a JSON number or as a numeric
-string - a `variable:` resolved from the command line is text - so `3`, `3.0`,
-`"3"` and `"3.0"` are all the whole number 3, and `"23.976"` is a frame rate.
-A whole-number argument (a frame count, an index, a sample rate, a pixel size)
-refuses a fractional value such as `3.5` or `"22050.5"` rather than truncating
-it, naming the argument and saying "whole number". Every numeric argument
-refuses `true`/`false`, text that is no number (`"abc"`) and an infinite or NaN
-value. What validation refuses, the run refuses with the same sentence, and
-the reverse.
-`Task.run` reads every declared numeric argument before the command sees it
-(`coerce_arguments`), so a handler is handed a number, never the string it
-arrived as: `exposure: "0.5"` grades exactly as `0.5` does. An argument whose
-only rule is being a number, such as `grade`'s `exposure` or a gain in dB,
-declares the `finite` domain, so validation reads it too.
+Every task's schema lists a `"device"` argument (`cpu`, `cuda:1`, ...) that pins
+where a helper model runs, for example off the accelerator a loaded pipeline is
+using. A task that runs no model ignores it.
+
+`GET /api/tasks/{command}` on the [server](SERVER.md) (and `get_task` over MCP)
+returns each command's arguments, read from its implementation's real
+signature. The web editor builds task forms from it, and validation flags an
+unknown task argument as it flags a pipeline one.
+
+**Numeric arguments.** Domains a signature cannot carry are declared in `dw/task_domains.py`, and
+validation reports a value outside one as an error at its JSON path: a count of
+frames or seconds and a sample or frame rate must be above zero, an offset zero
+or above. The commands refuse the same values at run time, which catches a value
+that arrived from a `variable:` or an earlier step.
+
+Every command and validation read numbers the same way (`whole_number` and
+`real_number` in `dw/task_domains.py`). A number may be a JSON number or a
+numeric string, since a `variable:` from the command line is text: `3`, `3.0`,
+`"3"` and `"3.0"` are the whole number 3, and `"23.976"` is a frame rate. A
+whole-number argument (a frame count, an index, a pixel size, `resample_audio`'s
+`target_sample_rate`) refuses a fractional value such as `3.5`, naming the
+argument and saying "whole number"; it is never truncated. Every numeric
+argument refuses `true`/`false`, non-numeric text and an infinite or NaN value.
+Validation and the run refuse with the same sentence. `Task.run` coerces every
+declared numeric argument before the command sees it (`coerce_arguments`), so
+`exposure: "0.5"` grades exactly as `0.5` does.
 
 ## Adding a task
 
-A task is one function, registered with `@register_command` in
-`dw/tasks/registry.py`. Its signature is its argument schema, and everything
-validation knows about it beyond the signature is declared on the same
-decorator, so a new task cannot be half-registered (#692):
+A task is a handler registered with `@register_command` (`dw/tasks/registry.py`).
+The function named by `implementation` is the argument schema: its signature is
+what `get_task` reports and validation checks. Every validate-time rule is
+declared on the same decorator, so a task cannot be half-registered (#692):
 
 ```python
 from .registry import register_command
@@ -66,55 +53,58 @@ from ..task_domains import NON_NEGATIVE, POSITIVE
 from ..task_problems import my_task_errors
 
 
+def my_task(source, num_frames, start_frame=0, mode="fast"): ...
+
+
 @register_command(
     "my_task",
+    implementation="dw.tasks.my_task.my_task",
     domains={"num_frames": POSITIVE, "start_frame": NON_NEGATIVE},
+    whole_numbers=("num_frames", "start_frame"),
     choices={"mode": ("fast", "best")},
     static_check=my_task_errors,
     media_arguments=("source",),
 )
-def my_task(source, num_frames, start_frame=0, mode="fast"): ...
+def _handle_my_task(task, arguments, previous_pipelines):
+    return my_task(**arguments)
 ```
 
-- `domains` - each numeric argument's domain (`dw/task_domains.py`'s
-  `POSITIVE`, `NON_NEGATIVE`, ...): refused at validation at
-  `steps[i].task.arguments.<name>`, and again at run time when the task calls
-  `check_arguments`.
-- `choices` - each argument's literal choices; `get_task` lists them.
-- `static_check` - a function of the step's `arguments` that returns
-  `(argument, message)` pairs for rules across arguments. It lives in
-  `dw/task_problems.py` (or, for the media-timing rules - window, slice, fit -
-  `dw/task_domains.py`), named `*_errors`, and is registered to exactly one
-  command - or, when it needs the task's own parser, in the task's module
-  behind an import on use, as `attribute_voices`' does.
+- `domains` - each numeric argument's domain (`POSITIVE`, `NON_NEGATIVE`, ...
+  in `dw/task_domains.py`), refused at validation at
+  `steps[i].task.arguments.<name>` and at run time.
 - `whole_numbers` - the numeric arguments that take only a whole number; the
-  rest of `domains` take any real number. Validation refuses `3.5` for one
-  of these, as the task's own `whole_number` call does at run time.
+  rest of `domains` take any real number.
+- `choices` - each argument's literal choices; `get_task` lists them.
+- `static_check` - a function of the step's `arguments` returning
+  `(argument, message)` pairs for rules across arguments, named `*_errors`, in
+  `dw/task_problems.py` (the media-timing rules for window, slice and fit are in
+  `dw/task_domains.py`), registered to exactly one command. One that needs the
+  task's own parser lives in the task's module behind an import on use, as
+  `attribute_voices`' does.
 - `media_arguments` - an argument that reads a file under a name that does not
-  say so (`source`, `media`, `lut`, `clip`, `track`). Validation confines it as it does `image`
-  or `*_video`; an argument left out of it is not path-checked.
+  say so (`source`, `media`, `lut`, `clip`, `track`). Validation confines it as
+  it does `image` or `*_video`; an argument not listed is not path-checked.
 
 `TASK_ARGUMENT_DOMAINS`, `TASK_ARGUMENT_CHOICES`, `TASK_STATIC_CHECKS`,
-`TASK_WHOLE_NUMBER_ARGUMENTS` and `TASK_MEDIA_ARGUMENTS` are read-only views of these declarations;
-`tests/test_task_registry_rules.py` pins them to the registry and every name in
-them to the command's signature.
+`TASK_WHOLE_NUMBER_ARGUMENTS` and `TASK_MEDIA_ARGUMENTS` are read-only views of
+these declarations; `tests/test_task_registry_rules.py` pins them to the
+registry and to each command's signature.
 
 A new task is:
 
 1. a module in `dw/tasks/` holding the function and its decorator;
 2. one import line for that module in `dw/tasks/task.py` - the registry is
-   filled by importing `dw.tasks.task`, and modules are not discovered
-   automatically, so a module it does not import registers nothing;
+   filled by importing `dw.tasks.task`, so a module it does not import
+   registers nothing;
 3. a section in this file.
 
-A task that loads Hub weights also declares `required_weights(arguments)`,
-named on the decorator as
-`register_command(required_weights="dw.tasks.<module>.required_weights")`. It
-returns `[{"repo": ...}]` for the step's arguments (an empty list when the
-repo is not known yet, e.g. a `previous_result:` `model_name`), and resolves
-each repo with `argument_or_default` from `dw/tasks/weights.py`, the same call
-the loader makes, so the declaration and the load cannot name different
-repos. A validate's `downloads_required` lists those repos; `tests/test_task_weights.py` pins it.
+A task that loads Hub weights also declares
+`register_command(required_weights="dw.tasks.<module>.required_weights")`. The
+hook returns `[{"repo": ...}]` for the step's arguments (empty when the repo is
+not known yet, e.g. a `previous_result:` `model_name`) and resolves each repo
+with `argument_or_default` from `dw/tasks/weights.py`, the call the loader
+makes, so the declaration and the load name the same repo. A validate's
+`downloads_required` lists those repos; `tests/test_task_weights.py` pins it.
 
 ## Image Processing
 
@@ -135,7 +125,7 @@ Generate control images for ControlNet pipelines:
 | `openpose` | Pose estimation |
 | `dw_pose` | DW pose estimation |
 | `mlsd` | Line segment detection |
-| `lineart` | Line art extraction |
+| `lineart` | Coarse line art extraction |
 | `lineart_standard` | Standard line art |
 | `hed` | HED edge detection |
 | `scribble` | Scribble-style edges |
@@ -148,22 +138,20 @@ Generate control images for ControlNet pipelines:
 | `depth_estimator` | Depth hint generation |
 | `depth_estimator_tensor` | Depth hint as tensor |
 
-A validate's `downloads_required` lists the repo each detector fetches when it is not cached: `depth` `Intel/dpt-hybrid-midas`; `depth_estimator` and `depth_estimator_tensor` `Intel/dpt-large`; the `controlnet_aux` detectors `lllyasviel/Annotators` (`teed` `fal-ai/teed`, `anyline` `TheMistoAI/MistoLine`, `sam` `ybelkada/segment-anything`); `segmentation` `openmmlab/upernet-convnext-small`. `zoe_depth` and `dw_pose` fetch outside the Hugging Face cache and are not listed.
+A validate's `downloads_required` lists the repo each detector fetches when it is not cached: `depth` `Intel/dpt-hybrid-midas`; `depth_estimator` and `depth_estimator_tensor` `Intel/dpt-large`; the pretrained `controlnet_aux` detectors (`mlsd`, `normal_bae`, `lineart`, `openpose`, `hed`, `scribble`, `pidi`, `midas`, `zoe`, `leres`) `lllyasviel/Annotators`; `teed` `fal-ai/teed`; `anyline` `TheMistoAI/MistoLine`; `sam` `ybelkada/segment-anything`; `segmentation` `openmmlab/upernet-convnext-small`. `canny`, `shuffle` and `lineart_standard` fetch nothing. `zoe_depth` and `dw_pose` fetch outside the Hugging Face cache and are not listed.
 
-All accept an `image` argument with processing parameters:
+Each takes an `image`. The detector's own parameters go beside it in `arguments`, not inside the `image` dict, whose keys other than `location` are ignored:
 
 ```json
 {
     "task": {
         "command": "canny",
         "arguments": {
-            "image": {
-                "location": "https://example.com/photo.jpg",
-                "low_threshold": 50,
-                "high_threshold": 200,
-                "detect_resolution": 1024,
-                "image_resolution": 1024
-            }
+            "image": { "location": "https://example.com/photo.jpg" },
+            "low_threshold": 50,
+            "high_threshold": 200,
+            "detect_resolution": 1024,
+            "image_resolution": 1024
         }
     }
 }
@@ -175,13 +163,13 @@ All accept an `image` argument with processing parameters:
 | ------- | ----------- | --------------- |
 | `remove_background` | Remove image background | |
 | `resize_center_crop` | Crop to a centered square, then stretch to width x height - distorts a non-square target | `width`, `height` |
-| `resize_resample` | Resample to nearest 64px multiple | |
+| `resize_resample` | Scale the short side to `resolution` (default 1024), each side rounded to a multiple of 64 | `resolution` |
 | `resize_rescale` | Resize to exact dimensions | `width`, `height` |
 | `resize_bucket` | Snap to closest model-native aspect ratio | `resolution`, `ratios`, `alignment` |
 | `crop_square` | Center crop to square | |
 | `recenter_crop` | Re-frame around a chosen point at a chosen scale, so a series of images registers on one feature; the window may run off the source | `center_x`, `center_y`, `crop`, `width`, `height`, `fill` |
-| `add_border_and_mask` | Add border with alpha mask | |
-| `add_border_and_mask_with_size` | Border with specific dimensions | `width`, `height` |
+| `add_border_and_mask` | Add a black border, as fractions of each side, and its mask; returns `{bordered_image, mask}` | `zoom_all`, `zoom_left`, `zoom_right`, `zoom_up`, `zoom_down`, `overlap` |
+| `add_border_and_mask_with_size` | Fit inside `width` x `height`, bordered to that size, with its mask | `width`, `height`, `overlap` |
 | `strip_exif` | Remove all EXIF/metadata from image | |
 | `add_watermark` | Add visible text watermark | `text`, `position`, `opacity`, `font_size`, `color`, `margin` |
 | `get_image_size` | Return `{width, height}` dict | |
@@ -190,7 +178,7 @@ A validate's `downloads_required` lists `briaai/RMBG-2.0` for `remove_background
 
 ### EXIF Stripping
 
-Remove all EXIF metadata, GPS coordinates, camera info, and timestamps from images for privacy-safe preprocessing:
+`strip_exif` returns a copy with pixel data only: no EXIF, GPS, camera or timestamp metadata.
 
 ```json
 {
@@ -204,11 +192,9 @@ Remove all EXIF metadata, GPS coordinates, camera info, and timestamps from imag
 }
 ```
 
-Returns a clean copy with pixel data only — no embedded metadata. Useful as a first step when processing user-uploaded images.
-
 ### Watermark Embedding
 
-Add a visible text watermark to images for responsible AI compliance:
+`add_watermark` draws a visible text watermark:
 
 ```json
 {
@@ -236,7 +222,7 @@ Add a visible text watermark to images for responsible AI compliance:
 
 ### Aspect Ratio Bucketing
 
-The `resize_bucket` command snaps an image to the closest model-native aspect ratio, then resizes with 64-pixel alignment. This avoids distortion and ensures the model generates at a resolution it was trained on.
+`resize_bucket` snaps an image to the closest model-native aspect ratio, then resizes with `alignment`-pixel rounding.
 
 ```json
 {
@@ -261,14 +247,76 @@ The `resize_bucket` command snaps an image to the closest model-native aspect ra
 
 For example, a 1600x900 photo (16:9) at resolution 1024 becomes 1792x1024. A 800x600 photo (4:3) becomes 1344x1024.
 
+### ingredients_grid
+
+Lay separate images (a character, a prop, a location) out on one canvas as a
+reference sheet: [`frame_grid`](#frame_grid)'s counterpart for separate images,
+and the still `ltx2/reference-sheet` conditions on. Pure PIL.
+
+```json
+{
+    "name": "sheet",
+    "task": {
+        "command": "ingredients_grid",
+        "arguments": {
+            "images": ["asset:hero.png", "asset:sword.png", "asset:castle.png"],
+            "width": 768,
+            "height": 448
+        }
+    },
+    "result": { "content_type": "image/png" }
+}
+```
+
+| Argument | Default | Description |
+| -------- | ------- | ----------- |
+| `images` | required | Images in reading order: references, paths or `{"location": ...}` dicts; `gather:` of a `for_each` step splices in |
+| `width`, `height` | 768, 448 | Canvas size in pixels |
+| `layout` | `auto` | `rows`, `panels` or `auto` |
+| `fit` | `contain` | `contain` scales each image to fit whole and pads with `background`; `cover` fills its cell and crops the overflow |
+| `gap` | 8 | Pixels between cells, and kept clear of the canvas edge |
+| `background` | `white` | Canvas colour, a name or `#hex` |
+| `max_images` | 12 | More images than this is refused, never silently dropped |
+
+`rows` keeps each image's aspect ratio and justifies each row to the canvas
+width, searching the in-order row splits for the least wasted canvas, overflow
+and row-height spread. `panels` gives every image an equal grid cell, with a
+short last row centred. `auto` uses `rows` for up to 3 images and `panels` for
+more. Transparency is flattened onto white. Returns one RGB image of exactly
+`width` x `height`.
+
+To build the sheet inside `ltx2/reference-sheet`, add this step first and set
+`static_sheet`'s `video` to `previous_result:sheet` in place of the
+`reference_sheet` asset, keeping `width` and `height` the clip's own:
+
+```json
+{
+    "name": "sheet",
+    "task": {
+        "command": "ingredients_grid",
+        "arguments": {
+            "images": ["asset:hero.png", "asset:sword.png", "asset:castle.png"],
+            "width": "variable:width",
+            "height": "variable:height"
+        }
+    },
+    "result": { "content_type": "image/png", "save": false }
+}
+```
+
 ### grade
 
-`grade` is the finishing pass: exposure, contrast, tonal range, local contrast,
-white balance, colour and a matte or vignette look, on CPU, for an image or a
-video. A video is graded one frame at a time and keeps its frame count, frame
-rate and audio. Unlike the other image commands, its input is named `media`,
-and a file path or `asset:`/`output:` reference to a video is accepted
-directly. An alpha channel passes through untouched.
+The four finishing commands (`grade`, `sharpen`, `film_grain`, `apply_lut`)
+share these rules. They run on CPU, on an image or a video. Their input is named
+`media`, and a file path or `asset:`/`output:` reference to a video is read with
+its audio. A video is processed one frame at a time and keeps its frame count,
+frame rate and audio. An alpha channel passes through untouched. A value outside
+an argument's range is refused by `validate_workflow`, naming the argument and
+the range, and again at run time when it arrives from a `variable:` or an
+earlier step; `get_task(<command>)` reports each range.
+
+`grade` adjusts exposure, contrast, tonal range, local contrast, white balance,
+colour and a matte or vignette look.
 
 ```json
 {
@@ -288,8 +336,8 @@ directly. An alpha channel passes through untouched.
 ```
 
 Every argument is optional and defaults to identity, so a step with none
-returns the input pixels. The step's job events carry one "applied" log naming
-every argument that is not at its identity value.
+returns the input pixels. The step's job events carry one log naming every
+argument not at its identity value, or "no adjustment (all identity)".
 
 | Argument | Range | Identity | Effect |
 | -------- | ----- | -------- | ------ |
@@ -306,24 +354,15 @@ every argument that is not at its identity value.
 | `fade` | 0.0..1.0 | 0.0 | Lifts the black floor and flattens the shadows toward it, white kept: a matte look |
 | `vignette` | -1.0..1.0 | 0.0 | Darkens (positive) or lightens (negative) toward the corners; the centre is untouched |
 
-**Order of operations:** exposure, contrast, whites/blacks,
-highlights/shadows, clarity, temperature/tint, saturation, fade, vignette. The
-order is fixed whatever order the arguments are written in: fade comes after
-the tonal controls so `blacks` cannot pull its floor back down, and vignette is
-last so it darkens the finished picture.
-
-A value outside its range is refused by `validate_workflow`, naming the
-argument and the range, and again at run time when it arrives from a
-`variable:` or an earlier step. `get_task("grade")` reports each range.
+**Order of operations** (fixed, whatever order the arguments are written in):
+exposure, contrast, whites/blacks, highlights/shadows, clarity,
+temperature/tint, saturation, fade, vignette.
 
 ### sharpen
 
-`sharpen` is an unsharp mask, on CPU, for an image or a video: the image is
-blurred with a Gaussian, and the difference between the image and its blur (the
-detail) is added back, scaled by `amount`. Like `grade`, its input is named
-`media`, and a file path or `asset:`/`output:` reference to a video is read with
-its audio. A video is sharpened one frame at a time and keeps its frame count,
-frame rate and audio. An alpha channel passes through untouched.
+`sharpen` is an unsharp mask: the difference between the image and its
+Gaussian blur (the detail) is added back, scaled by `amount`. The
+[finishing rules](#grade) apply.
 
 ```json
 {
@@ -346,17 +385,10 @@ frame rate and audio. An alpha channel passes through untouched.
 | `radius` | above zero | 2.0 | The Gaussian blur's radius in pixels: the scale of the detail that is sharpened |
 | `threshold` | 0..255 | 0 | The smallest difference, in channel levels, between a pixel and its blur that gets sharpened; smaller differences are left alone. A whole number of levels: a fractional value is rounded |
 
-A value outside its range is refused by `validate_workflow`, naming the
-argument and the range, and again at run time when it arrives from a
-`variable:` or an earlier step. `get_task("sharpen")` reports each range.
-
 ### film_grain
 
-`film_grain` adds seeded film grain, on CPU, to an image or a video. Its input
-is `media`, as in `grade` and `sharpen`: a video is processed one frame at a
-time and keeps its frame count, frame rate and audio, and an alpha channel
-passes through untouched. The grain is Gaussian noise, strongest in the
-midtones and weaker toward black and white.
+`film_grain` adds seeded Gaussian grain, strongest in the midtones and weaker
+toward black and white. The [finishing rules](#grade) apply.
 
 ```json
 {
@@ -380,26 +412,17 @@ midtones and weaker toward black and white.
 | `chroma` | 0.0..1.0 | 0.0 | 0 puts the same grain on every channel (brightness only, hue unchanged); 1 draws independent grain per channel; values between mix the two |
 | `seed` | a whole number, 0 or above | the workflow's or step's seed | Seeds the grain |
 
-**Seeding:** one generator is made per step from the seed and consumed frame by
-frame, so every frame of a video gets different grain and the same seed
-reproduces the whole output byte for byte. A workflow run always has a seed (a
-random one is drawn when the workflow names none, and recorded in the run's
-manifest), so rerunning a job reproduces its grain. An explicit `seed`
-argument overrides the workflow's or step's seed. The step's job events carry
-a log naming the seed used.
-
-A value outside its range is refused by `validate_workflow`, naming the
-argument and the range, and again at run time when it arrives from a
-`variable:` or an earlier step. `get_task("film_grain")` reports each range.
+**Seeding:** one generator per step, consumed frame by frame, so each frame
+gets different grain and the same seed reproduces the output byte for byte. A
+run always has a seed (drawn at random when the workflow names none, and
+recorded in the manifest), so rerunning a job reproduces its grain. The step's
+job events log the seed used.
 
 ### apply_lut
 
-`apply_lut` colours an image or a video through a 3D lookup table (LUT), read
-from a `.cube` file or built from a `palette`, on CPU. Its input is `media`, as in `grade`: a video is
-processed one frame at a time and keeps its frame count, frame rate and audio,
-and an alpha channel passes through untouched. Each pixel's colour is looked up
-with trilinear interpolation, and the result is blended with the original by
-`strength`.
+`apply_lut` colours through a 3D lookup table (LUT), read from a `.cube` file
+or built from a `palette`, with trilinear interpolation, blended with the
+original by `strength`. The [finishing rules](#grade) apply.
 
 ```json
 {
@@ -429,11 +452,10 @@ to light, spaced evenly along the luminance axis: the first colour sits at
 black, the last at white. Each pixel keeps its own luminance (Rec. 709 luma)
 and takes its hue and chroma from the palette at that luminance, interpolated
 between neighbouring colours, so shadows lean toward the first colour and
-highlights toward the last. Near black and white the chroma shrinks as far as
-it must to stay in range. The 33³ table is built in memory, never written to
-disk, and applied exactly as a `.cube` is (same interpolation, same `strength`
-blend). The same palette always gives the same result, so one palette passed
-as a `variable:` to every shot of a series gives them one look:
+highlights toward the last. Near black and white the chroma shrinks to stay in
+range. The 33³ table is built in memory and applied as a `.cube` is. The same
+palette always gives the same result, so one palette passed as a `variable:` to
+every shot of a series gives them one look:
 
 ```json
 {
@@ -456,13 +478,11 @@ as a `variable:` to every shot of a series gives them one look:
 ```
 
 A palette that is not a list, a colour that is not `#rrggbb`, or fewer than 2
-or more than 16 colours refuses, naming the bad entry - at `validate_workflow`,
-and again at run time for a palette that arrives from an earlier step.
+or more than 16 colours refuses, naming the bad entry.
 
 **Where the file may be.** A literal path is held to the same roots as any
-media argument, and must end in `.cube`; anything else is refused, naming only
-what the workflow wrote. The file is read and parsed once per step, not once
-per frame.
+media argument and must end in `.cube`; anything else is refused, naming only
+what the workflow wrote. The file is parsed once per step.
 
 **The `.cube` the parser takes** is the 3D subset of the format, read strictly.
 Anything else refuses the step, naming the file and the line:
@@ -477,16 +497,14 @@ Anything else refuses the step, naming the file and the line:
 - exactly size³ data rows of three finite numbers, each in 0..1, listed red
   fastest, then green, then blue, as the format specifies.
 
-Some grading applications write values slightly outside 0..1 or a wider domain;
-those files are refused rather than clipped. `strength` outside 0..1 is refused
-by `validate_workflow` and again at run time when it arrives from a `variable:`
-or an earlier step.
+A file with values slightly outside 0..1 or a wider domain, as some grading
+applications write, is refused rather than clipped.
 
 ### A finishing chain
 
-The three finishing commands chain through `previous_result:`, each taking the
-last one's output as its `media`. Grade first, sharpen the graded picture, and
-add grain last so it is not itself sharpened:
+The finishing commands chain through `previous_result:`, each taking the last
+one's output as its `media`. Grade first, sharpen, and add grain last so it is
+not itself sharpened:
 
 ```json
 {
@@ -538,9 +556,8 @@ The extracted frame is always a PIL image.
 
 ### stills_to_video
 
-Hold each still on screen for a set time and get one silent clip back - an
-animatic of a storyboard. The result joins with `concat_videos` and takes a
-score from `pair_audio` or the `assemble-and-score` template like any shot.
+Hold each still on screen for a set time, as one clip: an animatic of a
+storyboard. The result joins and scores like any shot.
 
 ```json
 {
@@ -567,28 +584,21 @@ score from `pair_audio` or the `assemble-and-score` template like any shot.
 | `durations` | Yes | Seconds per still: one number for all, or one per still |
 | `fps` | No | Frame rate, default 24, at most 120 |
 
-Each still gets `max(1, round(duration * fps))` frames, so a duration shorter
-than a frame still shows for one. The clip is at most 600 seconds and 36000
-frames in total; those cap its length, not its memory. The frames are held as
-uint8 pixels, so the command also refuses, before allocating, a clip over
-4 GiB (frames x width x height x 3), naming `durations`, `fps` and the image
-size.
-Stills of different sizes are refused, naming both sizes. The video carries a
-silent mono track (48 kHz) of its own length, so it joins and scores like a
-generated shot - `assemble-and-score` reads the shots' track for its world
-audio and refuses a video with none; its `shots` record where each still
-starts. Each entry of `images` that names a file is confined at
-`validate_workflow` like any media location.
-`validate_workflow` catches a non-positive duration, an `fps` out of range, a
-duration count that does not match a literal image list, and a total over 600
-seconds or 36000 frames (the 4 GiB limit depends on the image size, so it is
-checked at run time).
+Each still gets `max(1, round(duration * fps))` frames. The clip carries a
+silent 48 kHz mono track of its own length (`assemble-and-score` refuses a video
+with no track), and its `shots` record where each still starts.
+
+Refused: stills of different sizes (naming both); a non-positive duration; an
+`fps` out of range; a duration count that does not match a literal image list;
+a total over 600 seconds or 36000 frames; and, at run time before allocating,
+a clip over 4 GiB of uint8 frames (frames x width x height x 3). All but the
+last are caught by `validate_workflow`.
 
 ### concat_videos
 
-Concatenate videos - and the audio generated with them - into one video. The
-standalone counterpart of a chained pipeline step's stitching (see "Chained
-video generation" in the workflow guide):
+Concatenate videos and their audio into one video: the standalone counterpart
+of a chained pipeline step's stitching ("Chained video generation" in the
+workflow guide).
 
 ```json
 {
@@ -607,23 +617,21 @@ video generation" in the workflow guide):
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `videos` | Yes | The videos to join, in order - `previous_result` references, or the path or URL of a video file an earlier run wrote, which is read with the audio muxed into it; an entry may also be a `{"location": ...}` dict wrapping either. Every video must be the same frame size - unlike a sample-rate mismatch, this task does not resize one for you, so a statically-resolvable (`asset:`/`output:`/literal path, wrapped in a `{"location": ...}` dict or not) size disagreement is refused at validate; a `previous_result:` or other reference not yet resolved still fails only at run time (#504, #518). To fit the odd video: `video_frames` to get its frames, `resize_rescale` to the target size (`resize_center_crop` squares the frame first and then stretches it, distorting a non-square target), then `pair_audio(fit="video")` to put its soundtrack back before passing it here (#551) |
+| `videos` | Yes | The videos to join, in order: `previous_result` references, or the path or URL of a video file (read with its muxed audio), each optionally wrapped in `{"location": ...}`. All must be the same frame size; see [join input rules](#join-input-rules) |
 | `trim_frames` | No | Frames dropped from the head of every video after the first (default: 0) |
 | `crossfade_ms` | No | Equal-power crossfade at each audio seam, drawn from the trimmed material - no effect when `trim_frames` is 0, and validation warns when one is written there (default: 75) |
 | `audio_bleed_ms` | No | How long the outgoing video's tail rings on over the head of the next one, at seams with nothing trimmed to crossfade (default: 0, off) |
 | `audio_bleed_gain_db` | No | Gain applied to the bled tail before it is added, in dB - negative ducks a tail that would otherwise push the seam over 0 dBFS (default: 0, unchanged) |
 | `seam_fade_ms` | No | Fade on each side of a seam that gets neither a crossfade nor a bleed - for tonal material, not for a continuous bed (default: 3, just enough not to click) |
-| `fps` | No | Frame rate of the videos - required to join audio when trimming, and the rate the joined file is written at unless `result.fps` overrides it |
+| `fps` | No | Frame rate of the videos - required to join audio when trimming, and the rate the joined file is written at unless `result.fps` overrides it. An input carrying a different rate is re-timed, with an `fps_overrides_input` warning |
 | `match_levels` | No | Even the shots' loudness out before joining - `"rms"` for perceived level (the measurement `get_gallery_metadata` reports as `mean_dbfs`), `"peak"` for the loudest sample. Off by default |
 | `match_levels_dbfs` | No | The level `match_levels` moves every shot to (default: -1 dBFS for `peak`, -20 dBFS for `rms`). A shot that would clip at the target is held at -0.5 dBFS peak instead, reported as a `match_levels_held` warning with a per-shot log event |
+| `sample_rate` | No | The joined soundtrack's rate. Default: the highest among the inputs, the rest resampled up with a warning (#108) |
 
-A video may also be named by path or URL, which is how shots an earlier run
-already wrote are joined without regenerating them - the file is read with the
-audio muxed into it, and its track is fitted to the frames' own duration so the
-codec's block padding does not walk the sound off the picture over a dozen
-seams. A shot generated in memory through a `previous_result:` chain gets the
-same fit, applied where the file is written rather than where it is decoded,
-so per-shot drift does not accumulate across a cut the way it once did:
+Shots an earlier run wrote are joined by path or URL without regenerating them.
+A file's track, and a `previous_result:` shot's, is fitted to its frames'
+duration, so codec padding does not drift the sound off the picture across
+seams:
 
 ```json
 {
@@ -644,23 +652,16 @@ so per-shot drift does not accumulate across a cut the way it once did:
 ```
 
 Give each video its own entry. One `previous_result` reference naming a step
-that produced several videos does not hand them all over at once - it fans the
-step out over them, one concatenation per video, which is what makes the list
-form above the way to join a run's shots.
+that produced several videos fans the step out, one concatenation per video.
 
-`trim_frames` and `audio_bleed_ms` address opposite situations. A *chain* carries
-its keyframe forward, so the trimmed head is material that covers the same stretch
-of time as the outgoing tail and the two can be crossfaded. A *cut* generates each
-shot independently, so there is nothing to fade with - and generated shots tend to
-open on near-silence and end mid-sound, leaving a butt-join that drops a running
-laugh track or a ringing room into a hole. `audio_bleed_ms` fills it the way an
-audience carries across a picture cut: a decaying copy of the outgoing tail is laid
-over the incoming head, added to whatever is already there, shortening neither side.
-Reach for more than the gap looks like it needs: a shot's head is silent for
-longer than the picture suggests, and the bleed has to outlast it. Measured on
-a five-shot H3 sitcom cut, 700 ms still left a 44 dB hole at the worst seam;
-1800 ms brought it to 32 dB and 2500 ms gained almost nothing more, so the
-`dialogue-short` template defaults to 1800 and exposes it as `audio_bleed_ms`:
+**Seams.** `trim_frames` suits a *chain*: the trimmed head covers the same time
+as the outgoing tail, so `crossfade_ms` blends the two. A *cut* has nothing to
+crossfade, and generated shots tend to open on near-silence. `audio_bleed_ms`
+lays a decaying copy of the outgoing tail over the incoming head, shortening
+neither side. A shot's head is silent longer than the picture suggests: on a
+five-shot H3 sitcom cut, 700 ms left a 44 dB hole, 1800 ms 32 dB, and 2500 ms
+little better, so the `dialogue-short` template defaults `audio_bleed_ms` to
+1800:
 
 ```json
 {
@@ -677,63 +678,52 @@ a five-shot H3 sitcom cut, 700 ms still left a 44 dB hole at the worst seam;
 }
 ```
 
-A bleed works because it copies ambience, which has no pitch and no attacks to
-give the copy away. It is the wrong tool for anything tonal - a copied musical
-phrase or half-spoken word reads as a stutter whichever direction it runs.
-`bleed_join` checks the outgoing tail's spectral flatness and warns when it
-looks tonal or speech-like rather than noise-like, so this failure mode
-surfaces in the job's `warnings` list instead of only in the mix. When a
-shot ends on something tonal, either give the cut a continuous bed with
-`slice_audio` + `loop_audio` + `mix_audio` + `pair_audio`, which leaves no seam
-to treat at all, or fade the
-seam gracefully with `seam_fade_ms` (a hundred or so milliseconds) and accept the
-cut. That advice inverts on a continuous bed - a laugh track, room tone - where a
-longer fade only digs the hole deeper (the same sitcom cut measured 54-59 dB
-holes with a 250-500 ms fade and no bleed). `audio_bleed_ms` wins where both are
-set and there is material to bleed. A bleed covers the gap but cannot fill it:
-the silence is inside the incoming shot's own head, and the only complete fix is
-a continuous bed under the whole cut: `slice_audio` a few seconds of tone out of
-a shot, [`loop_audio`](#loop_audio) it to the length of the cut, `mix_audio` it
-under the episode and `pair_audio` it back onto the picture.
-
-Levels are the other thing a cut has to reconcile, and no fade control can
-touch it. Shots generated independently land wherever the model put them - two
-shots of one scene, same template, same cast, measured `peak_dbfs` -2.65 and
--12.56 - and each reads as fine on its own, because a shot is only wrong
-*relative to what it is cut against*. Butt-joined, that is a 10 dB drop at the
-cut, and it is not an artifact *at* the seam that a fade could smooth: it is
-either side of it. `match_levels` scales each track before the join -
-`"rms"` matches perceived level, which is usually what "make these sound the
-same" means, and `"peak"` matches the loudest sample, which is the safer
-choice on material with big transients. A shot whose gain would clip at the
-target is held just below full scale and the log says so. Left off - the
-default, so nothing existing changes - a spread of 6 dB or more across the
-tracks being joined is reported as a warning rather than passing in silence:
-on the job's `warnings` and as a `warning` event in its stream, not only in
-the server's log, since the caller who can act on it is the one who asked for
-the run. The other end of the range warns too: a shot at or below -40 dBFS,
-or one that needs 20 dB or more of gain to reach the target, is noise floor
-rather than a quieter performance, and matching it up is reported as
-`match_levels_near_silent`. `dissolve_videos` takes the same pair.
-
-The joined soundtrack is fitted to the joined frames. A track that comes out
-short of the frame grid - rounding in an input's own track, which otherwise
-compounds join after join - is padded with silence to it. A pad of a frame
-or more is a warning (`joined_audio_padded_to_frames`); less than a frame is
-rounding, and only logged. The file's AAC encode can then trim the track by a
-further handful of samples (typically 16-32, under a millisecond), which is
-logged the same way, or warned as `joined_audio_short_after_mux` if it
-reaches a frame. Either way the recorded shots are re-measured against the
-file as written, so `media.shots` stays accurate. Both apply to
-`dissolve_videos` the same way, and neither task warns about resampling
-inputs that already agree to a `sample_rate` the caller pinned.
+A bleed copies ambience; on anything tonal (music, a half-spoken word) the
+copy reads as a stutter, and `bleed_join` warns when the outgoing tail looks
+tonal rather than noise-like. For a tonal ending, fade the seam with
+`seam_fade_ms` (a hundred or so ms) and accept the cut. On a continuous bed (a
+laugh track, room tone) a longer fade only deepens the hole (54-59 dB with a
+250-500 ms fade and no bleed). `audio_bleed_ms` wins where both are set and
+there is material to bleed. A bleed covers the gap but cannot fill it; the
+complete fix is a continuous bed under the whole cut: `slice_audio` a few
+seconds of tone, [`loop_audio`](#loop_audio) it to the cut's length,
+`mix_audio` it under and `pair_audio` it back onto the picture.
+
+#### Join input rules
+
+These apply to `concat_videos` and `dissolve_videos` (and frame size and rate
+to `join_into_song`).
+
+- **Frame size.** Every video must be the same size; nothing is resized. A size
+  disagreement between `asset:`/`output:`/literal-path entries (wrapped in
+  `{"location": ...}` or not) is refused at validate; one involving a
+  `previous_result:` fails at run time (#504, #518). To fit the odd video:
+  `video_frames`, then `resize_rescale` to the target size (`resize_center_crop`
+  squares then stretches, distorting a non-square target), then
+  `pair_audio(fit="video")` to put its soundtrack back (#551).
+- **Levels.** Independent shots land far apart (two shots of one scene measured
+  `peak_dbfs` -2.65 and -12.56), a jump either side of the seam that no fade can
+  hide. `match_levels` scales each input before the join: `"rms"` for perceived
+  level, `"peak"` for material with big transients. Each input is levelled as
+  one unit; an input that is itself a join keeps its inner steps, and one wider
+  than 3 dB is an `inner_level_step` warning. Left off, a spread of 6 dB or more
+  is a job warning, unless a later step such as `pair_audio` replaces the track.
+  A shot at or below -40 dBFS, or one needing 20 dB or more of gain, is reported
+  as `match_levels_near_silent`.
+- **Sample rate.** Inputs at different rates are resampled up to the highest,
+  with a warning, unless `sample_rate` pins the target.
+- **Joined track.** The soundtrack is fitted to the joined frames. A track short
+  of the frame grid is padded with silence: a frame or more warns
+  `joined_audio_padded_to_frames`, less is only logged. The AAC encode can trim
+  a further 16-32 samples, logged, or warned as `joined_audio_short_after_mux`
+  if it reaches a frame. The shot records are re-measured against the file as
+  written, so `media.shots` stays accurate.
 
 ### dissolve_videos
 
 Join videos with a cross-dissolve at every seam, and fade the whole piece in
-from and out to a colour. Where `concat_videos` cuts - right for shots that
-each carry their own sound - this melts one shot into the next, which is what a
-montage cut to a score wants:
+from and out to a colour: a montage cut to a score, where `concat_videos` cuts.
+The [join input rules](#join-input-rules) apply.
 
 ```json
 {
@@ -753,14 +743,15 @@ montage cut to a score wants:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `videos` | Yes | The videos to join, in order - `previous_result` references, or the path or URL of a video file an earlier run wrote, one entry per video as with `concat_videos`; an entry may also be a `{"location": ...}` dict wrapping either. Every video must be the same frame size - unlike a sample-rate mismatch, this task does not resize one for you, so a statically-resolvable (`asset:`/`output:`/literal path, wrapped in a `{"location": ...}` dict or not) size disagreement is refused at validate; a `previous_result:` or other reference not yet resolved still fails only at run time (#504, #518). To fit the odd video: `video_frames` to get its frames, `resize_rescale` to the target size (`resize_center_crop` squares the frame first and then stretches it, distorting a non-square target), then `pair_audio(fit="video")` to put its soundtrack back before passing it here (#551) |
+| `videos` | Yes | The videos to join, in order, in the shapes `concat_videos` takes. All the same frame size |
 | `dissolve_frames` | No | Frames of overlap at each seam, blended linearly (default: 12). 0 is a hard cut |
 | `fade_in_frames` | No | Frames over which the first video rises out of `fade_color` (default: 0) |
 | `fade_out_frames` | No | Frames over which the last video sinks into it (default: 0) |
 | `fade_color` | No | The RGB colour the fades come from and go to (default: black) |
 | `fps` | No | Frame rate of the videos - required to crossfade audio at a dissolve, and the rate the dissolved file is written at unless `result.fps` overrides it |
 | `match_levels` | No | Even the shots' loudness out before joining - `"rms"` or `"peak"`, as with [`concat_videos`](#concat_videos). Off by default |
-| `match_levels_dbfs` | No | The level `match_levels` moves every shot to (default: -1 dBFS for `peak`, -20 dBFS for `rms`). A shot that would clip at the target is held at -0.5 dBFS peak instead, reported as a `match_levels_held` warning with a per-shot log event |
+| `match_levels_dbfs` | No | As with `concat_videos` |
+| `sample_rate` | No | The joined soundtrack's rate. Default: the highest among the inputs, the rest resampled up with a warning |
 
 Every seam shortens the result by one overlap, so eight 124-frame shots joined
 with 12-frame dissolves run 908 frames, not 992 - size a soundtrack slice to
@@ -773,12 +764,10 @@ when any input is silent the result is, and `pair_audio` puts a score under it.
 ### join_into_song
 
 Join a spoken scene into a musical number: dialogue shots keep their own
-audio, and the shots sung after them play over the *unbroken* song rather
-than the separate slices each was generated against. The song's entry point
-is `dialogue length - cue_seconds`, and a workflow cannot do that arithmetic
-itself - a hand-computed literal goes stale the moment one dialogue shot is
-regenerated at another length. This task measures the joined dialogue at run
-time and places the song from it, so the offset never goes stale:
+audio, and the sung shots after them play over the *unbroken* song rather than
+the slices each was generated against. The song enters at `dialogue length -
+cue_seconds`, measured at run time, so regenerating a dialogue shot at another
+length cannot leave a stale offset:
 
 ```json
 {
@@ -797,51 +786,41 @@ time and places the song from it, so the offset never goes stale:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `dialogue` | Yes | The spoken shots, in order, each keeping its own audio - a non-empty list, the same entries `concat_videos`' `videos` takes: `previous_result` references, or the path or URL of a video file an earlier run wrote, each optionally wrapped in a `{"location": ...}` dict. A shot with no track is filled with silence for its length |
-| `song_shots` | Yes | The sung shots, in order - a non-empty list in the same shapes as `dialogue`. Their own audio is discarded; they play over `song` |
+| `dialogue` | Yes | The spoken shots, in order, each keeping its own audio: a non-empty list in the shapes `concat_videos`' `videos` takes |
+| `song_shots` | Yes | The sung shots, in order, in the same shapes. Their own audio is discarded |
 | `song` | Yes | The unbroken track the song shots were sliced from - an audio result, a video or audio file's path, or anything carrying `.audio` and `.sample_rate`. Its rate is the output's |
-| `cue_seconds` | No | The song time that lands on the first song shot's frame 0 - the start of the slice that shot was generated against. `0` (the default) starts the song exactly at the cut; above `0` the song enters that long before it, under the last spoken line. Longer than the dialogue is refused - the song would have to start before the film. Must be ≥ 0 |
+| `cue_seconds` | No | The song time that lands on the first song shot's frame 0 (the start of the slice it was generated against). `0` (the default) starts the song at the cut; above `0` it enters that long before, under the last spoken line. Must be ≥ 0 and no longer than the dialogue |
 | `dialogue_target_lufs` | No | Integrated loudness (BS.1770) each dialogue shot is gained to, with one static gain per shot. Omitted (the default), the shots keep their own levels. A shot too short (under 400 ms) or too quiet to measure is left at its own level, with a `dialogue_unmatched` warning |
 | `duck_delay_ms` | No | How long after the song enters the dialogue starts to duck (default: `0`). At or past the dialogue's end, nothing ducks. Must be ≥ 0 |
 | `duck_db` | No | How far the dialogue ducks, in dB (default: `-12`). Must be ≤ 0 |
 | `duck_ramp_ms` | No | The length of the linear ramp into the duck (default: `250`). Must be ≥ 0 |
-| `fps` | No | The rate the videos play at, needed only when none of them carries one of its own - a pipeline's frames carry none, a file brings its own. Must be > 0 |
-
-The timeline, in samples at the song's rate: each dialogue shot's track is
-fitted to its own frames (trimmed or padded with silence, warning
-`dialogue_fitted_to_frames` past a frame's worth of difference), so the
-joined dialogue ends exactly where the first song shot's frame 0 is - call
-that sample `D`. The song is placed at `D - cue_seconds * sample_rate`, so
-song time `cue_seconds` lands on that frame, and it runs to the end of the
-picture; a song shorter than that warns `song_short` and is padded with
-silence rather than looped. The dialogue ducks by `duck_db` from
-`duck_delay_ms` after the song enters, over a linear `duck_ramp_ms` ramp. The
-song shots' own audio is discarded, and a dialogue shot with no track of its
-own is filled with silence for its length rather than skipped, so later
-shots do not land early.
-
-Frames are joined one for one, so every video must share one frame size and
-one frame rate: a mismatch is refused rather than resampled, as is a join
-where no video carries a rate and `fps` is not given, or an `fps` that
-contradicts the rate the videos carry. A step that wrote its video with
-`result.fps` hands that rate on, so a shot written at 12 fps from 24 fps frames
-is a 12 fps shot to the join, as it is when read back with `output:`.
-
-There is no final normalization here - what level a deliverable sits at is
-the workflow's to decide, with [`normalize_audio`](#normalize_audio) after
-this step; the templates that mux to video normalize to -3 dBFS peak, as
-with any `pair_audio` mux.
-
-The result is one `video/mp4`: the dialogue's frames then the song shots',
-over the mix, with a shot record per input (`shot@<key>`, named from the
-step's `dialogue` then `song_shots` references, as `for_each` names a
-member), each shot's samples measured off the built waveform rather than
-derived from its frames.
+| `fps` | No | The rate the videos play at, needed only when none carries one (a pipeline's frames carry none, a file brings its own). Must be > 0 |
+
+Each dialogue shot's track is fitted to its own frames (trimmed or padded,
+warning `dialogue_fitted_to_frames` past a frame's difference); a shot with no
+track is filled with silence for its length. The joined dialogue ends at sample
+`D`, the first song shot's frame 0, and the song is placed at
+`D - cue_seconds * sample_rate`, running to the end of the picture; a song
+shorter than that warns `song_short` and is padded with silence. The dialogue
+ducks by `duck_db` from `duck_delay_ms` after the song enters, over a linear
+`duck_ramp_ms` ramp.
+
+Frames are joined one for one: videos of different sizes or rates are refused,
+as is a join where no video carries a rate and `fps` is not given, or an `fps`
+that contradicts the videos' rate. A step that wrote its video with
+`result.fps` hands that rate on.
+
+There is no final normalization; follow with
+[`normalize_audio`](#normalize_audio) (the templates normalize to -3 dBFS peak).
+
+The result is one video: the dialogue's frames then the song shots', over the
+mix, with a shot record per input (`shot@<key>`, named from the references as
+`for_each` names a member), its samples measured off the built waveform.
 
 ### stabilize_video
 
-Remove a generated clip's accumulated framing drift - the slow wander a video
-model adds over a shot that was meant to hold still:
+Remove a generated clip's accumulated framing drift, the slow wander a video
+model adds over a shot meant to hold still:
 
 ```json
 {
@@ -858,22 +837,14 @@ model adds over a shot that was meant to hold still:
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
 | `clip` | Yes | The video - a frame list, a frame array or tensor, an audio+video pair, or the path or URL of a video file, read with its audio, so a shot an earlier run wrote can be steadied without regenerating it |
-| `smooth` | No | `0` (the default) locks the framing to the first frame, which is what a shot generated from a pinned keyframe wants. A window in frames instead removes only the wander faster than that window, so a slow deliberate camera move survives and the drift around it does not |
-
-The argument is `clip`, not `video`, on purpose: the engine loads an argument
-named `video` itself, as bare frames, which would strip the soundtrack off
-before the task ever saw it. Frames are shifted back and the result is cropped
-to the region every frame covers, then resized to the original size; a
-soundtrack passes through untouched.
-
-It is a stabilization pass, not a format pass. `smooth: 0` on a shot with a
-deliberate camera move fights the move - every frame is shifted back toward
-the first, cropped and rescaled - and nothing downstream will notice, since
-the duration, size and sample rate all survive. Run it on a shot that drifts,
-as its own step; do not run it on every shot before a cut, which is what the
-assembly templates once did and what made their output visibly wider than
-the source. The join tasks refuse shots of different sizes, so no
-normalization step is needed before them.
+| `smooth` | No | `0` (the default) or `1` locks the framing to the first frame, for a shot generated from a pinned keyframe. A window of 2 or more frames removes only the wander faster than that window, so a slow deliberate camera move survives |
+
+The argument is `clip`, not `video`, because the engine loads an argument named
+`video` itself as bare frames, dropping the soundtrack. Frames are shifted back,
+cropped to the region every frame covers and resized to the original size; the
+soundtrack passes through. `smooth: 0` on a shot with a deliberate camera move
+fights the move, silently. Run it only on a shot that drifts, not on every shot
+before a cut.
 
 ### trim_video
 
@@ -894,32 +865,25 @@ Keep one span of a video's frames, and its audio over the same span:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `video` | Yes | The clip. A path, `asset:` or `output:` reference is read with its audio; an earlier step's video is used as it is. A URL is fetched as frames only |
+| `video` | Yes | The clip. A path, `asset:` or `output:` reference is read with its audio; an earlier step's video is used as is. A URL is fetched as frames only |
 | `start_frame` | Yes | The first frame kept, from `0` |
 | `num_frames` | Yes | How many frames are kept: `1` or more |
 | `fps` | No | The clip's frame rate. Defaults to the rate the clip carries; needed only to cut its audio, which is refused without one |
 
 Frames `[start_frame, start_frame + num_frames)` are kept. The audio is cut to
-the same span at the track's own sample rate: each end is `frames_to_samples`
-of a frame index, rounded on its own, so consecutive trims tile the track with
-no sample lost or repeated. A clip with audio (or an audio+video pair) comes
-back as an audio+video pair with its frame rate and sample rate kept; a bare
-frame list comes back as frames.
-
-The shots the clip carries are clipped to the span and re-based to start at
-`0`, with their sample side cleared. A clip that carries none (a decoded file,
-a fresh render) comes back as one shot spanning the kept frames, named after
-the file it was read from (else `video 1`), its samples measured off the cut
-track. A span that reaches past the clip's end is refused, naming the clip's
-frame count - it is never shortened or padded - and so is a `num_frames` of
-`0` or less or a negative `start_frame`; `validate_workflow` catches these on
-literal values.
+the same span, each end `frames_to_samples` of a frame index rounded on its
+own, so consecutive trims tile the track exactly. A clip with audio comes back
+as an audio+video pair; a bare frame list as frames. Carried shots are clipped
+to the span and re-based to `0`, their sample side cleared; a clip with none
+comes back as one shot named after its file (else `video 1`). A span past the clip's end is refused, naming the clip's frame count (never
+shortened or padded), as are a `num_frames` of `0` or less and a negative
+`start_frame`; `validate_workflow` catches these on literal values.
 
 ### window_video
 
 Cut one overlapping, fixed-length window out of a long video, so a
-video-to-video model that reads at most one bucket of frames (121 for LTX)
-can work through a longer source a window at a time:
+video-to-video model that reads one bucket of frames (121 for LTX) can work
+through a longer source a window at a time:
 
 ```json
 {
@@ -957,23 +921,18 @@ Every window is exactly `num_frames` frames, float32 in `[0, 1]` like
 `loop_frames`' output. A source of `N` frames takes `ceil(N / stride)`
 windows, indexes `0` to `(N - 1) // stride`.
 
-The window's audio is the source's samples for its real frames, cut on the
-source's own frame boundaries (`frames_to_samples`, #401), so adjacent
-windows' strides tile the track exactly; the repeated frames carry silence of
-the length they would have had. A source with no track gives a silent window.
+The window's audio is the source's samples for its real frames, cut on frame
+boundaries (`frames_to_samples`, #401); the repeated frames carry silence.
 
 Memory (#695): a file source (a path, `asset:` or `output:`) is never decoded
-whole - the window reads only the source frames it covers, from the keyframe
-before them, and the soundtrack on its own, so a window of a long source
-holds about one window of float32 frames plus the decoded track. An earlier
-step's video is already in memory and is cut from there.
-
-One step makes one window: a task cannot return a list of them, so drive it
-with `for_each` over `{name, index}` entries as above. It refuses an
-`overlap` that is not below `num_frames`, and a negative `overlap` or `index`
-- `validate_workflow` catches these on literal values - and, at run time, an
-`index` whose window would start at or past the source's last frame, naming
-the source's frame count and the last valid index.
+whole; the window reads only the frames it covers plus the soundtrack.
+
+One step makes one window, so drive it with `for_each` over `{name, index}`
+entries as above. Refused: an `overlap` not below `num_frames`, a negative
+`overlap` or `index` (caught by `validate_workflow` on literal values), and, at
+run time, an `index` whose stride would start at or past the source's end
+(`index * stride >= source frames`), naming the frame count and the last valid
+index.
 
 ### join_windows
 
@@ -1034,34 +993,25 @@ different size from the source (a 2x upscale); the output is at the windows'
 size.
 
 Memory (#695): the output is built as uint8, with only a seam's `overlap`
-frames blended in float32, so a join holds the output at one byte a channel
-plus the windows it was given - never the whole video in float32. A file
-`source` is read for its frame count and soundtrack only, never its picture.
+frames blended in float32. A local file or `asset:`/`output:` `source` is read
+for its frame count and soundtrack only; a URL source is decoded whole.
 
 The window count must be exactly `ceil(source_frames / (num_frames -
-overlap))` - the rule has one home, `window_count` in `dw/task_domains.py`,
-and `window_video`'s last valid `index` is one below it. A different count is
-refused naming both numbers and the list entries (by index) to add or drop:
-by `validate_workflow`, before any window renders, when the count is
-knowable from the document - `source` an `asset:`/`output:` reference or a
-literal path (probed header-only), `num_frames` and `overlap` literal after
-substitution, `videos` a list (a `gather:` over a `for_each` step is one) -
-and otherwise at run time, once the source is decoded. The validate-time
-check is the task's (`dw/window_count_errors.py`), so any `join_windows`
-step gets it, and it applies the same `window_count_problem` the task does. It also refuses a window whose frame count is not `num_frames`
-(named by position) and windows of different sizes. An unknown `curve` and an
-`overlap` that is not below `num_frames` are refused by `validate_workflow`
-on literal values as well, and so is an `fps` that is neither `"source"`
-nor a number above zero.
-
-The output's audio is the source's own track over exactly
-`frames_to_samples(source_frames)`, and none when the source has none; the
+overlap))` (`window_count` in `dw/task_domains.py`; `window_video`'s last valid
+`index` is one below it). A different count is refused naming both numbers and
+the entries to add or drop: by `validate_workflow` when the count is knowable
+from the document (`source` an `asset:`/`output:` reference or literal path,
+probed header-only; `num_frames` and `overlap` literal; `videos` a list or a
+`gather:` over a `for_each` step), otherwise at run time
+(`dw/window_count_errors.py` applies the task's `window_count_problem`). Also
+refused: a window whose frame count is not `num_frames` (named by position),
+windows of different sizes, an unknown `curve`, an `overlap` not below
+`num_frames`, and an `fps` that is neither `"source"` nor above zero.
+
+The output's audio is the source's own track (none when it has none); the
 windows' audio is discarded. Its shot records are one per window over the
-frames it owns (window `i > 0` starts at its blended head), with
-`overlap_frames` on every seam and cumulative `frames_to_samples` sample spans
-(#401), so `assess_output` reads the seams as dissolves and its sync check
-measures against the source's timeline. Like `concat_videos` and
-`dissolve_videos`, its catalog shape is a cut task.
+frames it owns, with `overlap_frames` on every seam and cumulative sample
+spans (#401), so `assess_output` reads the seams as dissolves.
 
 ### fit_to_model and restore_to_source
 
@@ -1101,13 +1051,12 @@ and exactly `num_frames`, the pipeline runs on the fitted video, and
 
 #### Reframe to another aspect
 
-To turn a 16:9 clip into 9:16, 4:5 or 1:1 there are two ways today:
+To turn a 16:9 clip into 9:16, 4:5 or 1:1:
 
 - **Crop.** `fit_to_model` with `mode: "crop"` and the target `width`/`height`
-  scales each frame to fill and centre-crops it. It works at full resolution
-  with no model, and it loses the sides. `num_frames` is required: set it to
-  the clip's own length to keep every frame. `restore_to_source` is the wrong
-  follow-up for a reframe, because it undoes the fit.
+  scales each frame to fill and centre-crops it, at full resolution with no
+  model, losing the sides. Set `num_frames` to the clip's own length to keep
+  every frame. Do not follow it with `restore_to_source`, which undoes the fit.
 
   ```json
   {
@@ -1135,44 +1084,34 @@ Generative outpaint of video (filling the new sides) is not supported; see #1066
 (resize to fill exactly) or `crop` (scale to fill, centre-crop). The frame
 count is exact: a longer source is cut to its first `num_frames` frames and a
 shorter one holds its last frame. `downscale` (default 1) divides `width` and
-`height`, which must both be divisible by it: a 2x upscaler whose `width` x
-`height` is the output it renders takes its reference at half that, so it fits
-with `downscale: 2` and passes the same `width`/`height` to the pipeline
-(`templates/ltx2/upscale-clip`). The step returns `{video, fit}`, read as
-`previous_result:<step>.video` and `previous_result:<step>.fit`. `video` is a
-float32 array of frames, height, width, RGB in 0-1 that carries its `fps`, so
-it goes straight to a pipeline's `video` or a reference condition's `frames`;
-saved, it is written as an mp4 at that rate. The `fit`
-record holds `mode`, `source_width`, `source_height`, `source_frames`,
+`height`, which must both be divisible by it: a 2x upscaler fits its reference
+with `downscale: 2` and passes the full `width`/`height` to the pipeline
+(`templates/ltx2/upscale-clip`).
+
+The step returns `{video, fit}` (`previous_result:<step>.video` and `.fit`).
+`video` is a float32 `(frames, height, width, 3)` array in 0-1 carrying its
+`fps`, ready for a pipeline's `video` or a reference condition's `frames`. The
+`fit` record holds `mode`, `source_width`, `source_height`, `source_frames`,
 `model_width`, `model_height`, `model_frames`, `content_box` (where the source
-sits in the model frame), `source_box` (the part of the source kept - all
-of it unless `mode` is `crop`), each box `{x, y, w, h}`, and `source_fps`
-(the source's frame rate, `null` when it carries none). A record written
-before `source_fps` existed still restores.
-
-`restore_to_source` takes the pipeline's output and that record; it has no
-`scale` argument. The scale is the output's size over the fitted size and must
-be the same across and down, so a 2x upscaler restores to twice the source.
-Letterbox bars are cropped off, and frames are trimmed to the source's count,
-dropping the held ones (an output shorter than that is returned as it is).
-`crop` cannot bring back the edges it cut, so it returns the kept region at
-the source's pixel density times the scale: 640x480 fitted into 512x288
-restores to 640x360. A saved video keeps its exact size; only an odd side
-grows by one pixel, since the encoder needs even sides.
-
-`fps` sets the restored clip's frame rate; it never adds or drops a frame.
-Omitted, the clip keeps the rate it came in at - the pipeline's, which for a
-model that runs at a fixed rate is not the source's. `"source"` takes the
-record's `source_fps`, falling back to the input's rate when the record has
-none. A positive number sets that rate. Anything else is refused, at
-validation for a literal. A model that runs at 24 fps on an 8 fps clip
-returns one frame per source frame, so `"fps": "source"` writes it back at 8
-and it stays frame-aligned with the source in time as well as in count
-(`templates/ltx2/alpha-matte`, whose `frame_rate` variable defaults to
-`"source"` and passes through to this argument).
-
-Neither task carries a soundtrack. Pair the source's with `pair_audio` after
-the restore.
+sits in the model frame), `source_box` (the part of the source kept, all of it
+unless `mode` is `crop`), each box `{x, y, w, h}`, and `source_fps` (`null`
+when the source carries none).
+
+`restore_to_source` takes the pipeline's output and that record. The scale is
+the output's size over the fitted size and must be the same across and down,
+so a 2x upscaler restores to twice the source. Letterbox bars are cropped off
+and frames are trimmed to the source's count (a shorter output is returned as
+is). `crop` returns the kept region at the source's pixel density times the
+scale: 640x480 fitted into 512x288 restores to 640x360. A saved video grows an
+odd side by one pixel, since the encoder needs even sides.
+
+Its `fps` sets the restored clip's rate without adding or dropping a frame:
+omitted, the input's rate (the pipeline's); `"source"`, the record's
+`source_fps` (else the input's); or a positive number. Anything else is
+refused, at validation for a literal. `templates/ltx2/alpha-matte` passes
+`"source"`, so a 24 fps model run on an 8 fps clip is written back at 8.
+
+Neither task carries a soundtrack; pair the source's back with `pair_audio`.
 
 ### crop_face_track
 
@@ -1208,57 +1147,43 @@ face-detail pass that needs the face large and still:
 | `fps` | No | Refuse the clip unless its frame rate is this, within `0.01` (23.976 passes for `23.98`, not for `24`). Above `0`. No check when unset |
 | `detector_repo` | No | Hub repo holding the YuNet weights. Default `opencv/face_detection_yunet`, read at a pinned revision; any other must be a Hub repo id |
 | `detector_file` | No | The detector file in that repo, a bare `.onnx` name. Default `face_detection_yunet_2023mar.onnx` |
-| `device` | No | Where detection runs |
-
-A validate's `downloads_required` lists the detector file as a file entry when it is not cached, pinned at its revision for the default repo and file.
-
-`modulus`, `remainder` and `multiple` follow `plan_cuts`: the model's grid is
-the workflow's to declare. The defaults (8, 1, 32) are LTX's 8n+1 frames and
-32-pixel steps, so a workflow that names none gets the output it always did;
-the `face-repair` template passes `modulus: 8, remainder: 1, multiple: 32`
-explicitly. A model on another grid names its own: MiniMax H3's is `modulus:
-17, remainder: 5, multiple: 32` (17n+5 frames, 32-pixel steps). A `modulus` below 1, a `remainder` below 0 or not below `modulus`,
-and a `crop_size` that is not a multiple of `multiple` are refused at
-validation.
-
-These defaults are the task's own. The `face-repair` template tunes them on
-lem for LTX-2.5 at `padding` 1.5, `gate_full` 0.03 and `gate_zero` 0.06: a
-medium face (7.5% of the frame's width when tuned) already renders cleanly, and the wider
+
+A validate's `downloads_required` lists the detector file as a file entry when it is not cached, pinned at its revision when `detector_repo` is the default.
+
+`modulus`, `remainder` and `multiple` follow `plan_cuts`: the workflow declares
+the model's grid. The defaults (8, 1, 32) are LTX's 8n+1 frames and 32-pixel
+steps; MiniMax H3's is `modulus: 17, remainder: 5, multiple: 32`. The
+`face-repair` template tunes the gate for LTX-2.5 at `padding` 1.5, `gate_full`
+0.03 and `gate_zero` 0.06: a medium face already renders cleanly, and the wider
 context keeps the refine from re-inventing hair and clothing.
 
 **Example:** [face-repair.json](../workflows/templates/ltx2/face-repair.json) — find, refine and paste back a wide shot's far face.
 
-Detection is OpenCV's YuNet, run on the full frame and on four overlapping
-enlarged tiles, merged by non-maximum suppression, so a face only a few dozen
-pixels wide is still seen. One track is kept and smoothed with an exponential
-moving average; across a short detector miss the last box is held at a decaying
-strength. The track restarts at each shot boundary the clip records, or, for a
-clip that records none, where the picture jumps: a sharp drop in its HSV
-colour histogram, or a spike in its difference from the previous frame (a cut
-between two framings of one picture keeps its colours). A padded square
-around the smoothed box is cut from every frame and resized to `crop_size`, then
-the crops are padded to `modulus * n + remainder` frames (8n+1 by default) with mirrored warm-up and cool-down frames.
-
-Each frame carries a strength from 0 to 1: how much a face-detail pass should
-change it. It is 1 when face width over frame width is at or below `gate_full`,
-0 at or above `gate_zero`, linear between, and multiplied by the hold decay on
-frames the detector missed.
-
-`min_frames`, `max_frames` and `fps` refuse a clip the model the crops feed
-cannot take, before the detector loads: the window is checked against the
-padded crop count, and the message names both the clip's frame count and the
-count it pads to (`the clip's 346 frames pad to 362 crops on the 17n+5 grid,
-above max_frames 345`). A clip that records no frame rate is refused when
-`fps` is set.
-
-It returns `{crops, track}`. `crops` is a video at the clip's frame rate. When
-the clip has a soundtrack, `crops` carries it padded to match: `pad_before`
-frames of silence, the clip's own sound, then `pad_after` frames of silence, so
-the track is exactly `crop_frames / fps` seconds long and a step holding the
-crops' audio (`hold_audio: previous_result:<step>.crops`) lines up with them
-frame for frame. A silent clip gives crops with no audio; a soundtrack on a
-clip with no frame rate to place it by is dropped, with a `crop_audio_dropped`
-warning. `track` is a JSON record saved as its own `.json` file, readable by
+Detection is OpenCV's YuNet on the full frame and on four overlapping enlarged
+tiles, merged by non-maximum suppression, so a face a few dozen pixels wide is
+still seen. One track is kept, smoothed with an exponential moving average;
+across a short miss the last box is held at a decaying strength. The track
+restarts at each shot boundary the clip records, or, with none recorded, where
+the picture jumps (a sharp drop in its HSV colour histogram, or a spike in
+frame difference). A padded square around the smoothed box is cut from every
+frame, resized to `crop_size`, and the crops are padded to
+`modulus * n + remainder` frames with mirrored warm-up and cool-down frames.
+
+Each frame's strength (how much a face-detail pass should change it) is 1 when
+face width over frame width is at or below `gate_full`, 0 at or above
+`gate_zero`, linear between, times the hold decay on missed frames.
+
+`min_frames`, `max_frames` and `fps` refuse a clip the downstream model cannot
+take, before the detector loads, naming the clip's frame count and its padded
+count (`the clip's 346 frames pad to 362 crops on the 17n+5 grid, above
+max_frames 345`). With `fps` set, a clip with no frame rate is refused.
+
+It returns `{crops, track}`. `crops` is a video at the clip's frame rate; a
+soundtrack is padded with `pad_before` and `pad_after` frames of silence to
+exactly `crop_frames / fps` seconds, so `hold_audio:
+previous_result:<step>.crops` lines up frame for frame. A soundtrack on a clip
+with no frame rate is dropped with a `crop_audio_dropped` warning. `track` is a
+JSON record saved as its own `.json` file, read as
 `previous_result:<step>.track`:
 
 | Field | Description |
@@ -1275,11 +1200,13 @@ warning. `track` is a JSON record saved as its own `.json` file, readable by
 
 A clip with no face still succeeds: every strength is 0, the crops are the
 centre square, and a `no_face_found` warning is raised. `validate_workflow`
-refuses a `crop_size` that is not a multiple of 32, a `gate_zero` at or below
-`gate_full`, a `padding` out of range, a `detector_repo` that is not a repo id,
-a `detector_file` that is not a `.onnx` name, a `min_frames`, `max_frames` or
-`fps` at or below 0, and a `max_frames` below `min_frames`, before anything
-downloads.
+refuses, before anything downloads: a `crop_size` that is not a multiple of
+`multiple`; a `modulus` below 1; a `remainder` below 0 or not below `modulus`;
+a `gate_full` or `gate_zero` above 1, or a `gate_zero` at or below `gate_full`;
+a `min_confidence` at or above 1; a `padding` out of range; a `detector_repo`
+that is not a repo id; a `detector_file` that is not a `.onnx` name; a
+`min_frames`, `max_frames` or `fps` at or below 0; and a `max_frames` below
+`min_frames`.
 
 ### paste_face_track
 
@@ -1310,34 +1237,24 @@ using the `track` record `crop_face_track` returned:
 | `fps` | No | The output's frame rate: omitted or `"source"` for the clip's own, or a number above zero for that rate. No frames are added or dropped |
 
 The pad frames are dropped: the crop at `pad_before + i` goes back on source
-frame `i`. Each crop is resized to the square it was cut from, then blended
-over the frame with a radial feathered mask times that frame's strength. The
-mask is the circle inscribed in the square; its outer `feather` fraction fades
-from 1 to 0 along a smoothstep, so the square's corners and everything past
-the circle keep the source. A square that ran past the frame's edge is
-clipped to the frame.
-
-A frame with strength 0 (no face, or a face too large for the gate) is passed
-through exactly. With `color_match`, the crop's mean colour is shifted onto the
-source's inside the mask, so a repair pass that drifted in tone does not show
-as a patch.
-
-It returns the source clip with its audio and shots carried, the same frames
-one for one, at the clip's frame rate unless `fps` sets another. It refuses a track whose frame count or frame size
-does not match the clip, a `repaired` with fewer frames than the track's
-`crop_frames`, and a `track` that is not a `crop_face_track` record.
-`validate_workflow` refuses a `feather` past `1`, and an `fps` that is
-neither `"source"` nor a number above zero, before the job runs.
+frame `i`, resized to its square and blended with a radial feathered mask
+(the inscribed circle, its outer `feather` fraction fading on a smoothstep)
+times that frame's strength. A frame with strength 0 passes through exactly.
+
+It returns the source clip with its audio and shots, frame for frame, at the
+clip's rate unless `fps` sets another. Refused: a track whose frame count or
+size does not match the clip, a `repaired` with fewer frames than the track's
+`crop_frames`, and a `track` that is not a `crop_face_track` record; at
+validation, a `feather` past `1` and an `fps` that is neither `"source"` nor
+above zero.
 
 **Example:** [face-repair.json](../workflows/templates/ltx2/face-repair.json) — the repaired crops pasted back over the source, its soundtrack kept.
 
 ### video_frames
 
 The frames of a generated video, as one `(frames, height, width, channels)`
-uint8 array. That is the shape an argument taking frames rather than a video
-wants - LTX-2's keyframe conditions, which are mapped from 0-255 - and it is one
-artifact where a list of frames would become one artifact per frame and multiply
-the step that consumed it:
+uint8 array: the shape LTX-2's keyframe conditions want (mapped from 0-255), and
+one artifact where a list of frames would fan the consuming step out per frame:
 
 ```json
 {
@@ -1360,12 +1277,46 @@ this array; hand those over with `previous_result:step.frames`.
 
 **Example:** [extend-clip.json](../workflows/templates/ltx2/extend-clip.json)
 
+### loop_frames
+
+Repeat a still, or lap a clip from its first frame, to exactly `num_frames`
+frames, with no crossfade: the video counterpart of `loop_audio`, for a
+conditioning input shorter than its model reads (LTX-2.5's Ingredients IC-LoRA
+reference wants a static video of at least 121 frames).
+
+| Argument | Required | Description |
+| -------- | -------- | ----------- |
+| `video` | Yes | Frames, or a still. A still on disk is passed as `{"media_type": "image", "location": "asset:x.png"}`, since a bare path under `video` is read as a video file; a still from an earlier step as `previous_result:` |
+| `num_frames` | Yes | Frames to return, a whole number of at least 1 |
+
+Returns a `(num_frames, height, width, channels)` float32 array in `[0, 1]`
+with no fps or audio: the shape `LTX2ReferenceCondition.frames` takes, not a
+keyframe condition, which wants uint8 (use `video_frames`). Refuses an empty
+input. No weights.
+
+**Example:** [reference-sheet.json](../workflows/templates/ltx2/reference-sheet.json)
+
+### frame_grid
+
+A contact sheet of a clip: `count` evenly spaced frames, first and last
+included, tiled into one PIL image. No weights.
+
+| Argument | Required | Description |
+| -------- | -------- | ----------- |
+| `video` | Yes | Frames in any shape a result carries |
+| `count` | No | Frames to sample, clamped to the clip's length. Default `12` |
+| `columns` | No | Tiles per row. Default: a wide-biased grid, the last row left-justified |
+| `tile_width` | No | Tile width in pixels; height follows the aspect ratio. Default `320` |
+| `label` | No | Burn each tile's timestamp (or frame index, when the clip carries no fps) into its corner. Default `true` |
+
+`count`, `columns` and `tile_width` must be whole numbers of at least 1, and
+`label` a boolean. An empty clip is refused.
+
 ### pair_audio
 
-Pair a video with an audio track, so the two are saved as one muxed file. A
-pipeline that generates its own soundtrack returns the pair together; anything
-working on the frames alone - a latent upsampler, an interpolator, an upscaler -
-returns frames without it, and this puts it back:
+Pair a video with an audio track, saved as one muxed file: the way to put a
+soundtrack back after a step that works on frames alone (an upsampler, an
+interpolator, an upscaler):
 
 ```json
 {
@@ -1382,51 +1333,43 @@ returns frames without it, and this puts it back:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `video` | Yes | The frames - a frame list, a frame array or tensor, or an audio+video pair whose own soundtrack is replaced; their own rate is carried through to the output, so `result.fps` is only needed to override it. A file brings its rate along, and so does a pipeline step whose pipeline reports one (MiniMax-H3's `videos` carry 24 fps, #1056); frames that carry none are fitted and written at 8 fps unless `fps` says otherwise |
-| `audio` | Yes | The soundtrack - a waveform, the earlier step whose video carried one, or the path or URL of an audio or video file; the last two bring their sample rate along. A mono track is fine: an mp4 audio stream takes stereo and nothing else, so saving duplicates the one channel into two and warns that it did |
-| `sample_rate` | No | Sample rate of the waveform. Required unless `audio` carries one; given here it wins |
-| `fps` | No | The rate the frames play at, only needed when they carry none of their own. What `fit` and the length-mismatch check measure the track against, and the rate the paired video then carries to the writer. `result.fps` is not a substitute: the writer applies it after the fit has run, so declaring it alone on rate-less frames writes a 24 fps picture under a track padded to the 8 fps length (`video_fps_defaulted` says so, #1056) |
-| `fit` | No | `"video"` cuts or pads the track with silence to the length of the frames, warning either way (`audio_padded_to_video` / `audio_trimmed_to_video`). Left unset (the default) the track is used as it is, and a length that disagrees with the frames' is warned about rather than corrected (`audio_video_length_mismatch`). Any other value is refused at run time, not by `validate_workflow` |
-
-`fit`'s guarantee is exact for the waveform handed to the encoder, not for
-the file the encoder writes: muxing is a lossy AAC encode, and it can still
-trim or pad the written track by a further handful of samples (#428
-measured up to ~30, under a millisecond). That residual is logged, not
-warned; on a video with recorded `shots` it becomes a
-`joined_audio_short_after_mux` warning only if it reaches a frame. `get_gallery_metadata`'s
-`media.shots` and `assess_output`'s `sync_length` are measured against the
-written file, not the pre-encode prediction, so they are the number to
-trust for the track's actual length.
-
-When the video carries recorded `shots` (from an earlier `concat_videos`,
-`dissolve_videos` or chain step), `pair_audio` remeasures each one's sample
-fields against the track it was handed. Every shot but the last is
-`round(start_frame / fps * sample_rate)`; the last one runs to the track's
-actual end, and once the file is written it is measured again against what
-the file decodes to. So its `num_samples` can sit a few dozen samples off
-`round(num_frames * sample_rate / fps)`: the encoder's trim, which the job's
-event log records. A real mismatch between the
-track and the video's length is a separate, thresholded warning
-(`audio_video_length_mismatch`, or `audio_padded_to_video` /
-`audio_trimmed_to_video` when `fit: "video"` corrected it), so a last shot
-short by less than a millisecond is expected, not a bug. `get_gallery_metadata`'s `media.shots`
-reports the remeasured fields.
+| `video` | Yes | The frames: a frame list, array or tensor, or an audio+video pair whose soundtrack is replaced. Their own rate is carried through (a file's, or a pipeline's that reports one, #1056); frames that carry none are fitted and written at 8 fps unless `fps` is given |
+| `audio` | Yes | An [audio input](#slice_audio). A mono track is duplicated into two channels on save, with a warning, since an mp4 audio stream takes stereo only |
+| `sample_rate` | No | Rate of a bare waveform; given, it wins |
+| `fps` | No | The rate the frames play at, needed only when they carry none. `fit` and the length check measure against it. `result.fps` is not a substitute: it is applied after the fit, so alone on rate-less frames it writes a 24 fps picture under a track fitted to 8 fps (`video_fps_defaulted`, #1056) |
+| `fit` | No | `"video"` cuts or pads the track with silence to the frames' length, warning `audio_padded_to_video` / `audio_trimmed_to_video`. Unset (the default), the track is used as is and a length mismatch warns `audio_video_length_mismatch`. Any other value is refused at run time |
+
+`fit` is exact for the waveform handed to the encoder. The AAC mux can trim or
+pad the written track by a further few dozen samples (#428), which is logged;
+on a video with recorded `shots` it warns `joined_audio_short_after_mux` only
+if it reaches a frame. `get_gallery_metadata`'s `media.shots` and
+`assess_output`'s `sync_length` measure the written file, so trust them for
+the track's actual length.
+
+When the video carries recorded `shots` (from `concat_videos`,
+`dissolve_videos` or a chain step), `pair_audio` remeasures their sample fields
+against the new track: every shot but the last starts at
+`round(start_frame / fps * sample_rate)`, and the last runs to the written
+track's end, so its `num_samples` can sit a few dozen samples off the frame
+count's. That is the encoder's trim, not a mismatch; a real length mismatch is
+the thresholded warning above.
 
 **Example:** [assemble-and-score.json](../workflows/templates/assemble-and-score.json)
 
 ### slice_audio
 
 Cut a slice out of an audio track, addressed in seconds or in video frames.
-Slices reaching past the end of the track are zero-padded — asking for more
-than the source holds returns a track of the length you asked for whose tail is
-digital silence, not a shorter track and not an error. Anything past a few
-milliseconds of that padding is reported as a `slice_past_end` warning on the
-job, because a score laid under a longer cut goes silent for the rest of the
-film without anything else saying so; to fill a cut longer than the recording,
-build a bed with [`loop_audio`](#loop_audio) first and slice that. Either half
-of a pair may be left out - an omitted start begins at the head of the track, an omitted
-duration runs to the end of it - so a workflow that trims only when it is given
-a length still passes the whole track along:
+Either half of a pair may be left out: an omitted start begins at the head, an
+omitted duration runs to the end. A slice past the end of the track is
+zero-padded to the length asked for, and more than a few milliseconds of
+padding warns `slice_past_end`; to fill a cut longer than the recording, build
+a bed with [`loop_audio`](#loop_audio) and slice that.
+
+**Audio inputs.** In every audio task, `audio` (and each entry of `audios`)
+takes the path or URL of an audio file, or of a video file whose soundtrack is
+taken; a waveform from a previous step; or an earlier step's video generated
+with a soundtrack. Files and videos bring their sample rate; `sample_rate` is
+needed only for a bare waveform, and given, it wins.
 
 ```json
 {
@@ -1445,24 +1388,17 @@ a length still passes the whole track along:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
+| `audio` | Yes | An [audio input](#slice_audio) |
 | `start_seconds` / `duration_seconds` | One pair | The slice in seconds; either may be omitted. `start_seconds` with `num_frames` + `fps` and no duration takes its length from the frames |
 | `start_frame` / `num_frames` / `fps` | One pair | The slice in video frames; `fps` is required, start and count may be omitted |
-| `lead_frames` | No | Frame form only, default 0: extra audio before the cut. The slice starts at `start_frame - lead_frames` and still runs `num_frames`, so `start_frame` 48 with `lead_frames` 12 at 24 fps is audio from frame 36 for `num_frames` frames. A whole number, 0 or above; refused with the seconds form, and refused when `start_frame - lead_frames` is below zero (by `validate_workflow` for literals, at run time otherwise) |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `lead_frames` | No | Frame form only, default 0: extra audio before the cut. The slice starts at `start_frame - lead_frames` and still runs `num_frames`, so `start_frame` 48 with `lead_frames` 12 at 24 fps is audio from frame 36 for `num_frames` frames. A whole number, 0 or above; a nonzero value is refused with the seconds form, and refused when `start_frame - lead_frames` is below zero (by `validate_workflow` for literals, at run time otherwise) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 ### gain_audio
 
-Apply a gain, in decibels, to a region of an audio track - the rest of the
-track passes through unchanged. The region is addressed the same way
-`slice_audio`'s is, in seconds or in video frames, so ducking a scene under
-another (lowering a dialogue track between two timestamps) is one step
-instead of the `slice_audio` → `gain` (a whole-track `normalize_audio` on the
-slice) → `mix_audio` → `rejoin` → `pair_audio` chain that used to be the only
-way to gain part of a track rather than all of it. Unlike `slice_audio`, a
-region reaching past the end of the track is clipped to it rather than
-zero-padded - there is no silence there to gain, only the end of the real
-material:
+Apply a gain, in decibels, to a region of an audio track, addressed as in
+`slice_audio`; the rest passes through. Unlike `slice_audio`, a region past the
+end of the track is clipped to it, not zero-padded:
 
 ```json
 {
@@ -1482,18 +1418,14 @@ material:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
+| `audio` | Yes | An [audio input](#slice_audio) |
 | `gain_db` | Yes | Gain to apply within the region, in decibels - negative ducks it, positive boosts it |
 | `start_seconds` / `duration_seconds` | No | The region in seconds; either may be omitted |
 | `start_frame` / `num_frames` / `fps` | No | The region in video frames; `fps` is required if either is given, start and count may be omitted |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
-No region argument is required: with every one of them omitted, the gain
-applies to the whole track (#395) - the same "no region means everything"
-reading `mix_audio`'s gains use. To gain everything from some point on
-instead, give just `start_seconds: 0` and leave `duration_seconds` unset (or
-`start_frame: 0` + `fps` and leave `num_frames` unset), which runs to the
-end of the track without needing to already know how long that is.
+With no region argument the gain applies to the whole track (#395). A start
+with no duration runs to the end of the track.
 
 ### crossfade_audio
 
@@ -1516,9 +1448,8 @@ tracks by the fade window:
 
 ### fade_audio
 
-Fade a track in from silence and out to it. A slice cut out of the middle of a
-piece ends on whatever was sounding at the cut; a fade turns that into an
-ending. The curve is the equal-power cosine the seam joins use:
+Fade a track in from silence and out to it, on the equal-power cosine the seam
+joins use:
 
 ```json
 {
@@ -1536,19 +1467,17 @@ ending. The curve is the equal-power cosine the seam joins use:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
+| `audio` | Yes | An [audio input](#slice_audio) |
 | `fade_in_ms` | No | Length of the fade in, from the head of the track (default: 0) |
 | `fade_out_ms` | No | Length of the fade out, to the tail of the track (default: 0) |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 **Example:** [audio-trim-fade.json](../workflows/templates/audio-trim-fade.json) — slice a generated track to length, then fade the cut into an ending.
 
 ### normalize_audio
 
-Scale a track so its loudest sample sits at a level. Generated music comes out
-wherever the model happened to land - a quiet take needs lifting before it sits
-under a picture, a hot one needs headroom before the encoder. Only the gain
-changes, so the dynamics survive:
+Scale a track so its loudest sample, or its integrated loudness, sits at a
+level. Without `limit` only the gain changes:
 
 ```json
 {
@@ -1565,19 +1494,19 @@ changes, so the dynamics survive:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
+| `audio` | Yes | An [audio input](#slice_audio) |
 | `peak_dbfs` | No | The level the loudest sample is moved to, in dB below full scale (default: -1.0). 0 is full scale. With `limit`, a true-peak (4x oversampled, BS.1770) ceiling the output never crosses |
 | `target_lufs` | No | An integrated loudness (BS.1770) to scale the track to instead, in LUFS (0 or below). Without `limit` the gain is capped so the peak stays under `peak_dbfs`, and `target_lufs_capped` warns when that cap wins. A track shorter than 400 ms (or silent throughout) cannot be measured, warns `target_lufs_unmeasurable`, and falls back to `peak_dbfs` |
-| `limit` | No | Reach `target_lufs` past a transient instead of letting it cap the gain: a true-peak look-ahead limiter (5 ms look-ahead, 20 ms hold, 150 ms release, linked across channels) holds `peak_dbfs`, and the gain is searched for until the limited track lands within 0.1 LU of `target_lufs` - limiting takes some loudness back, more on dense material. The limiter itself never adds gain, and it stops at 12 dB of reduction - past that the gain stops too, and `target_lufs_capped` warns with `limited: true` and `shortfall_lu` (as it does for any track left more than 0.1 LU short). More than 6 dB warns `limiter_heavy` (pumping can be audible). The step's log carries `constraint: "limiter"`, `gain_db`, `max_gain_reduction_db`, `limited_fraction` (0 when the limiter was not needed), `output_true_peak_dbfs` and `output_lufs` (default: false, which behaves exactly as without it) |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `limit` | No | Reach `target_lufs` past a transient instead of letting it cap the gain: a true-peak look-ahead limiter (5 ms look-ahead, 20 ms hold, 150 ms release, linked across channels) holds `peak_dbfs`, and the gain is searched for until the limited track lands within 0.1 LU of `target_lufs` - limiting takes some loudness back, more on dense material. The limiter itself never adds gain, and it stops at 12 dB of reduction - past that the gain stops too, and `target_lufs_capped` warns with `limited: true` and `shortfall_lu` (as it does for any track left more than 0.1 LU short). More than 6 dB warns `limiter_heavy` (pumping can be audible). The step's log carries `constraint: "limiter"`, `gain_db`, `max_gain_reduction_db`, `limited_fraction` (0 when the limiter was not needed), `output_true_peak_dbfs` and `output_lufs` On an input that carries shots, `limiter_uneven_across_shots` names a shot the limiter works much harder on than the rest (default: false) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 A silent track is returned unchanged.
 
-To level dialogue with a laugh or a shout on it, `limit` is the switch: a
-peak-only gain lets that one transient set the level of every line around it.
+To level dialogue with a laugh or a shout on it, use `limit`, so one transient
+does not set the level of every line around it:
 
 ```json
-"arguments": {"audio": "previous_result:dialogue", "target_lufs": -16, "peak_dbfs": -1.0, "limit": true}
+{"arguments": {"audio": "previous_result:dialogue", "target_lufs": -16, "peak_dbfs": -1.0, "limit": true}}
 ```
 
 **Example:** [dissolve-between-shots.json](../workflows/templates/dissolve-between-shots.json)
@@ -1587,29 +1516,18 @@ soundtrack checks the written level against two thresholds, reported in
 `get_job`'s warnings and readable back afterward as `media.peak_dbfs` from
 `get_gallery_metadata`:
 
-- `audio_no_headroom` fires when a plain audio file's waveform, before
-  encoding, peaks at or above -0.5 dBFS - encoding can push a level that
-  already has no headroom over full scale.
-- `audio_clipped` fires when the file is decoded back *after* writing and
-  measures at or above 0.0 dBFS - the ground truth of what a consumer's
-  decoder will actually see, since an encoder's own overshoot varies by
-  codec and is not reliably predictable from the pre-encode level.
-
-A video mux only ever reports the second one: its pre-encode prediction is
-held rather than emitted, because a mux's overshoot is not reliably positive
-the way a plain audio encode's is. That leaves a real gap between the two
-thresholds - a video whose soundtrack decodes back between -0.5 and 0.0 dBFS
-produces no warning at all, because it predicted risk but measured clean.
-That is the file's own measured level, not a threshold bug: read
-`media.peak_dbfs` against -0.5 and 0.0 to judge a specific file rather than
-relying on the warning alone.
+- `audio_no_headroom`: a plain audio file's waveform peaks at or above
+  -0.5 dBFS before encoding.
+- `audio_clipped`: the written file, decoded back, measures at or above
+  0.0 dBFS.
+
+A video mux reports only `audio_clipped`, so a soundtrack decoding back between
+-0.5 and 0.0 dBFS warns nothing; read `media.peak_dbfs` to judge one.
 
 ### mix_audio
 
-Layer tracks on top of one another. `crossfade_audio` puts tracks one after
-another; this puts them on top of each other - a score laid under a film's own
-sound, where the music runs unbroken while the world underneath it is replaced
-at every cut:
+Layer tracks on top of one another (`crossfade_audio` puts them one after
+another), such as a score under a film's own sound:
 
 ```json
 {
@@ -1626,25 +1544,20 @@ at every cut:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audios` | Yes | The tracks to layer - waveforms, audio or video file paths, or videos generated with a soundtrack |
+| `audios` | Yes | The tracks to layer, each an [audio input](#slice_audio) |
 | `gains` | No | One plain multiplier per track, in the same order - not decibels. Defaults to unity on every track |
 | `sample_rate` | With a raw waveform | Sample rate of the waveforms. Required unless every track brings its own; given here it wins |
 
-Tracks of different lengths are padded with silence to the longest, so a score
-shorter than the picture leaves the tail dry rather than cutting the picture
-down to fit. Summing can push peaks past full scale and the sum is *not*
-rescaled - follow it with `normalize_audio` to bring the peak back down.
+Tracks of different lengths are padded with silence to the longest. The sum is
+not rescaled; follow with `normalize_audio` if it peaks past full scale.
 
 **Example:** [dissolve-between-shots.json](../workflows/templates/dissolve-between-shots.json) — a
 generated score mixed under the shots' own audio.
 
 ### loop_audio
 
-Make a bed of a given length out of a short recording — the room tone laid
-under a whole cut, which is the only complete fix for the hole at a seam. Each
-shot in a cut carries its own room and nothing runs underneath the join;
-a continuous bed does, the way a location's room tone is laid under a dialogue
-scene so the edits stop being audible:
+Make a bed of a given length out of a short recording: room tone laid under a
+whole cut, the complete fix for the hole at a seam:
 
 ```json
 {
@@ -1668,19 +1581,17 @@ scene so the edits stop being audible:
 | `crossfade_ms` | No | Crossfade at each loop point, clamped to the material available (default 250) |
 | `sample_rate` | With a waveform | Sample rate of a waveform passed directly; given for a file or a video it overrides the rate they carry |
 
-Laps are joined with an equal-power crossfade rather than butted together, so
-the loop point itself is not a click. That only smooths the seam: a transient
-in the source (a hit, a swell) still recurs once per lap at full strength, so
-the loop still reads as a level pulse at the lap rate — measured at 9.3 dB on
-a source with one such transient. Pick a source with even internal level to
-avoid the pulse; the crossfade does not remove it. The source is used whole
-every lap and only the last one is trimmed, so the bed lands exactly on the
-requested length; a source longer than the request is trimmed to it.
+Laps are joined with an equal-power crossfade, so the loop point does not
+click, but a transient in the source recurs once per lap as a level pulse (9.3
+dB measured on one source); pick a source with even level, or find one with
+[`find_loop_bed`](#find_loop_bed). Only the last lap is trimmed, so the bed
+lands exactly on the requested length.
 
 The bed is laid under the cut with `mix_audio` and attached to the picture with
 `pair_audio`:
 
 ```json
+[
 { "name": "bed",   "task": { "command": "loop_audio",
                              "arguments": { "audio": "previous_result:room_tone",
                                             "target_frames": 620, "fps": 24 } } },
@@ -1692,26 +1603,20 @@ The bed is laid under the cut with `mix_audio` and attached to the picture with
                              "arguments": { "video": "previous_result:episode",
                                             "audio": "previous_result:mixed" } },
   "result": { "content_type": "video/mp4", "fps": 24 } }
+]
 ```
 
-The bed's source: [`find_loop_bed`](#find_loop_bed) picks the stretch. Run it
-against the cut (or a stem of it) over the range that should hold room tone,
-copy the top candidate's `start_seconds`/`duration_seconds` into `slice_audio`,
-`loop_audio` the slice to the cut's length, and `mix_audio` it in at the
-candidate's `gain` — the room the shots were generated in, picked by
-measurement rather than by ear.
+To pick the source, run [`find_loop_bed`](#find_loop_bed) over the range that
+should hold room tone, `slice_audio` its top candidate's
+`start_seconds`/`duration_seconds`, loop that, and mix it in at the candidate's
+`gain`.
 
 ### find_loop_bed
 
 Pick the window of a recording worth looping into the room-tone bed above,
-measured as it will sound looped rather than as it sits in the source. A
-level check alone misses three things, each found on a real episode:
-
-- near-programme material — faint speech attenuated ~30 dB reads as quiet,
-  and is audible once it repeats every lap.
-- lap-rate modulation — the loop's repeat rate beats against the source's own
-  level movement, invisible in one pass through the source.
-- ticks — a 1 ms transient is invisible to 50 ms RMS and recurs once per lap.
+measured as it will sound looped. A level check alone misses faint speech
+(audible once it repeats every lap), lap-rate modulation, and 1 ms ticks that
+50 ms RMS cannot see.
 
 ```json
 {
@@ -1756,19 +1661,14 @@ one it fails, so `rejected` is a tally of the whole grid:
   the loop's seam.
 - `tonal` — the flatness/harmonicity test `bleed_join` uses, taken over every
   0.1 s and every 0.2 s block inside the window (one per 50 ms step; no
-  longer than `min_seconds`). A window is tonal when any block in it is. Faint
-  speech comes and goes, and over a whole window the pauses dilute a
-  syllable below the threshold; in the block it sits in, it is not diluted.
-  Two lengths, because 0.1 s sits inside one syllable and 0.2 s holds enough
-  periods of a low hum. Flatness is measured over the band the source
-  actually occupies: a source resampled up (a 16 kHz bed mixed at 24 kHz)
-  has an empty band above its own Nyquist that reads as tonal whatever the
-  material is, as `bleed_join`'s tail does (#198). A candidate's `flatness`
+  longer than `min_seconds`). A window is tonal when any block in it is, so a
+  faint syllable is not diluted by the pauses around it. Flatness is measured
+  over the band the source occupies, so an upsampled source's empty top band
+  does not read as tonal (#198). A candidate's `flatness`
   and `harmonicity` are the readings of its blocks closest to failing
   (lowest flatness, highest harmonicity).
 
-On a cut, a bed must come from inside one shot: a window across a cut
-loops the seam's change of room as a once-per-lap step. Shots resolve in the
+On a cut, a bed must come from inside one shot. Shots resolve in the
 probes' order - the `shots` argument (`shots_source: "argument"`), else the
 shots a video from an earlier step carries (`"artifact"`), else the ones the
 run manifest beside the file records (`"manifest"`, which is why
@@ -1783,9 +1683,8 @@ cut and the audio's can sit a few samples apart. A shot placed by frames on
 a source with no frame rate is refused; pass `fps`.
 
 The survivors are thinned so no two overlap, steadiest source first, to a
-pool of up to 200 (`LOOPED_POOL`). The pool does not depend on
-`max_candidates`, which only cuts the final ranking, so asking for one
-candidate returns the default run's first. Each one in the pool is then
+pool of 200 (`LOOPED_POOL`), or `max_candidates` when that is larger. Each one
+in the pool is then
 looped with `loop_audio`'s own crossfade to `loop_seconds` and measured:
 `ripple_db` (the 5-95% spread of the looped 50 ms bins), `envelope_peak_db`/
 `envelope_peak_hz` (the strongest level wobble) and `lap_component_db` (the
@@ -1827,29 +1726,14 @@ first; one whose `envelope_peak_db` is above -15 dB carries the
 ```
 
 `start_seconds`/`duration_seconds` are `slice_audio`'s arguments and `gain`
-is `mix_audio`'s multiplier for reaching `target_bed_dbfs` — the answer picks
-a window and builds nothing, so the remedy is a copy. Finding nothing is an
-answer, not an error: `candidates` is `[]`, `rejected` counts the windows
-each rule threw out, and one `no_loop_bed` finding says which rule to relax.
-
-Like the assessment probes it answers JSON and decides nothing, but it
-searches rather than checking a finished cut, so it is not one of them — it
-stays out of `list_tasks`' `assessment` list. The result must be saved as
-`application/json`.
-
-```json
-{
-    "variables": { "audio": "output:episode/latest/final/cut.mp4" },
-    "steps": [
-        { "name": "bed", "task": { "command": "find_loop_bed",
-                                    "arguments": { "audio": "variable:audio" } },
-          "result": { "content_type": "application/json" } }
-    ]
-}
-```
+is `mix_audio`'s multiplier for reaching `target_bed_dbfs`; the task builds
+nothing. Finding nothing is not an error: `candidates` is `[]`, `rejected`
+counts the windows each rule threw out, and one `no_loop_bed` finding says
+which rule to relax.
 
-run against the finished cut once it exists, then read back over MCP with
-`get_output_text` (JSON results are text) rather than `get_output_audio`.
+It is not an assessment probe (it is not in `list_tasks`' `assessment` list).
+The result must be saved as `application/json`; read it over MCP with
+`get_output_text`.
 
 Out-of-range literals (`min_seconds`/`max_seconds`/`loop_seconds` at or below
 zero, `max_candidates` at or below zero, a negative `crossfade_ms`, and the
@@ -1859,9 +1743,8 @@ source shorter than `min_seconds` fail at run time — they depend on the file.
 
 ### resample_audio
 
-Convert a track to a different sample rate. A pipeline that conditions on audio
-wants it at its own rate (MiniMax H3 at its audio VAE's), and resampling a
-supplied recording once, up front, feeds it what it already wants:
+Convert a track to a different sample rate, such as the rate a pipeline that
+conditions on audio wants (MiniMax H3's audio VAE):
 
 ```json
 {
@@ -1877,26 +1760,21 @@ supplied recording once, up front, feeds it what it already wants:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio or video file, a video generated with a soundtrack (which brings its sample rate along), or a waveform |
-| `target_sample_rate` | Yes | The rate to convert to |
-| `sample_rate` | With a waveform | Sample rate of a waveform passed directly; given for a file or a video it overrides the rate they carry |
+| `audio` | Yes | An [audio input](#slice_audio) |
+| `target_sample_rate` | Yes | The rate to convert to, a whole number above zero |
+| `sample_rate` | With a waveform | Rate of a bare waveform; given for a file or video, it overrides the rate they carry |
 
 A track already at the target rate is returned untouched. The conversion is
-PyAV's, which dw already needs for video - no torchaudio dependency.
-
-Every audio task returns the waveform *and* the rate it is at, so one chains
-into the next without the rate being restated: a `resample_audio` fed
-`previous_result:` from a `slice_audio` takes the source rate from the slice. A
-`sample_rate` given on the step still wins, and one declared on the step's
-`result` still decides what is written to disk.
+PyAV's. Every audio task returns the waveform with its rate, so tasks chain
+through `previous_result:` without restating it; a `result.sample_rate`
+decides what is written to disk.
 
 **Example:** [assemble-and-score.json](../workflows/templates/assemble-and-score.json)
 
 ### compress_audio
 
-Shape a track's dynamics with an envelope-follower - a compressor, a limiter
-and a gate are the same algorithm with different knob settings, so one task
-covers all three through `mode`:
+Shape a track's dynamics with an envelope follower: a compressor, limiter or
+gate, chosen by `mode`:
 
 ```json
 {
@@ -1916,13 +1794,13 @@ covers all three through `mode`:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
+| `audio` | Yes | An [audio input](#slice_audio) |
 | `threshold_dbfs` | Yes | The level the envelope is measured against, in dB below full scale. Cannot be above 0 |
 | `ratio` | No | How hard the reduction is above the threshold, in `compress`/`gate` mode (default: 4.0). Ignored in `limit` mode, which always holds the signal at the threshold |
 | `attack_ms` | No | How fast the envelope rises to a louder signal (default: 10.0). 0 means instantly |
 | `release_ms` | No | How fast the envelope falls back after a louder signal ends (default: 100.0). 0 means instantly |
 | `mode` | No | `compress` (turn down what's above the threshold), `limit` (hold the signal at the threshold), or `gate` (turn down what's below the threshold) (default: `compress`) |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 A silent track is returned unchanged.
 
@@ -1933,8 +1811,7 @@ loudness target, use `normalize_audio` with `limit: true`.
 
 ### filter_audio
 
-Run a track through a single biquad filter stage - trimming the frequencies a
-mix doesn't need, or carving out room for another element:
+Run a track through one biquad filter stage:
 
 ```json
 {
@@ -1952,19 +1829,18 @@ mix doesn't need, or carving out room for another element:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
+| `audio` | Yes | An [audio input](#slice_audio) |
 | `cutoff_hz` | Yes | The filter's corner frequency. Must be below the Nyquist frequency (half the sample rate) |
 | `kind` | No | `lowpass`, `highpass`, `bandpass`, or `notch` (default: `lowpass`) |
 | `q` | No | The filter's resonance/bandwidth (default: 0.707, a Butterworth response) |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 A silent track is returned unchanged.
 
 ### analyze_audio
 
-Measure a track without changing it - peak and RMS level, crest factor, and a
-rough low/mid/high spectral balance, the numbers a `compress_audio` or
-`filter_audio` step downstream is tuned against rather than guessed at:
+Measure a track's peak and RMS level, crest factor and a rough low/mid/high
+spectral balance, to tune a `compress_audio` or `filter_audio` step against:
 
 ```json
 {
@@ -1980,14 +1856,14 @@ rough low/mid/high spectral balance, the numbers a `compress_audio` or
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack (which brings its sample rate along) |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `audio` | Yes | An [audio input](#slice_audio) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 Returns a dict, not a track: `peak_dbfs`, `rms_dbfs`, `crest_factor_db`,
 `low_dbfs` (20-250 Hz), `mid_dbfs` (250-4000 Hz), `high_dbfs` (4000-20000 Hz).
-The three bands are each a share of the track's total power on the same
-scale as `rms_dbfs` (their powers sum to it), so the loudest band sits near
-`rms_dbfs` rather than tens of dB under it - comparable to `compress_audio`'s
+Each band is its share of the track's power on the same scale as `rms_dbfs`
+(their powers sum to the part of it between 20 Hz and 20 kHz), so the loudest
+band sits near `rms_dbfs` and compares with `compress_audio`'s
 `threshold_dbfs`. A silent track, or a band with no content at the track's
 sample rate, reads as `null` rather than `-inf`.
 
@@ -2012,21 +1888,20 @@ Measure how far a shot's audio sits from the source slice it should match. `anal
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | The shot's audio: a path, `asset:`/`output:` reference of an audio or video file, a waveform, or an earlier step's generated audio or video |
+| `audio` | Yes | The shot's audio, an [audio input](#slice_audio) |
 | `reference` | Yes | The slice of the source it should match, in the same forms. A shot with no slice has nothing to measure and is refused |
 | `max_lag_seconds` | No | The largest lag searched, either way (default `2`). A lag at this edge may be larger still, and warns |
-| `threshold_seconds` | No | A lag longer than this, either way, is an `audio_out_of_sync` finding (default `0.1`) |
+| `threshold_seconds` | No | A lag longer than this, either way, is an `audio_out_of_sync` finding (default `0.1`), unless the correlation is under `min_confidence` |
 | `min_confidence` | No | A correlation under this is a `sync_low_confidence` finding and the lag is not to be trusted (default `0.3`) |
-| `sample_rate` | With a waveform | Rate of a directly passed waveform (files carry their own) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 Returns `{lag_seconds, confidence, max_lag_seconds, audio_seconds, reference_seconds, findings, warnings}`. **`lag_seconds` is positive when the audio is late** - its events fall that many seconds after the reference's - and negative when early. `confidence` is the normalised correlation at that lag, 0 to 1. A silent or flat audio or reference gives `lag_seconds: null` and a `sync_unmeasurable` finding. Each finding is `{rule, severity, at, value, threshold, says}`. It is not part of `assess_output`.
 
 ### analyze_beats
 
-Find where a song's beats fall, to cut picture to them - a Music 3 song
-carries no tempo map, and a cut that lands on the beat needs one. An onset
-envelope gives the tempo and the beats are tracked through it; the result is
-JSON and nothing is built:
+Find where a song's beats fall, to cut picture to them (a Music 3 song carries
+no tempo map). The beats are tracked through an onset envelope; the result is
+JSON:
 
 ```json
 {
@@ -2044,8 +1919,8 @@ JSON and nothing is built:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path, `asset:`/`output:` reference of an audio or video file, a waveform from a previous step, or an earlier step's generated audio or video |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `audio` | Yes | An [audio input](#slice_audio) |
+| `sample_rate` | With a waveform | Rate of a bare waveform, a whole number |
 | `tempo_bpm` | No | The tempo, when known. With exactly one anchor it lays an exact grid and nothing is detected; otherwise it steers detection toward that tempo |
 | `anchors` | No | Where beats are known to fall, ascending: a list of times in seconds, or of `{beat_index, seconds}` marks placing detected beat `beat_index` (from 0) at `seconds`. One kind per list |
 | `min_bpm` / `max_bpm` | No | The tempo range searched (default `60` / `200`); `min_bpm` must be below `max_bpm` |
@@ -2069,39 +1944,33 @@ A bare-seconds anchor snaps to the nearest detected beat, so it corrects a
 drift of under half a beat; where the detection is further off than that, use
 `{beat_index, seconds}` marks, which name the beat instead of guessing it.
 
-A track with no clear, regular onsets - a pad, a swell, a noise bed at any
-level - falls back to the peaks of its loudness (`method` `rms_peaks`, with a
-warning that no reliable beat was found and the peaks follow swells rather
-than a pulse). A level that drifts (a fade, a loud first second) is not a
-pulse. A pulse that tracks but is faint - its onsets repeat weakly at the
-period and its beats sit low against the rest of the envelope, as in a song
-with a long quiet build - is kept, with a warning to check a few beats against
-the song and pass `anchors` or `tempo_bpm` if they are off. The tempo is the
-track's strongest period, folded into the range: a half-time song whose pulse
-is 50 BPM reports 100, not a riff that happens to repeat at 95. A silent track returns empty `beats` and a warning. So does a
-near-silent one whose loudest 50 ms is under -40 dBFS - room tone, hiss, or a
-song mixed far too low - since the log-compressed onset envelope would find a
-pulse in it. Raise a real song's level first (`gain_audio`).
-
-`bpm` is always within `min_bpm`-`max_bpm`, or `null`. A pulse found outside the
-range is folded by octaves into it (85.7 BPM searched at 140-200 reports 171.4,
-beating every half pulse). Where no octave fits, the beats keep to a tempo in
-the range and a warning names the pulse. Where the `rms_peaks` peaks have no
-octave in the range, `bpm` is `null` with a warning. The beats run to the
-song's ends: a hit at 0 s is a beat, and a noise bed starting at 0 s is not.
-
-Refused: `min_bpm` at or above `max_bpm`, and malformed anchors (mixed kinds,
-out of order, a non-whole `beat_index`), at `validate_workflow`; an anchor past
-the song's end, or a `beat_index` past the detected count, at run start.
-It returns JSON, so `result` may only be `application/json`.
+A track with no clear, regular onsets (a pad, a swell, a noise bed) falls back
+to the peaks of its loudness (`method` `rms_peaks`), with a warning. A faint
+pulse that still tracks is kept, with a warning to check a few beats and pass
+`anchors` or `tempo_bpm` if they are off. The tempo is the track's strongest
+period, folded into the range (a half-time pulse of 50 BPM reports 100). A
+silent track, or one whose loudest 50 ms is under -40 dBFS, returns empty
+`beats` and a warning; raise a real song's level first (`gain_audio`).
+
+Detected without anchors, `bpm` is within `min_bpm`-`max_bpm`, or `null`: a
+pulse outside the range is folded by octaves into it (85.7 BPM searched at
+140-200 reports 171.4); where no octave fits, the beats keep to a tempo in the
+range and a warning names the pulse; `rms_peaks` peaks with no octave in range
+give `null`. With anchors, `bpm` is the warped beats' median tempo, and with
+`method: "grid"` it is `tempo_bpm` as given. The beats run to the song's ends.
+
+Refused at `validate_workflow`: `min_bpm` at or above `max_bpm`, and malformed
+anchors (mixed kinds, out of order, a non-whole `beat_index`). Refused at run
+time: an anchor past the song's end; a `beat_index` past the detected count,
+once detection has run; and, for a grid, an anchor that puts beat 0 before
+0 s. The result must be `application/json`.
 
 ### analyze_phrases
 
-Find when a song's sung phrases start and stop, from voiced energy alone - no
-Whisper, no model download, seconds of runtime. Where Whisper cannot hear the
-words (a non-English verse, a hallucinated transcript, wordless vocals) this
-still says when the lines are sung. Feed it the vocal stem where there is one;
-the result is JSON and nothing is built:
+Find when a song's sung phrases start and stop, from voiced energy alone (no
+model, no download). It works where Whisper cannot hear the words: a
+non-English verse, a hallucinated transcript, wordless vocals. Feed it the
+vocal stem where there is one; the result is JSON:
 
 ```json
 {
@@ -2118,12 +1987,12 @@ the result is JSON and nothing is built:
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | The song or its vocal stem: a path, `asset:`/`output:` reference of an audio or video file, a waveform, or an earlier step's generated audio (`previous_result:<separate_stems step>.vocals`) |
+| `audio` | Yes | The song or its vocal stem, an [audio input](#slice_audio) (`previous_result:<separate_stems step>.vocals`) |
 | `min_gap_seconds` | No | A silence shorter than this between voiced runs is bridged into one phrase (default `0.35`). Raise it to merge phrases split by breaths, lower it to split lines sung close together. At least 0 |
 | `min_phrase_seconds` | No | A phrase shorter than this, after bridging, is dropped as a click or breath (default `0.3`). Above 0 |
-| `floor_db` | No | The level, in dBFS of a 20 ms frame, a frame must reach to count as voiced. Unset, it is the track's 95th-percentile frame less 35 dB, never below -60 dBFS (the floor `attribute_voices` uses). It is the knob for a fused legato or reverberant verse: when the voice never drops below the relative floor between lines, raising it (less negative, e.g. `-30`) splits them where `min_gap_seconds` can't - then check the phrases by ear before trusting a one-to-one pairing: a raised floor can make the counts match on wrong boundaries. At or below 0 |
+| `floor_db` | No | The level, in dBFS of a 20 ms frame, a frame must reach to count as voiced. Unset, it is the track's 95th-percentile frame less 35 dB, never below -60 dBFS (the floor `attribute_voices` uses). For a fused legato or reverberant verse, raising it (e.g. `-30`) splits lines where `min_gap_seconds` cannot; check the result by ear, since a raised floor can match the counts on wrong boundaries. At or below 0 |
 | `centre` | No | `true` (the default) weights a stereo file to its centre, where a lead vocal is mixed; `false` uses the band energy of the downmix. A real boolean - `"false"` is refused |
-| `sample_rate` | With a waveform | Rate of a directly passed waveform (files carry their own) |
+| `sample_rate` | With a waveform | Rate of a bare waveform, a whole number |
 
 Returns `{phrases: [{start, end}], duration_seconds, method, warnings}`, in
 seconds rounded to 0.01; the phrases ascend and do not overlap.
@@ -2142,25 +2011,27 @@ A frame is voiced at or above the floor; gaps shorter than `min_gap_seconds`
 are bridged, then runs shorter than `min_phrase_seconds` are dropped.
 
 Warnings: no phrases found (a silent track, or one whose loudest frame is
-under -50 dBFS in the voice band - room tone, hiss - unless `floor_db` is
-given); and one phrase covering more than 90% of the track. The task can't tell
-a stem from a mix, so it names both remedies: on a mix whose centred bass and
-kick fill the voice band, feed it `separate_stems`' `vocals` instead; on a
-vocal stem already (legato or reverberant singing), raise `floor_db` and check
-the phrases by ear. `warnings` repeats what was emitted.
-
-Refused at `validate_workflow`: a missing `audio`, a negative
+under -50 dBFS in the voice band, unless `floor_db` is given); and one phrase
+covering more than 90% of the track, whose remedy is `separate_stems`'
+`vocals` on a mix, or a raised `floor_db` on a stem. `warnings` repeats what
+was emitted.
+
+Refused at validation and at run time: a missing `audio`, a negative
 `min_gap_seconds`, a `min_phrase_seconds` at or below 0, a `floor_db` above 0,
-and a `centre` that is not `true` or `false`; the run refuses the same.
-It returns JSON, so `result` may only be `application/json`.
+and a `centre` that is not `true` or `false`. The result must be
+`application/json`.
 
 ### plan_cuts
 
 Plan a music video's cuts from a song's lyrics and beats. `transcribe_audio`
 (with timestamps) says when each line is sung and `analyze_beats` says where
-the beats fall; this turns the two into shots - each a start frame and a
-length - that tile the song exactly, so one prompt per shot can be written and
-the list rendered. A transcript chunk `transcribe_audio` marked `suspect` `no_words`, `repetition` or `silent` is left out of the lines (no word, a decoding loop, or no voice under it - not a sung line); one marked `long` is still used. Each run of consecutive chunks with the same mark adds one warning naming its span, its chunk count and its text (clipped), so a hallucinated intro or fade is one line, not one per word. Lines squeezed to no time back to back join the next scene with one warning per run, the first three named. The result is JSON and nothing is built:
+the beats fall; this turns the two into shots, each a start frame and a
+length, that tile the song exactly. A transcript chunk `transcribe_audio`
+marked `suspect` `no_words`, `repetition`, `stock_phrase` or `silent` is left
+out of the lines; one marked `long` is still used. Each run of consecutive
+chunks with the same mark adds one warning naming its span, chunk count and
+(clipped) text. Lines squeezed to no time join the next scene, with one warning
+per run naming the first three. The result is JSON:
 
 ```json
 {
@@ -2186,12 +2057,12 @@ the list rendered. A transcript chunk `transcribe_audio` marked `suspect` `no_wo
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `transcript` | Yes | `transcribe_audio`'s result with `timestamps` set (`"segment"` or `"word"`): a `{text, chunks}` dict of `{start, end, text}` chunks in seconds. A null `end` runs to the next chunk's start, or the song's end. Its `result` must be `application/json`. Or an [`analyze_phrases`](#analyze_phrases) result (recognised by its `phrases` key), for a song Whisper can't time: each phrase is one line, and suspect filtering and word alignment don't apply. Without `lyrics` a phrase is a line with no lyric (shot `lyric: null`, kind vocal), and `segment_by` `line`, `stanza` (phrases a silence of at least `min_gap_seconds` apart are grouped) and `beat` all work. With `lyrics` of as many lines as phrases they map one to one, in order; a warning names any line whose phrase is under 1/4 or over 4x the median phrase length, since its boundaries may be wrong (the pairing stands - check it by ear). With a different count a warning names both counts and the knob: raise `analyze_phrases`' `min_gap_seconds` when there are more phrases than lyric lines (it bridges more pauses), lower it when fewer, or, for fused legato or reverberant lines, raise its `floor_db` (e.g. `-30`) and check the phrases by ear before trusting the pairing; the lines are then spread evenly from the first phrase's start to the last phrase's end, never paired silently. A long phrase split into pieces gives every piece its whole lyric |
+| `transcript` | Yes | `transcribe_audio`'s result with `timestamps` set: a `{text, chunks}` dict of `{start, end, text}` chunks in seconds (a null `end` runs to the next chunk's start, or the song's end). Or an [`analyze_phrases`](#analyze_phrases) result (recognised by its `phrases` key), each phrase one line, with no suspect filtering or word alignment; see [phrase transcripts](#plan_cuts) below |
 | `lyrics` | No | The song's own lyrics, one line per line with a blank line or a lone section tag (`[Chorus]`) between stanzas, or a list of lines. The shots carry these lines verbatim and in order; the transcript only times them (an `analyze_phrases` transcript: one line per phrase, see `transcript`). `null` or `""` means none: the transcript's text is used (a phrase result has none: `lyric` is `null`) |
 | `beats` | For `segment_by: "beat"` | `analyze_beats`' result, or a list of beat times in seconds |
 | `segment_by` | No | `line` (default), `stanza` or `beat` |
 | `fps` | No | Frames per second the shots are counted in (default `24`) |
-| `duration_s` | No | The song's length; `analyze_beats`' `duration_seconds` when `beats` is that whole dict, else a phrase result's `duration_seconds`, else the transcript's last end (with a warning). `"beats": "previous_result:beats"` passes only the result's `beats` list - the engine takes the key an argument is named for - so name the length too: `"duration_s": "previous_result:beats.duration_seconds"` |
+| `duration_s` | No | The song's length. Default: `analyze_beats`' `duration_seconds` when `beats` is that whole dict, else a phrase result's `duration_seconds`, else the transcript's last end (with a warning). `"beats": "previous_result:beats"` passes only the result's `beats` list (the engine takes the key an argument is named for), so pass `"duration_s": "previous_result:beats.duration_seconds"` too |
 | `min_scene_s` | No | The shortest a shot may be (default `1.0`) |
 | `max_scene_s` | No | The longest a shot may be (default none) |
 | `vocal_tail_s` | No | Seconds a sung shot holds past its last word (default `0`) |
@@ -2210,12 +2081,22 @@ sum to `round(duration_s * fps)`), `lead_frames` the run-up before the cut,
 and `num_frames` the length to render: `max(min_frames, lead_frames +
 cut_frames)` raised to the grid, so the render starts at `start_frame -
 lead_frames` and its cut is frames `[lead_frames, lead_frames + cut_frames)`.
-With no grid arguments `num_frames` is `cut_frames` and `lead_frames` `0`.
+With no grid arguments and no `lead_s`, `num_frames` is `cut_frames` and `lead_frames` is `0`.
 `render_frames` is the sum of the `num_frames` - what the render costs, which
 is more than `total_frames` when shots lead or round up. `lyric` the lines sung in it joined by newlines or `null`, and `kind`
-`"vocal"` or `"instrumental"`. A dict rather than a list, since a list result
-would become one artifact per shot. Its `result` may only be
-`application/json`.
+`"vocal"` or `"instrumental"`. It is a dict, not a list, so it stays one
+artifact. Its `result` may only be `application/json`.
+
+**Phrase transcripts.** Without `lyrics`, a phrase is a line with no lyric
+(`lyric: null`, kind vocal), and all three `segment_by` values work (`stanza`
+groups phrases a `min_gap_seconds` silence apart). With `lyrics` of as many
+lines as phrases they map one to one, and a warning names any line whose phrase
+is under 1/4 or over 4x the median phrase length. With a different count, a
+warning names both counts and the knob (raise `analyze_phrases`'
+`min_gap_seconds` for more phrases than lines, lower it for fewer, or raise its
+`floor_db` for fused lines), and the lines are spread evenly from the first
+phrase's start to the last phrase's end. A long phrase split into pieces gives
+every piece its whole lyric.
 
 How the plan is made:
 
@@ -2253,40 +2134,41 @@ otherwise: a bare-string `transcript` (the message names `timestamps` /
 `return_timestamps`), `segment_by` not one of the three, `segment_by: "beat"`
 without `beats`, `min_scene_s` above `max_scene_s`, and `fps`, `duration_s`,
 `max_scene_s` at or below zero or `min_scene_s`, `vocal_tail_s`,
-`min_gap_seconds` below zero, and a `remainder` outside `0` to below `modulus` (or one with no
-`modulus`) or a `max_frames` below the smallest grid length at or above
-`min_frames`. Also refused at run start: a song whose length
-can't be known (no `duration_s`, no `beats` result, and no transcript to end
-on) and `lyrics` with no sung lines.
+`min_gap_seconds` or `lead_s` below zero, `modulus`, `min_frames` or
+`max_frames` at or below zero, `min_frames` above `max_frames`, a `remainder`
+outside `0` to below `modulus` (or one with no `modulus`), and a `max_frames`
+below the smallest grid length at or above `min_frames`. Also refused at run
+time: a song whose length can't be known (no `duration_s`, no `beats` result,
+and no transcript to end on), `lyrics` with no sung lines, and a `lead_s` whose
+frames leave no room under `max_frames` for a shot's own frames.
 [music-video-cuts.json](../workflows/templates/minimax/music-video-cuts.json)
-chains `separate_stems`, `transcribe_audio` (of the vocal stem, so an instrumental intro is not folded into the first word), `analyze_beats` (of the full mix) and `plan_cuts`, passing H3's
-grid (`17`, `5`, `124`, `345`) and a `lead_s` variable (default `0.5`, 12
-frames at 24 fps). When Whisper can't time the words (a non-English verse, a hallucinated transcript, wordless vocals), swap the `transcribe` step for an `analyze_phrases` step on `previous_result:stems.vocals`, keep `plan_cuts`' `transcript` on that step and pass `lyrics`, one line per sung phrase; the default chain is unchanged. Its shots are what
+chains `separate_stems`, `transcribe_audio` (of the vocal stem), `analyze_beats`
+(of the full mix) and `plan_cuts`, passing H3's grid (`17`, `5`, `124`, `345`)
+and a `lead_s` variable (default `0.5`). When Whisper can't time the words,
+swap the `transcribe` step for `analyze_phrases` on
+`previous_result:stems.vocals` and pass `lyrics`, one line per sung phrase. Its
+shots are what
 [music-video.json](../workflows/templates/minimax/music-video.json) renders:
 the agent writes a prompt per shot, drops `lyric` and `kind`, and passes the
 list as `shots`.
 
 ## Assessment Probes
 
-Three read-only commands measure a finished cut and say where to look -
-`analyze_shots`, `analyze_seams`, `analyze_sync_drift`. Each is registered
-with `assessment=True` (`register_command`, `dw/tasks/task.py`), which is
-what makes a command a probe - not merely answering JSON, since
-[`attribute_voices`](#voice-attribution) and [`check_script`](#script-check)
-answer JSON too and are not probes.
-Each takes a video
-(a stored file - `asset:`, `output:` or a path, read straight from disk
-rather than decoded first - or the video an earlier step returned; not a
-URL, whose download is a bare frame list with no soundtrack) and answers one JSON
-document: every measurement it took, plus `findings` (the measurements that
-crossed a rule in the table below), `rules_applied` (the rule names the probe
-checked) and `shots_source` (where the shot list came from). A probe reads
-the file streaming - a 64x36 grey thumbnail per frame and the soundtrack,
-never a full frame list - so it runs on a cut of any length.
-
-Findings are places to look, not verdicts: nothing in the engine acts on
-one, no run fails for one, and a finding someone has looked at and accepted
-is simply left alone.
+Three read-only commands measure a finished cut and say where to look:
+`analyze_shots`, `analyze_seams`, `analyze_sync_drift`. A probe is a command
+registered with `assessment=True` (`register_command`, `dw/tasks/registry.py`;
+the probes register in `dw/tasks/audio_handlers.py`); other JSON-answering
+tasks such as [`attribute_voices`](#voice-attribution) and
+[`check_script`](#script-check) are not probes. Each takes a video (a stored
+file - `asset:`, `output:` or a path - or the video an earlier step returned;
+not a URL, whose download has no soundtrack) and answers one JSON document:
+every measurement, plus `findings` (measurements that crossed a rule in the
+table below), `rules_applied`, `rules_skipped` (rules that could not run, with
+a reason) and `shots_source`. A probe streams the file (a 64x36 grey thumbnail
+per frame plus the soundtrack), so it runs on a cut of any length.
+
+Findings are places to look, not verdicts: nothing in the engine acts on one
+and no run fails for one.
 
 A probe's `result` must save as JSON:
 
@@ -2302,14 +2184,15 @@ A probe's `result` must save as JSON:
 }
 ```
 
-Any other `content_type` (or none) fails validation - a JSON document can
-only be saved whole under `application/json`; every other content type
-would explode it key by key or die trying to write a number.
+Any other `content_type` (or none) fails validation.
 
 Shot boundaries come, in order: the step's own `shots` argument, the shots
 carried by the video an earlier step returned, the run manifest beside the
 file, and otherwise the whole file is treated as one shot. `shots_source`
-reports which - `argument`, `artifact`, `manifest`, or `none`.
+reports which: `argument`, `artifact`, `manifest`, or `none`. With `none`, the
+shot-against-shot rules (`shot_level_spread`, and all of `analyze_seams`') go
+to `rules_skipped` with `reason: "no shot boundaries"`, and the run warns
+`no_shot_boundaries`.
 
 ### analyze_shots
 
@@ -2340,8 +2223,9 @@ Every seam between shots, audio and picture:
 | `seams[].between` | `[previous shot name, next shot name]` |
 | `seams[].seconds` | Where the seam sits in the file |
 | `seams[].kind` | `cut` or `dissolve` (a dissolve has `overlap_frames`) |
-| `seams[].hard_cut` | Whether the incoming shot is marked `hard_cut: true` - `concat_videos` sets it on a shot opening a seam it butt-joined (a plain cut, or a cut with a `seam_fade_ms` fade). A seam that got an `audio_bleed_ms` bleed carries `audio_bleed_ms` instead of `hard_cut` |
-| `seams[].audio_bleed_ms` | The bleed the seam actually got (clamped to the material); absent where none ran. The picture is still a cut, so `seam_frame_jump` stays quiet |
+| `seams[].hard_cut` | Whether the incoming shot is marked `hard_cut: true`; `concat_videos` sets it on a shot opening a seam it butt-joined (a plain cut, or a cut with a `seam_fade_ms` fade). False on a bled seam |
+| `seams[].audio_bleed_ms` | The bleed the seam got (clamped to the material); absent where none ran. The picture is still a cut, so `seam_frame_jump` stays quiet |
+| `seams[].seam_fade_ms` / `crossfade_ms` | Copied from the incoming shot record when present; such a seam skips `seam_hole` |
 | `seams[].before_shot_rms_dbfs` / `after_shot_rms_dbfs` | RMS level of the whole shot either side of the seam |
 | `seams[].level_step_db` | The absolute difference between those two shot levels. Shot against shot, not the audio at the seam's edges: a take's own tail and head can sit 20 dB apart, which is not a step the cut made |
 | `seams[].before_rms_dbfs` / `after_rms_dbfs` | RMS level of the 0.25 s either side of the seam - what `seam_hole`'s both-sides-voiced guard reads |
@@ -2367,7 +2251,9 @@ when the file has no picture.
 ### analyze_sync_drift
 
 How far the soundtrack sits from the picture, shot by shot and over the
-whole file:
+whole file. Only shots whose records carry `start_sample`/`num_samples` are
+measured; with none, `shots` is empty, `max_offset_ms` is null, and only
+`sync_length` can fire:
 
 | Field | Meaning |
 | ----- | ------- |
@@ -2380,20 +2266,23 @@ whole file:
 
 ### analyze_face_drift
 
-Whether a recurring character still looks like their cast portrait. It
-answers the shape of the probes above but is not one: it measures faces, not
-the soundtrack, and its two rules live in the command rather than in the
-table below. Needs the SFace and YuNet weights from `opencv/face_recognition_sface`
-and `opencv/face_detection_yunet`, which `validate_workflow` lists under
-`downloads_required`.
+Whether a recurring character still looks like their cast portrait. It is not
+a probe: it measures faces, and its two rules live in the command rather than
+in the table below. It returns `measurements`, `findings`, `rules_applied`,
+`shots_source` and `notes` (no `rules_skipped`). Needs the SFace and YuNet
+weights from `opencv/face_recognition_sface` and `opencv/face_detection_yunet`,
+which `validate_workflow` lists under `downloads_required`.
 
 ```json
 {
-    "command": "analyze_face_drift",
-    "arguments": {
-        "video": "output:<identity>/latest/final/cut.mp4",
-        "reference": "asset:cast/ada.png"
-    }
+    "task": {
+        "command": "analyze_face_drift",
+        "arguments": {
+            "video": "output:<identity>/latest/final/cut.mp4",
+            "reference": "asset:cast/ada.png"
+        }
+    },
+    "result": { "content_type": "application/json" }
 }
 ```
 
@@ -2401,7 +2290,7 @@ and `opencv/face_detection_yunet`, which `validate_workflow` lists under
 | -------- | ------- |
 | `video` | A stored video, or the video an earlier step returned |
 | `reference` | The face to hold. With several faces the largest is used and `notes` says so; with none the step is refused |
-| `shots` | Shot records, as the probes take them; else the video's own, else the run manifest, else the whole file is one shot |
+| `shots` | Shot records, as the probes take them; else the video's own, else the run manifest, else the whole file is one shot (and `notes` says so) |
 | `threshold` | Median cosine similarity below which a shot raises `face_drift`, from -1 to 1. Default 0.363, OpenCV's published SFace threshold |
 | `samples_per_shot` | Frames read per shot, evenly spaced, 1 to 64. Default 8 |
 
@@ -2433,41 +2322,40 @@ its threshold, and the severity of a crossing:
 | `sync_drift` | `analyze_sync_drift` | `end_offset_ms` | > 40.0 ms (magnitude) | warn |
 | `sync_length` | `analyze_sync_drift` | `length_delta_ms` | > 40.0 ms (magnitude) | warn |
 
-Three rules carry a guard beyond the threshold: `seam_hole` only fires while
-both sides of the seam are voiced above -30 dBFS (a quiet join between two
-quiet shots is not a hole, it's a pause the shots themselves hold);
-`seam_frame_jump` is skipped at a seam whose incoming shot is marked
-`hard_cut: true` - a cut meant as a cut; and `shot_dead_air` is skipped
-inside a shot whose own rms is at or below -30 dBFS - a shot that is quiet
-throughout, on purpose, rather than one holding a gap. A gap the guard lets
-through is what `slice_audio` -> `loop_audio` -> `mix_audio` is for: cut a
-room-tone bed from the take, loop it to the gap's length, and mix it under
-the line rather than leaving the drop silent.
+Guards beyond the threshold:
+
+- `seam_hole` fires only while both sides of the seam are voiced above -30 dBFS
+  (a quiet join between quiet shots is a pause, not a hole), and is skipped at
+  a seam carrying `seam_fade_ms` or `crossfade_ms` (listed in `rules_skipped`
+  with those seams).
+- `seam_frame_jump` is skipped at a seam whose incoming shot is `hard_cut: true`
+  or that got an `audio_bleed_ms` bleed.
+- `shot_dead_air` is skipped inside a shot whose own RMS is at or below
+  -30 dBFS, and for a shot record marked `silent_by_design: true` (#1162). The
+  remedy for a gap it reports is a room-tone bed: `slice_audio` ->
+  `loop_audio` -> `mix_audio`.
 
 A `shots` record reaching past the file's own length is a separate finding,
-`shot_span_overrun`, on all three probes - not a threshold crossing, since
-the engine clips the record to the file before any of the rules above run.
+`shot_span_overrun` (severity `warning`), on all three probes; the record is
+clipped to the file before the rules above run.
 `validate_workflow` reports the same mistake ahead of the run when the
 video's length is already knowable (a `shots` argument against an
 `asset:`/literal video); a `previous_result:`/`output:` video not yet
 written is left to the finding.
 
 `list_tasks` names the probes in their own `assessment` list, alongside
-`commands`, so a caller looking for a way to check a cut can find them
-without reading every command's schema. The list is exactly the commands
-declared `assessment=True`; `attribute_voices` stays in `commands` only,
-since it analyzes a song rather than checking a cut.
+`commands`: exactly the commands declared `assessment=True`.
 
 ## Stem separation
 
-Split a mix into its htdemucs stems - `vocals`, `drums`, `bass` and `other` -
-each returned as its own audio result. It is the separator `attribute_voices`
-runs (one shared loader and cache), exposed for its own sake: transcribing the
-`vocals` stem aligns sung lyrics better than transcribing the full mix, and
-`drums` + `bass` + `other` is the instrumental to put under dialogue as a
-score bed.
+Split a mix into its htdemucs stems, `vocals`, `drums`, `bass` and `other`,
+each its own audio result. It is the separator `attribute_voices` runs (one
+shared loader and cache). Transcribing the `vocals` stem aligns sung lyrics
+better than the full mix, and `drums` + `bass` + `other` is an instrumental
+bed.
 
 ```json
+[
 {
     "name": "stems",
     "task": {
@@ -2484,32 +2372,32 @@ score bed.
     },
     "result": { "content_type": "application/json" }
 }
+]
 ```
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a waveform from a previous step, or an earlier step's video generated with a soundtrack |
-| `sample_rate` | With a waveform | Sample rate of a directly passed waveform (files carry their own) |
+| `audio` | Yes | An [audio input](#slice_audio) |
+| `sample_rate` | With a waveform | Rate of a bare waveform |
 
 The result saves one file per stem, `<file_base_name>-vocals`, `-drums`, `-bass`
 and `-other`, and a later step reads one as `previous_result:<step>.vocals`.
-Stems are 44.1 kHz stereo, whatever the source was, and sum back to the mix.
-The model is fixed (`htdemucs`, run in fp32), its weights download on first
-use, and it needs the `demucs` package. On MPS a failed separation falls back
-to the CPU and warns (`separation_cpu_fallback`).
-
-**Dialogue and the rest.** The same split takes a dialogue shot apart from a
-score mixed under it: `vocals` is the dialogue and `drums` + `bass` + `other`,
-summed with `mix_audio`, is the rest. Measured on H3 shots under a Music 3
-score, the `vocals` stem matched the dry shot within 0.1 dB rms, and the
-score's leak into it sat about 30 dB under the bed
-([record](proposals/audits/2026-11-dialogue-stems.md)). The rest is not a
-clean score, though: htdemucs puts a shot's own SFX and foley in `drums` and
-`other`, so they come out with the music. Keep the score file when the goal is
-to replace the music. Transcribing the `vocals` stem also gives the line where
-transcribing the dry shot can loop on a hallucination; a speechless shot gives
-a near-silent `vocals` stem and an invented transcript, so judge "no dialogue"
-by the stem's level. Single-speaker dialogue doesn't raise `stacked_vocals`.
+Stems are 44.1 kHz stereo, whatever the source was, and sum back to the mix
+(approximately). The model is fixed (`htdemucs`, fp32) and needs the `demucs`
+package; its weights download on first use through demucs' own downloader,
+outside the Hugging Face cache, so `downloads_required` does not list them. On
+MPS a failed separation falls back to the CPU and warns
+(`separation_cpu_fallback`).
+
+**Dialogue and the rest.** On a dialogue shot with a score under it, `vocals`
+is the dialogue and `drums` + `bass` + `other`, summed with `mix_audio`, the
+rest. On H3 shots under a Music 3 score the `vocals` stem matched the dry shot
+within 0.1 dB RMS, with the score's leak about 30 dB under
+([record](proposals/audits/2026-11-dialogue-stems.md)). The rest is not a clean
+score: a shot's SFX and foley land in `drums` and `other`, so keep the score
+file when the goal is to replace the music. A speechless shot gives a
+near-silent `vocals` stem and an invented transcript, so judge "no dialogue"
+by the stem's level.
 
 **Stacked vocals.** A chorus is often recorded as layered stereo doubles - every
 Music 3 chorus is - and htdemucs returns all of them in the one `vocals` stem.
@@ -2523,10 +2411,9 @@ separate_stems: the vocals stem holds stacked (layered stereo) voices over
 processing them as one
 ```
 
-Read it before working on a span as one voice: pitch or formant work on a
-stacked span distorts it, and an `attribute_voices` window or a lip-sync shot
-over one has two singers in it. Pick a span outside the flagged ones, or plan
-the shot as a two-shot. The stems themselves are unchanged. The check is a 1 s
+Pitch or formant work on a stacked span distorts it, and an `attribute_voices`
+window or a lip-sync shot over one has two singers in it: pick a span outside
+the flagged ones, or plan a two-shot. The stems are unchanged. The check is a 1 s
 window test on the stereo stem: the window must be sung (half its frames voiced,
 and within 14 dB of the stem's level), with an L/R correlation under 0.45 and a
 side/mid energy ratio over 0.4. Windows merge across a one-window gap, and
@@ -2536,21 +2423,18 @@ word never flag it. The thresholds are the `STACK_*` constants in
 
 ## Voice attribution
 
-A validate's `downloads_required` lists `speechbrain/spkrec-ecapa-voxceleb` only. The demucs separator (and `separate_stems`) downloads outside the Hugging Face cache and is not listed.
-
-Which reference voice sings each line of a song, by timbre - staging
-lip-sync shots for a generated song needs its section-to-singer map, and a
-generation model (MiniMax Music3 included) does not hand one back. Pitch
-cannot stand in for it: a tenor and a mezzo share a range, and a pitch
-heuristic has called a tenor female.
+Which reference voice sings each line of a song, by timbre (pitch cannot
+tell a tenor from a mezzo): the section-to-singer map that staging lip-sync
+shots needs and a generation model does not return.
 
-`attribute_voices` separates the vocal stem (htdemucs), reduces every line
-and every voice's reference to its voiced frames, embeds each with
-speechbrain's ECAPA speaker encoder (`spkrec-ecapa-voxceleb`), scores every
-line against every voice by cosine, and rolls lines up into named windows
-(shots, say) by the voiced seconds they overlap. It decides nothing: the
-argmax is always reported, alongside the margin and how much of the line was
-voiced, so a weak answer is visible as weak rather than silently accepted.
+`attribute_voices` separates the vocal stem (htdemucs), reduces every line and
+every voice's reference to its voiced frames, embeds each with speechbrain's
+ECAPA speaker encoder, scores every line against every voice by cosine, and
+rolls lines up into named windows by the voiced seconds they overlap. It
+decides nothing: the argmax is always reported with its margin and voiced
+time, so a weak answer shows as weak. A validate's `downloads_required` lists
+`speechbrain/spkrec-ecapa-voxceleb` only; htdemucs downloads outside the
+Hugging Face cache.
 
 ```json
 {
@@ -2575,14 +2459,13 @@ voiced, so a weak answer is visible as weak rather than silently accepted.
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | The song - a path, `asset:`/`output:` reference, or an earlier step's audio or video |
-| `voices` | Yes | Each voice's name mapped to its reference: a list of `{start_seconds, duration_seconds}` (or `{start, end}`) spans into `audio`, or a path/`asset:` of a separate clip. At least 2 voices; names follow the variable-name pattern; each voice's reference must total at least `min_reference_seconds` |
+| `audio` | Yes | The song, an [audio input](#slice_audio) |
+| `voices` | Yes | Each voice's name mapped to its reference: a list of `{start_seconds, duration_seconds}` (or `{start, end}`) spans into `audio` (a single span may be given bare), or a separate clip as a path, `asset:` or `previous_result:` audio. At least 2 voices; names follow the variable-name pattern; each voice's reference must total at least `min_reference_seconds` |
 | `lines` | No | Spans to attribute, `{start, end, text?}` (a `transcribe_audio` transcript, or its `chunks` list, drops in directly) or `{start_seconds, duration_seconds, text?}`. A zero-length line (`start` = `end`, as Whisper's word timestamps sometimes give) comes back with `voice: null` rather than refusing the call. Omitted: the song is cut into fixed windows of `window_seconds` |
 | `windows` | No | Named spans to roll lines up into, `{name, start, end}`. Omitted: mirrors the fixed windows when `lines` is also omitted, otherwise none |
 | `window_seconds` | No | Length of the fixed windows used without `lines` (default `2.0`) |
 | `min_reference_seconds` | No | Least total reference length per voice; a shorter one is refused by name (default `3.0`) |
 | `separate` | No | Isolate the vocal stem with htdemucs before embedding (default `true`); `false` for audio that is already a dry vocal |
-| `device` | No | Where the models run |
 
 A literal `voices` is checked at validation as well as on the step: fewer
 than two voices, a bad name, a malformed span, or a span-list reference under
@@ -2599,11 +2482,8 @@ cosine between the voices' references), `voiced_floor_dbfs` (the floor this
 song's stem was read against), `warnings` and the `thresholds` compared
 against.
 
-The voiced floor is relative to the stem rather than a fixed level: a quiet
-sung verse under a loud chorus sits 20 dB or more beneath it, and a fixed
--40 dBFS floor dropped such a verse as unvoiced and refused it as a
-reference. Separation leaves near-silence between phrases, so the floor sits
-well above that and well below the singing.
+The voiced floor is relative to the stem, since a quiet sung verse can sit
+20 dB or more under a loud chorus.
 
 | Threshold | Value | What crossing it does |
 | --------- | ----- | ---------------------- |
@@ -2617,28 +2497,22 @@ well above that and well below the singing.
 | `mixed_line_share` | 0.25 | A line whose confidently attributed pieces give a second voice at least this share of their voiced time is `uncertain`, and its `reason` names each voice's seconds - one embedding of two singers can name either of them with a wide margin |
 | `voices_too_similar` | 0.8 | Two references scoring above this against each other make every line between them a weak answer whatever its scores say |
 
-A `voices_too_similar` pair is reported in `reference_similarity`, added to
-`warnings`, and emitted onto the job's warnings (`voices_too_similar`) -
-picking better-separated reference spans is the fix, not reading past it.
+A `voices_too_similar` pair is reported in `reference_similarity` and as a job
+warning; pick better-separated reference spans.
 
-Both models are fixed - there is no model-name argument - and both run in
-fp32 (their STFT front ends are fp32-only in practice, and both are small
-enough that it costs nothing). Weights download on first use - htdemucs
-through demucs' own downloader (from `dl.fbaipublicfiles.com` into the torch
-hub cache, outside the Hugging Face cache), ECAPA from speechbrain - and are
-cached between calls like any other model. On MPS a `separate: true`
-run that fails in htdemucs falls back to the CPU and warns
-(`separation_cpu_fallback`) rather than failing the step.
+Both models are fixed (no model-name argument) and run in fp32. Weights
+download on first use (htdemucs from `dl.fbaipublicfiles.com` into the torch
+hub cache, ECAPA from speechbrain) and are cached between calls. On MPS a
+failed htdemucs pass falls back to the CPU and warns
+(`separation_cpu_fallback`).
 
 **Example:** [attribute-lines.json](../workflows/templates/attribute-lines.json) — Which singer sings each line of a song, and when: `transcribe_audio` with timestamps, its transcript handed whole to `attribute_voices` as `lines`.
 
 ### Checking the lip-sync target
 
 A sung multi-shot piece can put the right song on the wrong mouth: the shot
-lip-syncs whoever the prompt or the reference made most prominent, not the
-singer the song gives that line to. Listening does not catch it, and one
-frame per shot does not either. This check lines each sung line up with the
-picture and looks at whose mouth is open on it.
+lip-syncs whoever the prompt or reference made most prominent. This check lines
+each sung line up with the picture and looks at whose mouth is open on it.
 
 1. Run `attribute-lines` on the song with each singer's reference spans as
    `voices`. Every line comes back with numeric `start` and `end` seconds into
@@ -2656,133 +2530,98 @@ picture and looks at whose mouth is open on it.
 6. Skip the lines that are `uncertain` or have a null `voice`, and name them
    in the report - a skipped line is unchecked, not passed.
 
-**Song time to cut time.** `attribute-lines` answers in seconds into the song
-it was given; the frames are read from the cut. The offset between the two
-depends on how the piece was assembled:
+**Song time to cut time.** `attribute-lines` answers in seconds into the song;
+the frames are read from the cut. The offset depends on the assembly:
 
-- `minimax/music-video`: song time is cut time unless `trim_frames > 0`. The
-  template slices the song `lead_frames` before each shot's `start_frame`,
-  renders `num_frames`, keeps the shot's `cut_frames` with `trim_video`, joins
-  the trims with `concat_videos` at `trim_frames: 0`, and lays the whole song back with
-  `pair_audio` at `fit: video`, so no offset applies. A trim drops frames at
-  every seam, and the song no longer lines up past the first one.
+- `minimax/music-video`: the template re-slices the song at each entry's
+  `start_frame` for `cut_frames`, joins those pieces with `crossfade_audio` and
+  lays them under the edit with `pair_audio` at `fit: video`. Song time is cut
+  time only when the entries start at frame 0, leave no gaps and are in song
+  order; otherwise map each line through its entry's `start_frame`.
 - `join_into_song`: add `start_frame / fps − cue_seconds` to every line time,
   where `start_frame` is the first sung shot's, from the step's shots output.
   That shot's frame 0 is where song time `cue_seconds` lands.
-- Any other assembly: transcribe the final cut's own track instead, so the
-  times are already the cut's. Attribution is weaker there - dialogue ducked
-  under the song is in the mix too.
+- Any other assembly: transcribe the final cut's own track instead (weaker
+  attribution, since ducked dialogue is in the mix too).
 
 **Where it misleads:**
 
-- A duet line - two singers on one line, or call and response inside one
-  Whisper segment - comes back `uncertain`: its 2 s pieces name more than one
-  voice, and the `reason` says how many seconds each holds.
-  `timestamps: "word"` splits it into words, each attributed on its own, at
-  the cost of noisier scores. A zero-length word comes back with a null
-  `voice`, like any line too short to embed.
-- Whisper can return one segment over most of a song, its text a repeated
-  hallucination, when the accompaniment drowns the words. Such a line is a
-  duet line by the rule above whenever two singers are in it; re-run with
-  `timestamps: "word"`, or transcribe the `vocals` stem of
-  [`separate_stems`](#stem-separation) instead.
-- `start + 0.3 s` is a heuristic: a singer who opens late or holds a pickup
-  note is still closed-mouthed there. That is why the midpoint frame is read
-  too; trust the two together, not either alone.
+- A duet line (two singers in one Whisper segment) comes back `uncertain`, its
+  `reason` giving each voice's seconds. `timestamps: "word"` attributes word by
+  word, with noisier scores.
+- When accompaniment drowns the words, Whisper can return one hallucinated
+  segment over most of a song; re-run with `timestamps: "word"`, or transcribe
+  the `vocals` stem of [`separate_stems`](#stem-separation).
+- `start + 0.3 s` is a heuristic (a singer may open late), so trust it together
+  with the midpoint frame, not alone.
 
-### ingredients_grid
+## Data Gathering
+
+### gather_images
 
-Lay individual images - a character, a prop, a location - out on one canvas as a
-reference sheet. It is `frame_grid`'s counterpart for separate images, and builds
-the still `ltx2/reference-sheet` conditions on. Pure PIL.
+Load images from URLs and/or a file glob:
 
 ```json
 {
-    "name": "sheet",
     "task": {
-        "command": "ingredients_grid",
+        "command": "gather_images",
         "arguments": {
-            "images": ["asset:hero.png", "asset:sword.png", "asset:castle.png"],
-            "width": 768,
-            "height": 448
+            "urls": ["https://example.com/a.jpg", "https://example.com/b.jpg"],
+            "glob": "./images/*.jpg"
         }
-    },
-    "result": { "content_type": "image/png" }
+    }
 }
 ```
 
-| Argument | Default | Description |
-| -------- | ------- | ----------- |
-| `images` | required | Images in reading order: references, paths or `{"location": ...}` dicts; `gather:` of a `for_each` step splices in |
-| `width`, `height` | 768, 448 | Canvas size in pixels |
-| `layout` | `auto` | `rows`, `panels` or `auto` |
-| `fit` | `contain` | `contain` scales each image to fit whole and pads with `background`; `cover` fills its cell and crops the overflow |
-| `gap` | 8 | Pixels between cells, and kept clear of the canvas edge |
-| `background` | `white` | Canvas colour, a name or `#hex` |
-| `max_images` | 12 | More images than this is refused, never silently dropped |
+Returns a list of images that can be referenced by later steps with `previous_result:`.
 
-`rows` keeps every image at its own aspect ratio and justifies each row to the
-canvas width. Which images share a row is searched: every way of cutting the
-list, in order, into rows is scored on wasted canvas, overflow past the height,
-and spread of row heights. `panels` gives every image an equal grid cell, the
-column count chosen to waste the least canvas, with a short last row centred.
-`auto` uses `rows` for up to 3 images and `panels` for more. Transparency is
-flattened onto white. Returns one RGB image of exactly `width` x `height`.
+### gather_videos
 
-To build the sheet inside `ltx2/reference-sheet`, add this step first and point
-`static_sheet`'s `video` at it (`"video": "previous_result:sheet"` in place of the
-`reference_sheet` asset). Keep `width` and `height` the clip's own, so the sheet
-reads at the size the model was trained on:
+As `gather_images` (`glob`, `urls`), for video files. Each video is one
+artifact holding its frames and muxed audio. To *join* videos on disk, give
+their paths to `concat_videos` directly: a `previous_result` reference to a
+gather step fans the consuming step out over the videos.
 
-```json
-{
-    "name": "sheet",
-    "task": {
-        "command": "ingredients_grid",
-        "arguments": {
-            "images": ["asset:hero.png", "asset:sword.png", "asset:castle.png"],
-            "width": "variable:width",
-            "height": "variable:height"
-        }
-    },
-    "result": { "content_type": "image/png", "save": false }
-}
-```
+### gather_inputs
 
-## Data Gathering
+Returns its whole `arguments` dict unchanged; it takes any keys. Useful for
+organizing data flow.
 
-### gather_images
+### select
 
-Load images from URLs and/or file glob patterns:
+Pick one candidate out of many by a fixed rule, with no model. With `judge`
+it makes best-of-N:
 
 ```json
 {
+    "name": "pick",
     "task": {
-        "command": "gather_images",
+        "command": "select",
         "arguments": {
-            "urls": ["https://example.com/a.jpg", "https://example.com/b.jpg"],
-            "glob": "./images/*.jpg"
+            "candidates": ["gather:still"],
+            "scores": ["gather:judge"],
+            "rule": "argmax"
         }
-    }
+    },
+    "result": { "content_type": "image/jpeg" }
 }
 ```
 
-Returns a list of images that can be referenced by later steps with `previous_result:`.
-
-### gather_videos
-
-Same as `gather_images` but for video files. Each video comes back as one
-artifact holding its frames and whatever audio was muxed alongside them, so a
-step referencing this one iterates over videos rather than over frames.
-
-To *join* videos that are already on disk, give their paths to `concat_videos`
-directly rather than gathering them first: a `previous_result` reference to a
-gather step fans the consuming step out over the gathered videos instead of
-handing it all of them at once.
+| Argument | Required | Description |
+| -------- | -------- | ----------- |
+| `candidates` | Yes | The values to choose among, in order |
+| `scores` | Yes | One number (or numeric string) per candidate |
+| `rule` | Yes | `argmax`, `argmin`, `first_above` (first score at or above `threshold`), `first_below` (at or below) or `index` |
+| `threshold` | For `first_above` / `first_below` | The score to compare against |
+| `index` | For `index` | A whole-number position into `candidates` |
 
-### gather_inputs
+The step's result is the winning value; its position and score are recorded in
+the manifest and the step's end event. Refused, at validation where literal and
+at run time: candidates and scores of different lengths, a non-numeric score,
+an unknown rule, a missing `threshold` or `index`, an `index` out of range, and
+(at run time) no candidate passing a threshold rule.
 
-Pass through arguments directly. Useful for organizing data flow.
+**Example:** [best-of-n-to-video.json](../workflows/templates/best-of-n-to-video.json)
 
 ## Videos in image tasks
 
@@ -2836,20 +2675,18 @@ Upscale images using spandrel-compatible super-resolution models (ESRGAN, SwinIR
 | `image` | Yes | PIL Image or `previous_result:` reference - a video runs frame by frame, see [Videos in image tasks](#videos-in-image-tasks) |
 | `model_name` | Yes | HuggingFace repo ID or local file path |
 | `filename` | No | Specific weight file in a HF repo (auto-detected if only one) |
-| `tile_size` | No | Tile size for large images (default: 512) |
+| `tile_size` | No | Tile size (default: 512) |
 | `tile_overlap` | No | Overlap between tiles in pixels (default: 32) |
 
 A validate's `downloads_required` lists the `model_name` / `filename` file when it is not cached, or the whole repo when no `filename` is given (such a repo must hold exactly one weight file); a `previous_result:` or local-path model gives none.
 
-Large images are automatically tiled to avoid GPU memory issues. Models can be loaded from HuggingFace Hub repos or local `.pth`/`.safetensors` files.
+Images over 512x512 pixels are tiled. Models load from Hugging Face Hub repos (`.pth`, `.safetensors`, `.pt`, `.ckpt`) or local files.
 
-**Examples:**
-- [upscale-spandrel.json](../workflows/templates/upscale-spandrel.json) — Upscale any existing image 4x.
-- [upscale-spandrel.json](../workflows/templates/upscale-spandrel.json) — Upscale an image you already have; there is no generation step, so the input is a path or URL.
+**Example:** [upscale-spandrel.json](../workflows/templates/upscale-spandrel.json) — upscale an image you already have 4x; the input is a path or URL.
 
 ## Diffusion Upscaling
 
-Upscale images using Stable Diffusion upscale pipelines. Text-guided upscaling with better detail recovery than traditional super-resolution, especially for faces and textures.
+Text-guided upscaling with Stable Diffusion upscale pipelines, with better detail recovery than super-resolution on faces and textures.
 
 Two modes are available:
 - **x4** (default): `StableDiffusionUpscalePipeline` — 4x upscale via `stabilityai/stable-diffusion-x4-upscaler`
@@ -2882,9 +2719,40 @@ A validate's `downloads_required` lists the repo for the `mode` given; `model_na
 | `guidance_scale` | No | Classifier-free guidance scale (default: 9.0) |
 | `noise_level` | No | Noise level for x4 mode (default: 20, ignored for x2) |
 
-**Examples:**
-- [upscale-diffusion.json](../workflows/templates/upscale-diffusion.json) — Upscale any existing image. `mode` selects which: `x4` (the default) reaches 2048px, `x2` reaches 1024px through the latent upscaler.
-- [upscale-diffusion.json](../workflows/templates/upscale-diffusion.json) — Prompt-guided upscale of an image you already have, with no generation step.
+**Example:** [upscale-diffusion.json](../workflows/templates/upscale-diffusion.json) — prompt-guided upscale of an image you already have; `mode` selects `x4` (the default) or `x2`.
+
+## H3 Latent Upscaling
+
+Two tasks promote a MiniMax-H3 take in latent space; the workflow guide's
+[Promoting an H3 take](WORKFLOW_GUIDE.md#promoting-an-h3-take-to-768p-in-latent-space-upscale_h3_latents-and-decode_h3_latents)
+section has the full recipe.
+
+### upscale_h3_latents
+
+Resize H3 video latents `(B, 24, T, H, W)` to a larger plate without denoising
+again, as the input to a tiled refine. Upscale-only output above 768p is soft.
+
+| Argument | Required | Description |
+| -------- | -------- | ----------- |
+| `latents` | Yes | H3 latents, e.g. `previous_result:base.latents` |
+| `width` / `height` | Yes | The target size: multiples of 16, 1x to 4x the latents' own size per axis, at most 1920x1088 (or 1088x1920 portrait) |
+| `model_name` | No | Hub repo id, not a path (default `LBH-123-AI/Minimax_h3_latent_Upscaler`, at a pinned revision). `Alissonerdx/Minimax-H3-ComfyUI` with `h3_upscaler_lms_v0.1.safetensors` is a sharpness-tuned alternative |
+| `weight_name` | No | A bare `.safetensors` name (default `minimax_h3_latent_upscaler_3d_conv_v1_bf16.safetensors`, read from the default repo's `minimax_h3_latent_upscaler_3d_conv_v1/` folder) |
+
+Returns latents `(B, 24, T, height/16, width/16)` in the input's dtype and
+device (bf16 on GPU, fp32 on CPU). A size that breaks the rules above is
+refused, at validation for literal values; so is an input that is not H3
+latents. `downloads_required` lists the upscaler file.
+
+### decode_h3_latents
+
+Decode H3 latents to frames through diffusers' `MiniMaxH3VideoDecodeStep` (VAE
+in fp32), so the colours match the pipeline's own decode. Takes `latents` (a
+pipeline step's `latents` or an `upscale_h3_latents` result) and `model_name`
+(default `MiniMaxAI/MiniMax-H3`, its `vae` subfolder). Returns one video of the
+first batch item at 24 fps with no audio; `pair_audio` puts a soundtrack back.
+It declares no `required_weights`: the VAE is the one the H3 pipeline step
+already cached.
 
 ## Face Restoration
 
@@ -2914,7 +2782,7 @@ Restore and enhance faces in images using spandrel-compatible face restoration m
 | `only_center_face` | No | Only restore the largest/center face (default: false) |
 | `detection_resize` | No | Resize shorter side for detection speed (default: 640) |
 | `eye_dist_threshold` | No | Skip faces with eye distance below this (default: 5) |
-| `upsample_img` | No | Pre-upscaled background image (e.g., from a prior upscale step) |
+| `upsample_img` | No | Pre-upscaled background image (e.g., from a prior upscale step). Single-image input only: refused with a video |
 
 A validate's `downloads_required` lists the `model_name` / `filename` file when it is not cached (the whole repo when no `filename` is given), and facexlib's two weights as URL entries while they are missing from facexlib's weights directory: `detection_Resnet50_Final.pth` always and `parsing_parsenet.pth` unless `use_parse` is false. Those still download outside the Hugging Face cache.
 
@@ -2924,7 +2792,7 @@ Models are loaded via spandrel, so any `.pth`/`.safetensors` face restoration we
 
 ### Combining with Upscaling
 
-You can chain upscaling and face restoration. Generate first, upscale the background, then paste restored faces onto the upscaled image:
+Upscale the background, then paste restored faces onto the upscaled image:
 
 ```json
 {
@@ -2964,8 +2832,6 @@ You can chain upscaling and face restoration. Generate first, upscale the backgr
 }
 ```
 
-This gives the best results: the super-resolution model handles background detail while the face model handles facial features, composited together at the upscaled resolution.
-
 ## Object Segmentation
 
 Detect and segment objects using text prompts via GroundingDINO + SAM2. Returns a binary mask image suitable for inpainting workflows.
@@ -2994,7 +2860,7 @@ A validate's `downloads_required` lists `IDEA-Research/grounding-dino-base` and
 | `threshold` | No | Detection confidence threshold (default: 0.3) |
 | `invert` | No | Invert the output mask (default: false) |
 
-Returns a grayscale PIL Image (mode "L") — white (255) for detected objects, black (0) for background. SAM2 proposes three masks for each detected box, and segment keeps the one SAM2 rates best. That is usually the whole object: on the docs cup photo the first proposal was the body alone, without the lid and with a hole at the logo. To erase what it found, pass the mask to [`remove_object`](#object-removal), which chains with no `invert`. To paint something new in its place, pass it to a prompted inpainter such as FluxFillPipeline.
+Returns a grayscale PIL Image (mode "L"): white (255) for detected objects, black (0) for background. Of SAM2's three proposals per detected box, segment keeps the one SAM2 rates best, usually the whole object. To erase what it found, pass the mask to [`remove_object`](#object-removal), which chains with no `invert`. To paint something new in its place, pass it to a prompted inpainter such as FluxFillPipeline.
 
 **Examples:**
 
@@ -3038,9 +2904,7 @@ Erase what a mask covers and fill the hole from the surrounding texture, with no
 
 **Limits.** LaMa suits small to medium objects: a cup, a sign, a watermark, a passer-by in the distance. A large region, such as a person filling a third of the frame, comes back smeared. For that, a prompted inpainter whose prompt names the background is the remaining route, and it may paint a patch that does not match (#946).
 
-**Weights.** `michaelgold/big-lama`, file `big-lama.safetensors` (about 200 MB), read at revision `98ab7cface89a594b381f32fc7a9f2cae3f91ccc`. `biali/big-lama` holds a byte-identical copy and is the fallback if that repo disappears; it is not tried automatically. LaMa is by advimman (Suvorov et al., *Resolution-robust Large Mask Inpainting with Fourier Convolutions*) and is licensed **Apache-2.0**. The Hub repo's card says MIT, but the upstream licence is the one that applies. The task reads safetensors only: `validate_workflow` refuses a `weight_name` that is not `.safetensors` (the common `big-lama.pt` is a TorchScript file) and a `model_name` that is a local path, and the loader refuses both again at run time. Weights that do not load as an inpainting model are refused too. The network runs in fp32 on every device.
-
-**First run.** The weights download on the first run, about 200 MB. A validate's `downloads_required` lists `michaelgold/big-lama` / `big-lama.safetensors` as a file entry (pinned at that revision for the default pair only; another `model_name` or `weight_name` is read at `main`) while it is not cached, so the download is quoted beforehand.
+**Weights.** `michaelgold/big-lama`, file `big-lama.safetensors` (about 200 MB), read at revision `98ab7cface89a594b381f32fc7a9f2cae3f91ccc` for the default pair (another `model_name` or `weight_name` is read at `main`); `downloads_required` lists it as a file entry while it is not cached. `biali/big-lama` holds a byte-identical copy and is not tried automatically. LaMa (Suvorov et al.) is licensed **Apache-2.0**, whatever the Hub card says. The task reads safetensors only: a `weight_name` that is not `.safetensors` (the common `big-lama.pt` is TorchScript) and a `model_name` that is a local path are refused at validation and at run time, as are weights that do not load as an inpainting model. It runs in fp32 on every device.
 
 **Examples:**
 
@@ -3049,9 +2913,10 @@ Erase what a mask covers and fill the hole from the surrounding texture, with no
 
 ## Image Captioning
 
-Generate text captions from images using a vision-language model.
-
-Transformers 5 removed the dedicated `image-to-text` pipeline this task used to build, along with the BLIP/ViT-GPT2/GIT captioning models that ran on it. Captioning now goes through the same `image-text-to-text` pipeline as any other VLM, so `model_name` needs a vision-language model (SmolVLM, Qwen2.5-VL, LLaVA, etc.) and `prompt` is a question put to the model rather than a text fragment to continue.
+Caption an image with a vision-language model, through transformers'
+`image-text-to-text` pipeline: `model_name` must be a VLM (SmolVLM, Qwen2.5-VL,
+LLaVA, ...) and `prompt` is a question put to it. BLIP/ViT-GPT2/GIT captioners
+do not load (transformers 5 removed them).
 
 A validate's `downloads_required` lists `HuggingFaceTB/SmolVLM-256M-Instruct` for `image_to_text` and `judge`; `model_name` overrides it.
 
@@ -3075,28 +2940,41 @@ A validate's `downloads_required` lists `HuggingFaceTB/SmolVLM-256M-Instruct` fo
 | `system_prompt` | No | System instruction for the model |
 | `max_new_tokens` | No | Maximum tokens to generate (default: 50) |
 
-The default model is deliberately tiny, matching the footprint of the old captioning default; it produces short, plain captions. Point `model_name` at something larger for detail.
-
-Returns a caption string. Save as `text/plain` for `.txt` output, or pass to a downstream step via `previous_result:` as a prompt for image generation.
+The default model is tiny and writes short, plain captions; point `model_name` at something larger for detail. Returns a caption string: save as `text/plain`, or pass on via `previous_result:` as a prompt.
 
-For a detailed caption, hand the image to [`text_generation`](#text-generation) with a question and a larger vision-language model; that is what [describe-and-regenerate.json](../workflows/templates/describe-and-regenerate.json) does ahead of its prompt expansion.
+For a detailed caption, hand the image to [`text_generation`](#text-generation--prompt-expansion) with a question and a larger vision-language model; that is what [describe-and-regenerate.json](../workflows/templates/describe-and-regenerate.json) does ahead of its prompt expansion.
 
 **Examples:**
 
-- [image-to-text.json](../workflows/templates/image-to-text.json) — Basic captioning with the default model, saves as `.txt`
-- [image-to-text.json](../workflows/templates/image-to-text.json) — Larger VLM answering a specific question
+- [image-to-text.json](../workflows/templates/image-to-text.json) — captioning with the default model, saved as `.txt`; set `model_name` to a larger VLM and ask a question
 - [describe-and-regenerate.json](../workflows/templates/describe-and-regenerate.json) — Describe an image, expand the caption, then regenerate it
 
+### judge
+
+Score an image against a rubric with a vision-language model, for `select` to
+pick from. Returns a bare number, so the step takes no `result` block.
+
+| Argument | Required | Description |
+| -------- | -------- | ----------- |
+| `image` | Yes | An image (or a list, scored as one prompt) |
+| `rubric` | Yes | The question or criterion |
+| `scale` | Yes | `[low, high]`; the prompt asks for one number in that range |
+| `model_name` | No | A VLM (default `HuggingFaceTB/SmolVLM-256M-Instruct`) |
+| `max_new_tokens` | No | Default `20` |
+
+Decoding is greedy, so a score reproduces. The first number in the reply is
+the score, not clamped to `scale`; a reply with no number is refused ("could
+not parse a score"). **Example:** [best-of-n-to-video.json](../workflows/templates/best-of-n-to-video.json)
+
 ## Composing Text
 
-Assemble one block of text out of parts written once. A multi-shot workflow
-says the same things about its characters in every shot — who they are, what
-they are wearing, what their voice sounds like — and the engine deliberately
-has no string interpolation to splice them in with (see the no-interpolation
-rule in [the workflow guide](WORKFLOW_GUIDE.md)). Composition is the way
-round it: a part is a *whole* value, and `compose_text` joins parts in order.
+Assemble one block of text out of parts written once, such as a character
+bible repeated in every shot's prompt. The engine has no string interpolation
+(see the no-interpolation rule in [the workflow guide](WORKFLOW_GUIDE.md)): a
+part is a *whole* value, and `compose_text` joins parts in order.
 
 ```json
+[
 {
     "name": "shot_1_prompt",
     "task": {
@@ -3115,6 +2993,7 @@ round it: a part is a *whole* value, and `compose_text` joins parts in order.
     "name": "shot_1",
     "pipeline": { "arguments": { "prompt": "previous_result:shot_1_prompt" } }
 }
+]
 ```
 
 | Argument | Required | Description |
@@ -3123,13 +3002,10 @@ round it: a part is a *whole* value, and `compose_text` joins parts in order.
 | `separator` | No | What goes between the parts (default: a blank line, the paragraph break the prompt formats use) |
 | `skip_empty` | No | Drop parts that are null or blank (default `true`). With it off, an empty part still contributes its separator |
 
-A part that is neither text nor a number is an error, not a coercion: it means
-the reference in that position resolved to something other than the text meant.
-
-The parts are positional. A named form (`"{bible} says {line}"`) would be the
-interpolation the engine does not have, one layer down — so a character bible
-is a variable named by every shot that needs it, and a voice string written
-once is checked by being the same value rather than by being compared.
+Refused: a `parts` that is not a list; a part that is neither text nor a
+number (the reference resolved to something other than text); and a list left
+with nothing once empty parts are dropped. The parts are positional; there is
+no named form.
 
 ## Extracting Sections
 
@@ -3154,15 +3030,18 @@ Reduce generated text to a known set of labelled sections, dropping anything els
 | `sections` | Yes | Section labels to keep, in the order they should appear |
 | `keep_preamble` | No | Keep any text before the first label (default: `true`) |
 
-A section runs from its `label:` to the end of that paragraph, so a blank line ends one and a single newline does not — a field holding one line per item stays intact. Repeats are dropped, missing sections are skipped, and text with no recognised label is returned unchanged.
-
-This exists because a model asked for a rigid format usually produces it and then keeps going — restating the description, appending a summary, or looping until it runs out of tokens. Prompting against that is unreliable, and at small model sizes adding rules to an already long specification can make adherence worse. Trailing text is not free either: a prompt is conditioning, and a pipeline that does not truncate spends memory and attention on whatever arrives. Keeping the fields that were asked for is deterministic where prompting is not.
+A label counts only at the start of a line, in any capitalisation, and is
+written back in the declared spelling. A section runs from its `label:` to the
+end of that paragraph, so a blank line ends one and a single newline does not.
+Repeats are dropped, missing sections are skipped, and text with no recognised
+label is returned trimmed but otherwise as it is. Use it on a model that
+produces a rigid format and then keeps going.
 
 The built-in `h3_context_ir` workflow applies this to its own output, so a workflow delegating to it receives only the fields MiniMax H3 expects.
 
 ## Text Generation / Prompt Expansion
 
-Generate or expand text using a local language model. Useful for expanding short prompts into detailed image generation prompts, rewriting text, or other text-to-text tasks.
+Generate or expand text with a local language model, such as a short prompt expanded into a detailed one.
 
 A validate's `downloads_required` lists `Qwen/Qwen2.5-1.5B-Instruct`, or `HuggingFaceTB/SmolVLM-256M-Instruct` when an `image` is given; `model_name` overrides it. A `system_prompt.from_hub` file is not listed.
 
@@ -3220,37 +3099,42 @@ Supplying `image` switches the task to a vision-language model, so the generated
 }
 ```
 
-This matters most ahead of an image-conditioned generation step. Those pipelines pin the supplied picture as the first frame, so a prompt written without seeing it will describe a scene the keyframe contradicts and the two conditionings pull against each other. Pass the same image to both and the prompt agrees with the frame it opens on.
+Ahead of an image-conditioned generation step, which pins the picture as the
+first frame, pass the same image to both so the prompt agrees with the frame.
+A vision model is worth releasing before the generation model loads
+(`release_models` in the workflow guide).
 
-A vision model is large enough to be worth releasing before the generation model loads — see `release_models` in the workflow guide.
+Generation is greedy so a workflow reproduces. Greedy decoding against a long,
+rigid format makes these models loop, so the vision path applies a
+`repetition_penalty` of 1.15 (on Qwen3-VL against the MiniMax H3 spec, 1.05
+still looped; 1.15 stopped on its own). Raise it if a model still repeats
+itself, or set `1.0` to disable; trim the output with
+[`extract_sections`](#extracting-sections) to keep only the fields asked for.
+Qwen3-VL-4B and 8B do not produce the H3 spec's `<d>[Language]...</d>` dialogue
+tag or `(S1)` speaker ids; their visual description is usable, but write
+prompts by hand where a subject has to speak.
 
-Generation stays greedy so a workflow reproduces, but greedy decoding against a long, rigid format specification makes these models loop — emitting a complete answer and then repeating its closing sections until the token budget runs out. The vision path applies a `repetition_penalty` of 1.15 to stop that. Measured on Qwen3-VL against the MiniMax H3 prompt spec, 1.05 still looped through the whole budget while 1.15 ended on its own at a length matching the format's own guidance. Raise it if a model still repeats itself, or set `1.0` to disable.
-
-A penalty reins the looping in but does not guarantee the model stops where the format ends; for that, trim the output with `extract_sections` below.
-
-There is a limit to what a small model will follow. Against the MiniMax H3 spec, neither Qwen3-VL-4B nor 8B produces the `<d>[Language]...</d>` dialogue tag or the `(S1)` speaker ids, whether the idea implies speech or supplies the line verbatim; the 8B is worse on layout, capitalising its section labels. Showing a complete worked example does produce them - by copying the example word for word, which is useless - and a placeholder skeleton does not produce them at all. The visual description these models write is grounded and usable; the dialogue markup is not. Write prompts by hand where a subject has to speak.
-
-For anything the arguments above do not cover, `generate_kwargs` goes straight to `generate()`:
+`generate_kwargs` goes straight to `generate()`, merged last, so it overrides
+`repetition_penalty` and the sampling settings:
 
 ```json
-"arguments": {
-    "prompt": "a cat on a windowsill",
-    "generate_kwargs": { "no_repeat_ngram_size": 25 }
+{
+    "arguments": {
+        "prompt": "a cat on a windowsill",
+        "generate_kwargs": { "no_repeat_ngram_size": 25 }
+    }
 }
 ```
 
-It is merged after everything else, so it can override `repetition_penalty` and the sampling settings as well as add to them.
-
-`generate_kwargs` is checked before the model loads, the same way `chat_template_kwargs` is. Values must be scalars or lists of scalars (`stop_strings` is a list), and the call's own keys are refused (`max_new_tokens`, `tokenizer_encode_kwargs`, `prefix`, `handle_long_generation` and the rest of the pipeline's named arguments). `custom_generate` and `trust_remote_code` need trusted workflows (`--trust-workflows`): with them, transformers downloads a repo's `custom_generate/generate.py` and runs it in place of its own decoding loop.
+It is checked before the model loads, as `chat_template_kwargs` is. Values must be scalars or lists of scalars (`stop_strings` is a list), and the call's own keys are refused (`max_new_tokens`, `tokenizer_encode_kwargs`, `prefix`, `handle_long_generation` and the rest of the pipeline's named arguments). `custom_generate` and `trust_remote_code` need trusted workflows (`--trust-workflows`): with them, transformers downloads a repo's `custom_generate/generate.py` and runs it in place of its own decoding loop.
 
 **Examples:**
 
-- [expand-prompt.json](../workflows/templates/expand-prompt.json) — Expand a short prompt and save as `.txt`
-- [expand-prompt.json](../workflows/templates/expand-prompt.json) — Expand prompt, then generate with Flux
+- [expand-prompt.json](../workflows/templates/expand-prompt.json) — Expand a short prompt and save as `.txt`; add a pipeline step to generate from it
 
 ## Speech Generation
 
-Speak a line of text with a local text-to-speech model. A validate's `downloads_required` lists `suno/bark-small` (`model_name` overrides it), plus `speechbrain/spkrec-xvect-voxceleb` when `speaker_embedding` is given. Any transformers text-to-speech checkpoint can be named as `model_name`; three families are wired for their voice controls:
+Speak a line of text with a local text-to-speech model. A validate's `downloads_required` lists `suno/bark-small` (`model_name` overrides it), plus `speechbrain/spkrec-xvect-voxceleb` when `speaker_embedding` is given. Any transformers text-to-speech checkpoint can be named as `model_name`. Three transformers families are wired for their voice controls, and Audio8-TTS has its own vendored branch:
 
 | Family | Checkpoint | Voice |
 |--------|-----------|-------|
@@ -3277,26 +3161,24 @@ The result is a waveform carrying the rate its model generated at, so it compose
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
 | `text` | One of `text`/`messages` | The line to speak |
-| `messages` | One of `text`/`messages` | Chat-templated input for a model such as VibeVoice that takes a conversation rather than a bare string — a list of `{"role": ..., "content": ...}` dicts, passed straight through as the pipeline's `text_inputs` so the model's own chat template applies. A model with no chat template configured (Bark and friends) raises when handed this instead of `text` |
+| `messages` | One of `text`/`messages` | Chat input for a model that takes a conversation (a list of `{"role": ..., "content": ...}` dicts), passed through as the pipeline's `text_inputs` so the model's chat template applies. A model with no chat template (Bark) raises |
 | `model_name` | No | HuggingFace model ID (default: `suno/bark-small`) |
-| `voice_preset` | No | The speaker, for a model with presets — `v2/en_speaker_0` through `v2/en_speaker_9` for Bark. A model with no processor (a single-voice model such as `facebook/mms-tts-eng`) refuses a `voice_preset` with an error rather than ignoring it |
-| `speaker_embedding` | No | A reference audio file (typically an `asset:` reference) whose voice a SpeechT5 model should speak in. Reduced to an x-vector with speechbrain's `spkrec-xvect-voxceleb` and injected into `forward_params` as `speaker_embeddings`. A model that isn't SpeechT5 refuses it the same way a single-voice model refuses `voice_preset` |
+| `voice_preset` | No | The speaker, for a model with presets (`v2/en_speaker_0` through `v2/en_speaker_9` for Bark). A preprocessing argument: passed through `forward_params` it would be dropped. A single-voice model with no processor (`facebook/mms-tts-eng`) refuses it |
+| `speaker_embedding` | No | A reference audio file (typically `asset:`) whose voice a SpeechT5 model speaks in, reduced to an x-vector with speechbrain's `spkrec-xvect-voxceleb` and passed as `speaker_embeddings`. Any other model refuses it |
 | `reference_audio` | No | Audio8-TTS only: a clip of the voice to clone (typically an `asset:` reference), up to 20 s; 5-10 s of clean speech is plenty. Needs `reference_text` |
 | `reference_text` | No | Audio8-TTS only: the words spoken in `reference_audio`, verbatim. Needs `reference_audio` |
-| `forward_params` | No | Passed to the model's forward/generate call |
+| `forward_params` | No | Passed to the model's forward/generate call; a multi-speaker VITS takes its `speaker_id` int here |
 | `generate_kwargs` | No | Ad-hoc generation settings for a generative model — `temperature`, `do_sample` |
 
-The command's own default is Bark because its voice presets give distinct speakers, which is what two characters in a scene need; `facebook/mms-tts-eng` is a quarter the size and a good override where one voice will do. [`generate-speech.json`](../workflows/templates/generate-speech.json) ships with `facebook/mms-tts-eng` as its own default instead, since a template is usually a single voice. `voice_preset` is a preprocessing argument — it selects the speaker before generation rather than parameterizing it — so naming it here is what makes it reach the processor. Passed through `forward_params` it would be dropped and every character would sound the same.
-
-`speaker_embedding` is the same kind of preprocessing argument for a SpeechT5 model (`microsoft/speecht5_tts`), which conditions its voice on an x-vector rather than a preset name. A VITS model's speaker is different again — a plain `speaker_id` int, passed through `forward_params` unchanged, since it was never a preprocessing argument and needs no argument of its own here.
+The command defaults to Bark for its distinct speakers; [`generate-speech.json`](../workflows/templates/generate-speech.json) defaults to `facebook/mms-tts-eng`, a quarter the size, for a single voice.
 
-The result needs no `sample_rate`. A generated track carries the rate its model produced it at, and that beats the 44100 default; declaring one still wins over both, for a track whose rate was reported wrong. Every TTS model runs at a different rate, so a declared rate that does not match plays the speech at the wrong speed and pitch without ever failing.
+The result needs no `sample_rate`: the track carries its model's rate. A declared `result.sample_rate` still wins, and one that does not match plays the speech at the wrong speed and pitch; the run succeeds with a `rate_override_mismatch` warning.
 
 ### Cloning a voice (Audio8-TTS)
 
-`model_name: "Edge0/Audio8-TTS-Realistic-0.6b"` takes its own branch. The model is vendored (`dw/vendored/audio8_tts_model.py`), so it loads no code from its repo, and its files - `config.json`, `generation_config.json`, `model.safetensors`, `tokenizer.json` and the codec `codec.pth` - are fetched at one pinned revision; a validate's `downloads_required` lists all five. The result is a 44.1 kHz mono track.
+`model_name: "Edge0/Audio8-TTS-Realistic-0.6b"` takes its own branch. The model is vendored (`dw/vendored/audio8_tts_model.py`), so no code loads from its repo; its files (`config.json`, `generation_config.json`, `model.safetensors`, `tokenizer.json`, the codec `codec.pth`) are fetched at one pinned revision, and `downloads_required` lists all five. The result is a 44.1 kHz mono track.
 
-Give `reference_audio` and `reference_text` together and it speaks `text` in the reference's voice; leave both out and it speaks in its default voice. The language follows `text` - there is no `language` argument. Every refusal fires before the model loads:
+With `reference_audio` and `reference_text` together it speaks `text` in the reference's voice; with neither, in its default voice. The language follows `text`. Every refusal fires before the model loads:
 
 - `reference_audio` without `reference_text` (an empty one counts as missing), or the reverse;
 - either one on any other model;
@@ -3305,26 +3187,36 @@ Give `reference_audio` and `reference_text` together and it speaks `text` in the
 - text that, with the reference, won't fit the model's 2,048-position context. It is refused, never truncated: split the line;
 - a `generate_kwargs` / `forward_params` key outside `temperature`, `top_p`, `top_k`, `do_sample`, `max_new_tokens`. `max_new_tokens` defaults to the room left in the context.
 
-The fit check is an estimate from the text's length; a line that passes it but still runs out of room before the model finishes is refused after generation rather than returned cut off. The workflow's `seed` fixes the take: the same seed, text and reference give the same audio.
+The fit check is an estimate; a line that passes it but runs out of room is refused after generation rather than returned cut off. The same seed, text and reference give the same audio.
 
 [`voice-clone.json`](../workflows/templates/voice-clone.json) is the template. **Clone only a voice you have the speaker's consent to use.**
 
 ### Generating a voice to condition on
 
-The role this earns its place in is voice *timbre reference*, not the track a mouth follows. A `MiniMaxH3AudioReference` is audio H3 reads, not audio it plays: it takes a few seconds of a voice to fix timbre, pitch and delivery while H3 still generates the line, and a mouth asked to follow a whole track through a reference follows it loosely. `music-video` and the `match_audio` chain templates pass each shot's slice as one, and a reference alone can re-sing the track shifted by whole beats (#795). Holding the track with `hold_audio` (the `workflows` guide, "H3: generating to a held soundtrack") keeps it exact: `music-video` holds each slice by default, the chains leave it null. #619 found hold with image-only references worse than the reference alone on a sung track; #808 measured hold plus the reference at 0.00 s offset on every shot against up to 1.37 s for the reference alone, audio only, mouth onsets not counted. Build the reference with `from_previous_result` and the clip's own sample rate comes across with it:
+Generated speech is best used as a voice *timbre reference*, not as the track
+a mouth follows. A `MiniMaxH3AudioReference` is audio H3 reads, not plays: a
+few seconds of a voice fix timbre, pitch and delivery while H3 still generates
+the line, and a mouth follows a whole track given only as a reference loosely,
+sometimes shifted by whole beats (#795). To keep a track exact, hold it with
+`hold_audio` (workflow guide, "H3: generating to a held soundtrack");
+`music-video` holds each slice by default (#808). Build the reference with
+`from_previous_result`, and the clip's sample rate comes with it:
 
 ```json
-"references": [
-    {
-        "reference_type": "diffusers.modular_pipelines.minimax_h3.MiniMaxH3AudioReference",
-        "from_previous_result": "voice"
-    }
-]
+{
+    "references": [
+        {
+            "reference_type": "diffusers.modular_pipelines.minimax_h3.MiniMaxH3AudioReference",
+            "from_previous_result": "voice"
+        }
+    ]
+}
 ```
 
-Referencing the same preset in every shot of a scene makes a character's voice a conditioning signal rather than a prose description that has to land identically a dozen times. The other honest uses are a voice that must be matched — a specific delivery H3 will not produce from description alone — and narration over shots where nothing has to lip-sync to it, muxed with [`pair_audio`](#pair_audio).
-
-A speech model is worth releasing before a video model loads — set `release_models` on the step, as in the example below.
+The same preset referenced in every shot of a scene makes a character's voice
+a conditioning signal rather than a prose description. Narration over shots
+nothing lip-syncs to is muxed with [`pair_audio`](#pair_audio). Set
+`release_models` on the speech step before a video model loads.
 
 **Examples:**
 
@@ -3355,17 +3247,17 @@ A speech model is worth releasing before a video model loads — set `release_mo
 | `video` | One of `video`/`prompt` | The clip the effects follow (typically an `asset:` or `previous_result:` reference) |
 | `prompt` | One of `video`/`prompt` | What should be heard |
 | `negative_prompt` | No | What to steer away from, such as `music, speech` |
-| `duration` | No | Seconds. Defaults to the clip's length, or 8 s text-only; at least 0.64 s (16 sync frames at 25 fps), at most 20 s and never longer than the clip |
+| `duration` | No | Seconds. Defaults to the clip's length (capped at 20 s), or 8 s text-only; a given value must be at least 0.64 s (16 sync frames at 25 fps), at most 20 s and no longer than the clip |
 | `steps` | No | Sampler steps (default: 25) |
 | `guidance_scale` | No | Classifier-free guidance (default: 4.5) |
 
-Video-only is the weaker mode: give a `prompt` that names the on-screen sound sources as well. The model was trained around 8 s, so a `duration` outside 4-12 s adds a warning to the result, as does noise-like output (spectral flatness of 0.3 or more with a centroid of 8 kHz or more).
+Video-only is the weaker mode; give a `prompt` naming the on-screen sound sources too. A `duration` outside 4-12 s (the model trained around 8 s) warns, as does noise-like output (spectral flatness of 0.3 or more with a centroid of 8 kHz or more).
 
 Every refusal fires before anything loads: neither `video` nor `prompt`; a `duration` under 0.64 s, over 20 s or longer than the clip; an unknown argument; and `trust_remote_code`, `custom_pipeline` or `custom_generate` (the trust gate).
 
 The output runs 7-25 dB hot. Route it through `match_levels` before mixing it with dialogue or music.
 
-**Licences.** The MMAudio weights (network, Synchformer and the VAE tensors) are CC-BY-NC 4.0: non-commercial use only. The DFN5B CLIP encoder is under the Apple Machine Learning Research Model License: research purposes only. Apple Machine Learning Research Model is licensed under the Apple Machine Learning Research Model License Agreement. See [ATTRIBUTIONS.md](ATTRIBUTIONS.md).
+**Licences.** The MMAudio weights (network, Synchformer and the VAE tensors) are CC-BY-NC 4.0: non-commercial use only. The DFN5B CLIP encoder is under the Apple Machine Learning Research Model License Agreement: research purposes only. See [ATTRIBUTIONS.md](ATTRIBUTIONS.md).
 
 **Examples:**
 
@@ -3376,7 +3268,7 @@ The output runs 7-25 dB hot. Route it through `match_levels` before mixing it wi
 
 A validate's `downloads_required` lists `openai/whisper-base`; `model_name` overrides it. `check_script` uses the same model.
 
-Transcribe spoken audio to text with a local Whisper-class model. The word-correctness of a TTS deliverable — a dropped line, a mid-sentence truncation — can only be inferred from duration and timing arithmetic without this; `transcribe_audio` checks it directly against the text the deliverable was supposed to speak.
+Transcribe spoken audio to text with a local Whisper-class model.
 
 ```json
 {
@@ -3392,16 +3284,24 @@ Transcribe spoken audio to text with a local Whisper-class model. The word-corre
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | Path or URL of an audio file (or of a video file, whose soundtrack is taken), a video with a soundtrack, or a waveform — usually a `previous_result:` reference |
-| `sample_rate` | No | Sample rate of a waveform passed directly |
+| `audio` | Yes | An [audio input](#slice_audio) |
+| `sample_rate` | No | Rate of a bare waveform |
 | `model_name` | No | HuggingFace model ID of a Whisper-class ASR model (default: `openai/whisper-base`) |
 | `timestamps` | No | `"segment"` or `"word"` to get chunk timings instead of plain text (see below) |
 
-Multi-channel audio is downmixed to mono and resampled to 16 kHz before transcription, since that is what a Whisper-class model is trained on; the source audio itself is untouched. By default the result is plain text, read with MCP's `get_output_text`.
+Audio is downmixed to mono and resampled to 16 kHz for the model; the source is untouched. By default the result is plain text, read with MCP's `get_output_text`.
+
+Set `timestamps` to `"segment"` or `"word"` to get chunk timings instead. The result becomes `{"text": ..., "chunks": [{"start": ..., "end": ..., "text": ...}, ...]}`, so the step's `result.content_type` must be `"application/json"`, read with MCP's `get_output_text`. Every `start` and `end` is a number of seconds: a chunk the clip ends inside - a song cut mid-line, which Whisper leaves open-ended - ends at the clip's duration, so the transcript drops straight into [`attribute_voices`](#voice-attribution)' `lines`. `"word"` can give a zero-length chunk (`start` = `end`) on a short or clipped word; `attribute_voices` reads it as a line with no voice.
+
+A chunk that is probably not speech carries one more field, `suspect` (#822), and is never removed. The reasons are tried in this order, and the first that holds wins:
 
-Set `timestamps` to `"segment"` or `"word"` to get chunk timings instead — a music video cut to the lyric, or a dialogue shot checked against its line, needs the times Whisper already produces past 30 s rather than the collapsed string. The result becomes `{"text": ..., "chunks": [{"start": ..., "end": ..., "text": ...}, ...]}`, so the step's `result.content_type` must be `"application/json"` rather than `"text/plain"`, and it's read with MCP's `get_output_text` (JSON results are text). A clip under 30 s asks Whisper for timestamps explicitly when `timestamps` is set — the 30 s long-form threshold is a separate, unrelated reason to ask. Every `start` and `end` is a number of seconds: a chunk the clip ends inside - a song cut mid-line, which Whisper leaves open-ended - ends at the clip's duration, so the transcript drops straight into [`attribute_voices`](#voice-attribution)' `lines`. `"word"` can give a zero-length chunk (`start` = `end`) on a short or clipped word; `attribute_voices` reads it as a line with no voice.
+- `"no_words"`: its text has no word at all (`—`, `♪`, `...`).
+- `"repetition"`: it holds a word of a repetition loop (`I'm sorry, I'm sorry, ...`), found by the detector and `REPEAT_RUN_MIN`/`REPEAT_MAX_PERIOD` that [`check_script`](#script-check) uses, however loud; or its text and overlapping span repeat an earlier chunk's (#1138).
+- `"silent"`: its loudest window is at or below `GUARD_FLOOR_DBFS`, where Whisper invents text; or the whole clip's mean level is at or below `NEAR_SILENT_CLIP_DBFS` (-40 dBFS), as in a voiceless vocals stem (#1135).
+- `"stock_phrase"`: its whole text is one of Whisper's stock outro lines ("Thank you for watching", "Please subscribe") (#1136).
+- `"long"`: a `"word"` chunk lasting more than `LONG_WORD_SECONDS` (2 s), an instrumental folded into the word. A `"segment"` chunk is never `long`.
 
-A chunk that is probably not speech carries one more field, `suspect` (#822), and is never removed. The first reason that holds wins: `"no_words"` when its text has no word at all (`—`, `♪`, `...`); `"repetition"` when it holds a word of a repetition loop (`I'm sorry, I'm sorry, ...`), found by the same detector and `REPEAT_RUN_MIN`/`REPEAT_MAX_PERIOD` [`check_script`](#script-check) discards loops with, however loud, and also a chunk whose text and overlapping span repeat an earlier chunk's (Whisper's duplicate decodes of one loop, #1138); `"stock_phrase"` when its whole text is one of Whisper's stock outro lines ("Thank you for watching", "Please subscribe"), which it writes over music at any level (#1136); `"silent"` when its loudest window sits at or below the dead-air floor `check_script` discards words at (`GUARD_FLOOR_DBFS`) - Whisper invents text over silence, a fade-out's end credits included; `"silent"` also marks every chunk of a clip whose whole mean level is at or below `NEAR_SILENT_CLIP_DBFS` (-40 dBFS), such as a voiceless vocals stem with residual music (#1135); and `"long"` when a `"word"` chunk lasts more than `LONG_WORD_SECONDS` (2 s), an instrumental folded into the word. A `"segment"` chunk is never `long`. A clean input does not prevent the marks: transcribe a song's vocal stem ([`separate_stems`](#stem-separation)) so an instrumental intro is not counted as speech, and the marks catch what Whisper still invents over the stem - its leakage can keep an intro above the floor, which is what `no_words` and `repetition` are for. [`plan_cuts`](#plan_cuts) leaves `no_words`, `repetition`, `stock_phrase` and `silent` chunks out of the lines and warns once per run of marked chunks.
+Transcribe a song's vocal stem ([`separate_stems`](#stem-separation)) so an instrumental intro is not counted as speech; the marks catch what Whisper still invents over the stem. [`plan_cuts`](#plan_cuts) leaves `no_words`, `repetition`, `stock_phrase` and `silent` chunks out of the lines.
 
 To compare a take against the lines it was meant to speak, use [`check_script`](#script-check) rather than reading the transcript by eye.
 
@@ -3411,16 +3311,16 @@ To compare a take against the lines it was meant to speak, use [`check_script`](
 
 A validate's `downloads_required` lists `openai/whisper-base` for its transcription; `model_name` overrides it.
 
-Whether a dialogue take speaks its script. Confirming it used to mean transcribing the take and comparing the transcript to the script by eye, which missed a word Whisper dropped, an H3 tag read aloud, and a last word cut off by the end of the file. `check_script` does the comparison and reports where to look.
+Whether a dialogue take speaks its script: a dropped word, an H3 tag read aloud, a last word cut off by the end of the file.
 
-It transcribes the take with [`transcribe_audio`](#speech-transcription) word timestamps - the same code and model cache, no second ASR path. Every heard word is measured, and one at or below the dead-air floor is discarded as unheard, because Whisper invents words over silence. So is every word of a repetition loop - the same words over and over (`Pre-pre-pre-...`), which Whisper invents over music or room tone loud enough to pass the floor. H3 markup is stripped from each expected line, and the heard words are aligned to the expected words in order (`difflib`). Each line scores the similarity of its own expected and heard words. It decides nothing: findings are places to look, not verdicts. It is not an assessment probe (`assessment=True`); like [`attribute_voices`](#voice-attribution) it answers a JSON document.
+It transcribes the take with [`transcribe_audio`](#speech-transcription) word timestamps (same code and model cache). A heard word at or below the dead-air floor is discarded as unheard, as is every word of a repetition loop (`Pre-pre-pre-...`). H3 markup is stripped from each expected line, the heard words are aligned to the expected words in order (`difflib`), and each line scores the similarity of its expected and heard words. Findings are places to look, not verdicts. It is not an assessment probe; it answers a JSON document.
 
 ```json
 {
     "task": {
         "command": "check_script",
         "arguments": {
-            "audio": "output:take.wav",
+            "audio": "asset:take.wav",
             "lines": ["<d>[English] Where were you?</d>", "I was at the station."]
         }
     },
@@ -3432,13 +3332,12 @@ The step's `result.content_type` must be `"application/json"`; read the answer w
 
 | Argument | Required | Description |
 | -------- | -------- | ----------- |
-| `audio` | Yes | The take - a path, `asset:`/`output:` reference, or an earlier step's audio or video (its soundtrack is taken) |
+| `audio` | Yes | The take, an [audio input](#slice_audio) |
 | `lines` | Yes | The expected lines in order: a list of strings or `{text, shot}` objects, `shot` naming the take's shot the line belongs in; a shot name the map holds more than once (a cut joined from episodes sharing names) also needs `start_frame` on the line, `{text, shot, start_frame}`, and findings call it `name#start_frame`. Markup is stripped (below). `[]` means no speech is expected |
 | `shots` | No | The take's shot map, `[{name, start_frame, num_frames, start_sample, num_samples}]`. Default: the take's own `.shots` (an earlier step's video), else the run manifest or `keep_output` sidecar beside an `asset:`/`output:` file - resolved as the assessment probes resolve them (`assess.resolve_shots`). The argument wins over both |
 | `similarity` | No | Least similarity, 0..1, of a line's heard words to its expected words before it is a `line_mismatch` (default `0.85`) |
 | `model_name` | No | HuggingFace ID of a Whisper-class ASR model (default `openai/whisper-base`) |
-| `sample_rate` | No | Sample rate of a waveform passed directly |
-| `device` | No | Where the ASR model runs |
+| `sample_rate` | No | Rate of a bare waveform |
 
 Markup stripped from an expected line: `<d>[Language] ...</d>`, `<scenetrans>`, `<cutoff>`, `[unclear]`, speaker IDs `(S1)` and `(S1,S2)`, and any other `<tag>` or `[tag]`. A plain parenthetical stays dialogue. The words of the stripped tokens (`cutoff`, `unclear`, `english`, `s1`) are what `tag_spoken` listens for; a tag word heard is reported there and left out of the line's similarity, so it is never a `line_mismatch` too.
 
@@ -3455,7 +3354,7 @@ Markup stripped from an expected line: `<d>[Language] ...</d>`, `<scenetrans>`,
 
 Every finding is severity `warn`; `at.shot` is set whenever the shot is known. A rule that does not apply to the call is listed in `rules_skipped` with its reason: `speech_in_silent_shot` and `unscripted_speech` are skipped when the shots carry neither a sample span nor a frame span the take's frame rate can place, or when no line names a shot (no shot is known to be meant silent). When no shots are known at all (no `shots`, none carried by the take, no manifest beside the file) and the lines name at most one shot, the whole file is checked as one implicit shot: `unscripted_speech` runs (a line spoken twice, an ad-lib) and `speech_in_silent_shot` is skipped; `line_clipped_at_end` looks at the file's end only. Lines naming two or more shots with none placed skip both. With shots known there is no minimum word count - one unmatched word inside a named shot fires it.
 
-The thresholds are module constants in `dw/tasks/script_check.py` (the guard's `GUARD_FLOOR_DBFS` and `GUARD_WINDOW_SECONDS` in its sibling `dw/tasks/script_take.py`, beside `guard_words`), deliberately not in `RULES` (`dw/assessment_rules.py`): every rule there must fire on a synthetic file that holds no script. The answer echoes them under `thresholds`.
+The thresholds are module constants in `dw/tasks/script_findings.py` (the repetition guard's `REPEAT_*` in `dw/script_lines.py`; `GUARD_FLOOR_DBFS` and `GUARD_WINDOW_SECONDS` in `dw/tasks/script_take.py`), re-exported by `dw/tasks/script_check.py`. They are not in `RULES` (`dw/assessment_rules.py`), whose rules must fire on a file that holds no script. The answer echoes them under `thresholds`.
 
 | Constant | Value | What crossing it does |
 | -------- | ----- | --------------------- |
@@ -3469,7 +3368,7 @@ The thresholds are module constants in `dw/tasks/script_check.py` (the guard's `
 | `REPEAT_RUN_MIN` | 6 | Heard words repeating the same unit this many times over, back to back, are a repetition loop: every chunk holding one is discarded (`reason: "repetition"`), however loud |
 | `REPEAT_MAX_PERIOD` | 6 words | The longest repeating unit a loop is looked for in - `pre pre pre` has period 1, `thank you thank you` period 2 |
 
-The result: `findings`, `lines[]` (`expected`, `heard`, `similarity`, `start`, `end`, `shot`; with the shot known, `start` and `end` are kept inside it, and the ASR's own times, when they fall outside - a word stretched across the seam over speech Whisper dropped - are kept as `asr_start` / `asr_end`, #1115), `discarded[]` (`text`, `start`, `end`, `level_dbfs`, `reason` - `below_floor`, `repeated_span` (a chunk repeating the previous one's exact span) or `repetition`, `count` - a repetition loop is one entry spanning its chunks), `unmatched[]` (heard words aligned to no line), `transcript`, `model_name`, `rules_applied`, `rules_skipped[]` (`rule`, `reason`, and `line` for a per-line skip) and `thresholds`. A line's `shot` is the shot it names, else the shot its heard words overlap most (null when none is known). `shots` is the map as placed - `[{name, start, end}]` in seconds on the take, or null when none is known - and `shots_source` says where it came from: `argument`, `artifact`, `manifest` or `none`.
+The result: `findings`, `lines[]` (`expected`, `heard`, `similarity`, `start`, `end`, `shot`; with the shot known, `start` and `end` are kept inside it, and the ASR's own times, when they fall outside - a word stretched across the seam over speech Whisper dropped - are kept as `asr_start` / `asr_end`, #1115), `discarded[]` (`text`, `start`, `end`, `level_dbfs`, `reason` - `below_floor`, `repeated_span` (a chunk repeating the previous one's exact span) or `repetition`, `count` - a repetition loop is one entry spanning its chunks), `unmatched[]` (heard words aligned to no line), `transcript`, `model_name`, `rules_applied`, `rules_skipped[]` (`rule`, `reason`, and `line` for a per-line skip), `thresholds`, and `transcription_pass` (`whole_cut` or `per_shot`; with `per_shot`, a `transcription_note`). A line's `shot` is the shot it names, else the shot its heard words overlap most (null when none is known). `shots` is the map as placed - `[{name, start, end}]` in seconds on the take, or null when none is known - and `shots_source` says where it came from: `argument`, `artifact`, `manifest` or `none`.
 
 How the alignment reads a take:
 
@@ -3481,11 +3380,11 @@ How the alignment reads a take:
 
 A malformed `lines` - not a list, an entry without text, an unknown key in a line object - is refused at validation, at `steps[i].task.arguments.lines`, as is a `similarity` outside 0..1. A line naming a shot the map lacks is refused, listing the map's shots, and so is a line naming a shot the map holds twice; a literal `shots` and literal lines are checked against each other at validation, and a `shots` or `lines` that is a reference is checked when the step runs.
 
-**Example:** [check-script.json](../workflows/templates/check-script.json) — Check a take against its script and save the answer as JSON. Its defaults are `openai/whisper-large-v3-turbo` at `similarity` 0.6, not the task's `openai/whisper-base` at 0.85: on the two-model measurement (#609, job `af5de241ca83`), turbo scored the take's correct lines 0.71 to 1.00 and its wrong or missing lines 0.00, where base scored correct lines as low as 0.43 on mishearings (`O'Connor`, `415`). Turbo writes numbers as words (`four fifteen`), so a script with digits costs similarity under it.
+**Example:** [check-script.json](../workflows/templates/check-script.json) — check a take against its script and save the answer as JSON. Its defaults are `openai/whisper-large-v3-turbo` at `similarity` 0.6, not the task's `openai/whisper-base` at 0.85: turbo scored correct lines 0.71 to 1.00 and wrong or missing ones 0.00, where base scored correct lines as low as 0.43 on mishearings (#609). Turbo writes numbers as words (`four fifteen`), so a script with digits costs similarity.
 
 ## Frame Interpolation
 
-Increase video frame rate using RIFE (Real-Time Intermediate Flow Estimation). Takes a video and inserts intermediate frames between each pair. The result is one video artifact without a soundtrack - the frame count changed, so [`pair_audio`](#pair_audio) is how the original track comes back. [interpolate-frames.json](../workflows/templates/interpolate-frames.json) shows the interpolation itself.
+Raise a video's frame rate with RIFE, inserting frames between each pair. The result is one video without a soundtrack; [`pair_audio`](#pair_audio) puts the original track back. [interpolate-frames.json](../workflows/templates/interpolate-frames.json) is the template.
 
 ```json
 {
@@ -3507,7 +3406,7 @@ Increase video frame rate using RIFE (Real-Time Intermediate Flow Estimation). T
 | `model_name` | No | HuggingFace repo with RIFE v4.13 weights (default: `imaginairy/rife-interpolation`) |
 | `filename` | No | Weights filename within the repo (default: `rife-flownet-4.13.2.safetensors`) |
 
-Uses vendored IFNet v4.13 architecture. Weights are downloaded from HuggingFace Hub on first use. A validate's `downloads_required` lists the `model_name` / `filename` file when it is not cached.
+The IFNet v4.13 architecture is vendored; the weights download on first use. A validate's `downloads_required` lists the `model_name` / `filename` file when it is not cached.
 
 **Example:** [interpolate-frames.json](../workflows/templates/interpolate-frames.json) — Generate video with Mochi, then 2x interpolate from 30fps to 60fps.
 
@@ -3527,9 +3426,9 @@ Embed generation parameters in saved images. Enable by setting `embed_metadata:
 | Format | Storage | Notes |
 | ------ | ------- | ----- |
 | PNG | Text chunk (`parameters` key) | Always available |
-| JPEG/WebP | EXIF UserComment | Requires `pip install piexif` |
+| JPEG/WebP | EXIF UserComment | Through piexif, a core dependency |
 
-Metadata includes step name, model name, and generation arguments (prompt, steps, guidance scale, etc.) as JSON.
+The metadata is JSON: the step name, the whole workflow and its seed, and the step's model name (a pipeline step) or `task_command` (a task step) with its arguments.
 
 **Example:** [embed-metadata.json](../workflows/templates/embed-metadata.json) — Generate with Flux and embed parameters in PNG.
 
@@ -3558,7 +3457,7 @@ The QR code is generated then resampled to `max(height, width)`, aligned to the
 
 ## Chat/Dict Plumbing
 
-These small tasks glue together multi-step pipelines that mix raw `transformers` components with task steps — for the cases `text_generation` does not cover.
+Glue for workflows that mix raw `transformers` components with task steps, where `text_generation` does not cover the case.
 
 ### format_chat_message
 
@@ -3643,11 +3542,9 @@ Canny edge detection followed by ControlNet generation:
             "task": {
                 "command": "canny",
                 "arguments": {
-                    "image": {
-                        "location": "photo.jpg",
-                        "low_threshold": 50,
-                        "high_threshold": 200
-                    }
+                    "image": { "location": "asset:photo.jpg" },
+                    "low_threshold": 50,
+                    "high_threshold": 200
                 }
             },
             "result": { "content_type": "image/jpeg" }
@@ -3677,23 +3574,20 @@ Canny edge detection followed by ControlNet generation:
 
 ## Examples
 
-- [controlnet.json](../workflows/templates/controlnet.json) — Canny edge ControlNet
-- [controlnet.json](../workflows/templates/controlnet.json) — Depth-guided generation
+- [controlnet.json](../workflows/templates/controlnet.json) — Canny edge ControlNet (swap the task for another preprocessor)
 - [qr-code.json](../workflows/templates/qr-code.json) — QR code with artistic ControlNet
 - [upscale-spandrel.json](../workflows/templates/upscale-spandrel.json) — Spandrel 4x upscale of an existing image
 - [restore-faces.json](../workflows/templates/restore-faces.json) — Generate portrait + GFPGAN face restoration
 - [segment.json](../workflows/templates/segment.json) — Text-prompted object segmentation
 - [segment-and-inpaint.json](../workflows/templates/segment-and-inpaint.json) — Segment + inpaint
 - [image-to-text.json](../workflows/templates/image-to-text.json) — image captioning with the SmolVLM default
-- [image-to-text.json](../workflows/templates/image-to-text.json) — VLM captioning with a specific question
 - [describe-and-regenerate.json](../workflows/templates/describe-and-regenerate.json) — Describe, expand, then regenerate
 - [interpolate-frames.json](../workflows/templates/interpolate-frames.json) — RIFE frame interpolation
 - [embed-metadata.json](../workflows/templates/embed-metadata.json) — Embed generation parameters in PNG
 - [expand-prompt.json](../workflows/templates/expand-prompt.json) — LLM prompt expansion
-- [expand-prompt.json](../workflows/templates/expand-prompt.json) — Expand prompt + generate image
-- [upscale-spandrel.json](../workflows/templates/upscale-spandrel.json) — Spandrel upscale of an existing image
 - [upscale-diffusion.json](../workflows/templates/upscale-diffusion.json) — Diffusion upscale of an existing image
 - [audio-trim-fade.json](../workflows/templates/audio-trim-fade.json) — Trim a generated track and fade its tail
+- [best-of-n-to-video.json](../workflows/templates/best-of-n-to-video.json) — `judge` and `select` pick the best of several stills
 - [relevel-clip.json](../workflows/templates/relevel-clip.json) — Fix a clipping soundtrack on a rendered clip without re-rendering
 - [generate-speech.json](../workflows/templates/generate-speech.json) — Speak a line with a local text-to-speech model
 - [voice-timbre-reference.json](../workflows/templates/minimax/voice-timbre-reference.json) — Generate a voice and condition H3's `<Audio 1>` on it
diff --git a/docs/TESTING.md b/docs/TESTING.md
index ca0e6bce..c8328139 100644
--- a/docs/TESTING.md
+++ b/docs/TESTING.md
@@ -13,9 +13,9 @@ pytest tests/test_security.py -v
 # Debugging with -s or --pdb needs one process
 pytest tests/ -n0 -s
 
-# The real-model tests (an accelerator, plus SD 1.5 and facebook/mms-tts-eng,
-# which download on first run) are the release gate, and are
-# excluded by default; one process, since each test holds a model on the device
+# The real-model tests: the release gate, excluded by default (-m "not integration"
+# in pytest.ini). They need CUDA or MPS and download SD 1.5 and facebook/mms-tts-eng
+# on first run; one process, since each test holds a model on the device
 pytest tests/ -m integration -n0
 
 # Match pattern
@@ -24,14 +24,15 @@ pytest tests/ -k "variables" -v
 # Stop on first failure
 pytest tests/ -x
 
-# With coverage
+# With coverage; open htmlcov/index.html
 pytest tests/ --cov=dw --cov-report=html
-# Open htmlcov/index.html to view report
 ```
 
+`scripts/preflight.sh` runs every local pre-merge check and lists the ones that failed: `ruff format` and `ruff check --fix` (both rewrite files in place), pytest, the integration tests, the architecture ratchet (`scripts/arch_metrics.py --check docs/stabilization/baseline.json`) and the UI's `npm run preflight`.
+
 ## Test Files
 
-Around 120 files and 3,600+ tests as of this writing (`pytest tests/ --collect-only -q` for the current count). The table below maps the core areas; newer features carry their own `test_<feature>.py` alongside them (server: `test_server.py`, events/cancellation: `test_events.py`, `test_worker_execute.py`, introspection: `test_introspection.py`, hub cache: `test_hub_cache.py`, chaining: `test_chain.py`).
+Over 300 files (`pytest tests/ --collect-only -q` for the current count). The table maps the core areas; a feature carries its own `test_<feature>.py`. `tests/README.md` lists more.
 
 | File | Area |
 | ---- | ---- |
@@ -45,10 +46,12 @@ Around 120 files and 3,600+ tests as of this writing (`pytest tests/ --collect-o
 | test_gather.py | Resource gathering tasks |
 | test_type_helpers.py | Dynamic type loading |
 | test_integration.py | End-to-end workflow tests |
+| test_real_models.py | Real-model release gate (`-m integration`) |
 | test_task.py | Task dispatch |
 | test_schema.py | JSON schema validation |
-| test_worker.py | Worker subprocess |
+| test_worker.py, test_worker_execute.py | Worker subprocess |
 | test_worker_manager.py | Worker process lifecycle management |
+| test_server.py, test_events.py | HTTP server, job events and cancellation |
 | test_pipeline_caching.py, test_pipeline_components.py, test_modular_pipeline.py | Pipeline caching, component discovery, `load_components` |
 | test_device.py, test_device_helpers.py | Device selection, shared device/dtype helpers |
 | test_image_utils.py, test_resize_bucket.py, test_strip_exif_and_watermark.py, test_tensor_image.py, test_list_images.py | Image processing task commands |
@@ -58,16 +61,46 @@ Around 120 files and 3,600+ tests as of this writing (`pytest tests/ --collect-o
 | test_prompt_weighting.py | Prompt weighting device handling |
 | test_argument_updates.py | Cached pipelines pick up fresh arguments across runs |
 | test_examples.py | Validates every workflow in `workflows/` against the schema |
+| test_introspection.py, test_hub_cache.py, test_chain.py | Introspection, Hub cache, chaining |
 | test_mcp_client.py, test_mcp_catalog.py, test_mcp_authoring.py, test_mcp_diagnose.py, test_mcp_media.py, test_mcp_server.py | MCP tool surface |
+| test_architecture_map.py | Every path, name and test `docs/ARCHITECTURE.md` names exists |
 
-The MCP tests fake the REST API with `httpx.MockTransport` — they never
-start a `dw.serve` process and never touch a GPU. `test_mcp_server.py`
-(which builds a real `MCPServer` and calls tools through it) skips with
-`pytest.importorskip("mcp", ...)` when the `mcp` extra is not installed.
+The MCP tests fake the REST API with `httpx.MockTransport`; they never start `dw.serve` or touch a GPU. `test_mcp_server.py` builds a real `MCPServer` and skips (`pytest.importorskip("mcp", ...)`) when the `mcp` extra is not installed.
 
 ## conftest.py
 
-An autouse `_clear_task_model_cache` fixture clears `dw.tasks.model_cache` before and after every test, so a cache hit in one test can't starve a later test's mocked loader of the call it expects. Test-specific fixtures (`test_data_dir`, `temp_output_dir`, `temp_image`, `valid_workflow_json`, etc.) are also defined here — see `tests/README.md` for the list.
+`tests/conftest.py` sets `DW_STRICT_RESPONSES=1`, so a key a server route emits that its response model does not declare is a 500. Its autouse fixtures make each test independent of the machine and of earlier tests:
+
+- `_trust_workflows_by_default` sets `DW_TRUST_WORKFLOWS=1`; `tests/test_workflow_trust.py` unsets it to test the trust gate.
+- `_isolate_settings_dir` points `DIFFUSERS_HELPER_ROOT` at the test's `tmp_path`.
+- `_isolate_device_capacity` checks a `vram_estimate` against the catalog's measured cards, never the local accelerator.
+- `_clear_task_model_cache` and `_clear_step_cache_after_test` empty the process-wide task model cache and step cache, so a cache hit in one test cannot starve a later test's mocked loader.
+
+Shared fixtures (`test_data_dir`, `temp_output_dir`, `temp_image`, `valid_workflow_json`, and others) are defined there too.
+
+## UI Tests
+
+From `ui/`:
+
+```bash
+npm run check          # svelte-check + tsc
+npm run lint           # eslint
+npm test               # vitest unit tests
+npx playwright test    # e2e (npm run e2e)
+npm run preflight      # check, lint, format, ratchet, build, test, e2e
+```
+
+`ui/playwright.config.ts` starts its own server (`ui/e2e/serve_fixture.py --port 8971`, against a scratch copy of the example workflows and prompts), so do not run `dw.serve` on that port. It runs on `DW_E2E_PYTHON` when set, else `venv/bin/python` or `.venv/bin/python`, else `python3`. It uses one worker because the fixture server's first import of a diffusers pipeline module is not concurrency-safe. The e2e specs serve the built UI, so run `npm run build` first.
+
+## CI
+
+`.github/workflows/ci.yml` runs on pushes to `master` and `develop`, on `v<semver>` tags and on pull requests:
+
+- **backend** (Python 3.12, CPU torch): the architecture ratchet, `ruff format --check`, `ruff check`, and `pytest -q`. Each step runs when the install succeeded, so one failure does not hide another.
+- **ui**: the response contract (`npm run gen:api` must leave `src/lib/generated` unchanged), prettier, lint, the UI ratchet, type check, unit tests and build.
+- **e2e**: Playwright against a real `dw.serve`, on pull requests into `develop` or `master` (except the `develop` to `master` release PR) and on pushes to `develop`.
+
+The `wheel`, `release`, `pypi` and `docker` jobs run on tags; see [RELEASING.md](RELEASING.md).
 
 ## Quick Validation
 
diff --git a/docs/WORKER_GUIDE.md b/docs/WORKER_GUIDE.md
index 48f9b846..b7619d26 100644
--- a/docs/WORKER_GUIDE.md
+++ b/docs/WORKER_GUIDE.md
@@ -2,59 +2,68 @@
 
 ## Overview
 
-`dw.serve` runs a persistent worker subprocess (`dw/worker.py`, managed by
-`dw/worker_manager.py`) to keep models loaded in GPU memory across jobs.
-After the first job loads a model, later jobs against the same workflow skip
-the loading step entirely.
-
-The worker uses the `spawn` multiprocessing start method, required for CUDA
-and MPS compatibility. This is configured automatically.
+`dw.serve` runs each job in a persistent worker subprocess (`dw/worker.py`,
+managed by `dw/worker_manager.py`), one per `--devices` card, so a model
+loaded by one job stays in memory for the next. Workers use the `spawn`
+start method (required for CUDA and MPS), start on the first job rather
+than with the server, and inherit the server's environment. A change to
+engine code needs a server restart before a job sees it.
 
 ## How It Works
 
-`JobManager` sends commands to the worker and reads results over
-`multiprocessing.Queue`s.
-
-- **First run of a workflow**: Worker starts and loads the model
-- **Subsequent runs**: Worker reuses cached models
-- **Workflow file edited**: A job runs the definition the server checked when it was submitted, so an edit reaches only jobs submitted after it. Pipelines are cached by what they load, so only a pipeline whose definition changed reloads
-- **A different workflow queued**: The worker switches in place - it frees the old workflow's models before loading the new one
-- **`POST /api/memory/clear`**: Frees GPU memory, models reload on next run
-- **Server shutdown**: Worker shuts down gracefully
+`JobManager` (`dw/server/jobs.py`) sends commands to a worker and reads its
+replies over a pair of `multiprocessing.Queue`s.
+
+- **First job on a card**: the worker starts and loads the model.
+- **Later jobs**: cached pipelines are reused.
+- **Workflow file edited**: a job runs the definition the server checked at
+  submit, so an edit reaches only jobs submitted after it. Pipelines are
+  cached by what they load, so only a pipeline whose definition changed
+  reloads.
+- **A different workflow**: the worker prepares the incoming definition and
+  frees only the models it does not load; a pipeline both workflows load
+  (same weights identity) stays warm. The step cache and the task model
+  cache are emptied, and if the incoming definition cannot be prepared
+  everything is released.
+- **`POST /api/memory/clear`** (MCP `clear_memory`): drops every pipeline
+  and the step cache on each idle card; models reload on the next job.
+- **diffusers updated from the Models page**: an idle worker is shut down,
+  so the next job imports the new version.
+- **Server shutdown**: workers shut down gracefully (10 s, then terminate).
+
+How jobs are assigned to cards, the host-RAM hold and the OOM ranking are in
+[SERVER.md](SERVER.md#jobs-api).
 
 ## Memory Management
 
-The worker cleans up automatically between runs (garbage collection + GPU
-cache clearing). If memory grows unexpectedly, `GET /api/memory` reports the
-current reading and `POST /api/memory/clear` resets it.
-
-Switching to a different workflow releases only the models the next workflow
-does not load: the worker prepares the incoming definition, and a pipeline
-both workflows load (same weights identity) stays warm. The step cache and
-the task model cache are still emptied, and if the incoming definition cannot
-be prepared everything is released. `clear_memory` (`POST /api/memory/clear`)
-remains the way to empty the card by hand.
+The worker collects garbage and clears the device cache between runs.
+`GET /api/memory` reports a card's reading and `POST /api/memory/clear`
+empties it; both take an optional `?device=`.
 
 ## Cold Loads
 
-With `DW_QUANTIZED_CACHE=1`, the first cold load of an SDNQ on-the-fly-quantized
-component pays the quantize and a save to disk. Later cold loads - a new worker,
-after a restart or after eviction - read the cached entry instead. See
-[QUANTIZATION.md](QUANTIZATION.md#cached-on-the-fly-dw_quantized_cache).
+With `DW_QUANTIZED_CACHE=1`, the first cold load of an SDNQ
+on-the-fly-quantized component pays the quantize and a save to disk; later
+cold loads (a new worker, after a restart or a memory clear) read the cached
+entry. See [QUANTIZATION.md](QUANTIZATION.md#cached-on-the-fly-dw_quantized_cache).
 
 ## Troubleshooting
 
-**Worker crashes**: `JobManager` detects it and starts a fresh worker on the
-next job. `WorkerManager.crash_details()` reports why, when the OS can say
-(a signal such as SIGKILL) - see the job's error message.
+**Worker crashes**: only its own job fails, and the next job on that card
+starts a fresh worker with a cold cache. The job's error carries
+`WorkerManager.crash_details()` - the signal (such as SIGKILL) when the OS
+reports one.
 
-**Execution errors**: The worker stays alive (models cached) so the next job
-can run immediately.
+**Execution errors**: the worker stays alive with its models cached, except
+after a CUDA error that poisons the process's context
+(`poisons_cuda_context`, `dw/worker_protocol.py`); then the worker restarts.
 
-**Long runs**: There is no execution timeout - a run waits as long as the
-worker is alive (liveness is polled every second, so a crashed worker is
-noticed immediately). `POST /api/jobs/{id}/cancel` cancels a running job in
-place, keeping models cached.
+**Long runs**: there is no execution timeout. A run waits as long as its
+worker is alive; liveness is polled every second, so a dead worker is
+noticed within a second. `POST /api/jobs/{id}/cancel` cancels in place and
+keeps models cached; during a model load or a task step it takes effect
+once that finishes (a `cancel_pending` event says so).
 
-**GPU out of memory**: Use `POST /api/memory/clear`, reduce model size, or
-check for other processes using the GPU.
+**GPU out of memory**: `POST /api/memory/clear`, a smaller or quantized
+model, offloading ([ACCELERATION.md](ACCELERATION.md)), or check for other
+processes using the GPU.
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index d8fba20f..5eb6ca52 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -10,15 +10,12 @@ sub-workflows, `pipeline_reference`, typed references), or a reference
 convention (`prompt:`, `previous_result:`). These are what to read and copy.
 Where several checkpoints run the same pattern through the same pipeline class,
 one template carries them all and its `description` spells out the per-checkpoint
-argument sets, so the variations travel with the file rather than in a document
-that drifts from it. The `templates/ltx2/` and `templates/minimax/` subfolders
-each hold a family whose members build on one baseline.
+argument sets. The `templates/ltx2/` and `templates/minimax/` subfolders each
+hold a family whose members build on one baseline.
 
 **`workflows/models/`** records a hardware fact: the quantization, offloading and
-component placement that make one checkpoint fit a real card. That is knowledge
-you cannot re-derive from a template, so it is kept runnable - but nobody learns
-a pattern from the fifth one, so these stay out of the way. Each carries a
-`configures` naming the template it is an instance of:
+component placement that make one checkpoint fit a real card. Each is runnable
+and carries a `configures` naming the template it is an instance of:
 
 ```json
 {
@@ -29,9 +26,6 @@ a pattern from the fifth one, so these stay out of the way. Each carries a
 }
 ```
 
-The distinction exists because a catalog entry that cannot say which of the two
-it is leaves every reader - and every agent - to guess from the filename.
-
 ## Structure
 
 Every workflow is a JSON file with an `id`, optional `variables`, and a list of `steps`.
@@ -52,13 +46,15 @@ and the server check a workflow against. A running server also serves it at
 }
 ```
 
-**Variables** define defaults that can be overridden from the command line:
+**Variables** define defaults that a caller overrides, from the command line or
+as `run_workflow`'s `arguments`:
 
 ```bash
 python -m dw.run my_workflow.json prompt="a cat" steps=50
 ```
 
-Variable names must be alphanumeric with underscores or hyphens.
+A variable name matches `^[a-zA-Z_][a-zA-Z0-9_-]*$`: a letter or underscore, then
+letters, digits, underscores or hyphens.
 
 ## Step Types
 
@@ -170,22 +166,9 @@ Invoke another workflow file:
 }
 ```
 
-`path` is read the way `run_workflow`'s `workflow_path` is: a catalog name as
-`list_workflows` reports it (`templates/minimax/reference-to-video`), with or
-without `.json`; a path relative to the file that names it (`../models/x.json`);
-or `builtin:name.json` for the packaged fragments in `dw/workflows/`. A name
-resolves beside the referencing file first, then against the run's own
-`workflows/` directory, then against each read-only source the server lists -
-so a stored template can be composed without copying it into the workspace. A
-path that lands outside every source is refused, and one that resolves nowhere
-is a validation error rather than a run that fails on its first step.
-
-When the composing step declares a `result`, that is where the composed output
-is written, once: the child's own last step does not save it a second time
-under its own name. A composing step that declares no `result` (or one with no
-`content_type`) leaves the saving to the child, as before. The child's other
-steps write into the same run directory, with the composing step's name
-leading their file names.
+`arguments` become the child workflow's variables. How `path` resolves, where
+the child's output is saved, and what validation checks are in
+[Composing a stored workflow](#composing-a-stored-workflow).
 
 ## Cross-Step Data Flow
 
@@ -197,11 +180,12 @@ Reference workflow variables with `variable:name`:
 "prompt": "variable:prompt"
 ```
 
-A variable's declared value is both its default and its type — a value passed in is
-converted to the type of the default, so declaring `25` and `"25"` are different things
-(see the schema note under Variables). Declaring `null` opts out of that: the variable
-becomes optional and untyped, taking whatever it is given and staying `null` when it is
-given nothing.
+A variable's declared value is both its default and its type: a value passed in is
+converted to the type of the default, so declaring `25` and `"25"` are different
+things. Declaring `null` makes the variable optional and untyped: it takes whatever
+it is given and stays `null` when given nothing. That is how a workflow exposes an
+argument a caller *may* pass. A caller can set only variables the workflow declares,
+so an optional argument still has to be declared.
 
 ```json
 "variables": { "image": null }
@@ -210,11 +194,6 @@ given nothing.
 A value the library already declares does not need a variable at all - see
 [Constant References](#constant-references).
 
-This is how a workflow exposes an argument a caller *may* pass without inventing a
-sentinel for its absence — a sub-workflow that behaves differently when handed an image,
-say. A caller can only set variables the workflow declares, so an optional argument still
-has to be declared to be passable.
-
 ### Previous Result References
 
 Pass output from one step to another with `previous_result:step_name`:
@@ -239,12 +218,11 @@ Pass output from one step to another with `previous_result:step_name`:
 }
 ```
 
-A reference is resolved wherever it appears in the arguments, not only at the top of
-them - an argument holding a list or a nested object can reference a step too, which is
-what lets a constructed object be
-[built from an earlier step](#objects-built-from-an-earlier-step).
-
-Multiple `previous_result` references create a **cartesian product**: if step A produces 4 images and step B produces 3 masks, a step referencing both will run 12 times.
+A reference resolves anywhere in the arguments, including inside a list or nested
+object, which is what lets a constructed object be
+[built from an earlier step](#objects-built-from-an-earlier-step). Several
+`previous_result` references on one step multiply into a cartesian product (see
+[Several `previous_result` references multiply](#several-previous_result-references-multiply)).
 
 A step whose result is a dict (a task returning several named outputs, or a pipeline
 step that returns something like `inverted_latents`) can be referenced property by
@@ -295,108 +273,94 @@ fails the run - unless it arrives in the `arguments` passed to
 `validate_workflow`, which checks an `asset:`, `prompt:` or `output:` there
 for existence.
 
-- `variable:` — `variable:name` is the workflow's own `variables` entry,
-  overridden by the caller's `arguments`. A variable declared `null` is optional and untyped.
-  Schema validation runs before substitution, so a default must already be the
-  JSON type the field expects: `25`, not `"25"`.
-- `previous_result:` — `previous_result:step_name` is the outputs of an earlier
-  step, named by that step's `name`. It iterates; see the cartesian rule
-  below. Validation checks it: a literal reference naming no earlier step is
-  an error with the JSON path it sits at, rather than a run-time failure
-  reached after everything before it has generated. A `.field` suffix
-  (`previous_result:invert.inverted_latents`) picks one field of a result that
-  is a dict, or a data attribute of a result object.
-- `constant:` — `constant:module.path.NAME` is a value declared in Python,
+- `variable:` - `variable:name` is the workflow's own `variables` entry,
+  overridden by the caller's `arguments`. A variable declared `null` is optional
+  and untyped. Schema validation runs before substitution, so a default must
+  already be the JSON type the field expects: `25`, not `"25"`. Once a workflow
+  has a `variables` block, a reference to a variable it does not declare is a
+  validation error (with no block, the string is passed through unresolved).
+- `previous_result:` - `previous_result:step_name` is the outputs of an earlier
+  step, named by that step's `name`. It iterates (see the cartesian rule below).
+  A literal reference naming no earlier step is a validation error at its JSON
+  path. A `.field` suffix (`previous_result:invert.inverted_latents`) picks one
+  field of a result that is a dict, or a data attribute of a result object.
+- `constant:` - `constant:module.path.NAME` is a value declared in Python,
   read by import rather than copied into JSON. Anything callable is refused.
-- `asset:` — `asset:name` is a file in the asset library. Rooted at the
-  library and confined to it, never resolved relative to the workflow file; a path that
-  escapes the library is rejected. `upload_asset` returns one of these names.
-  One name, three places it can sit: a bare string (`"image": "asset:x.png"`),
-  an element of a list, or the `location` of a media dict
-  (`{"location": "asset:x.png"}`, with or without `media_type`) - all resolve
-  to the same file. When the file is on neither the MCP session's machine nor
-  reachable as `content=` (over 4MB, or the agent has no filesystem access to
-  read it into the call), curl it straight into the library the same way the
-  web UI's file picker does — `POST /api/uploads`, body is the raw bytes, up to
-  200MB:
+- `asset:` - `asset:name` is a file in the asset library, confined to it and
+  never resolved relative to the workflow file. `upload_asset` returns one of
+  these names. It may be a bare string (`"image": "asset:x.png"`), a list
+  element, or the `location` of a media dict (`{"location": "asset:x.png"}`,
+  with or without `media_type`). A file `upload_asset` cannot send (over 4MB as
+  `content=`, or not readable from the MCP session's machine) goes straight to
+  `POST /api/uploads`, the route the web UI's file picker uses: the body is the
+  raw bytes, up to 200MB, and the reply (201) carries `reference`, the `asset:`
+  name to use. Send the bearer token when the server requires one:
   ```
   curl -H "Authorization: Bearer $DW_API_TOKEN" --data-binary @portrait.jpg \
     "http://<host>:8765/api/uploads?filename=portrait.jpg&asset_name=cast/portrait.jpg&workspace=<ws>"
   ```
-  It answers 201 with `reference` - the `asset:` reference to use in a workflow
-  argument (the bearer token only when the server requires one). The same
-  route is also how a file assembled entirely on the client - a finished cut
-  stitched locally rather than by a workflow step - gets onto the server at
-  all: it never lands in the gallery and `export_job` has no run to bundle it
-  from, so uploading it as an asset is the only way to hand it back to the
-  engine or to a teammate reading the workspace.
-- `output:` — `output:<workflow identity>/<run id>/<file>` is a file an earlier
-  run wrote, under the output root and confined to it. `latest` in the run-id position
-  picks the newest run that holds that file; `v<N>` picks the run the gallery labels
-  `v<N>` (`list_gallery`'s `version`), and only that run. A run id is not stable against
-  pruning: to depend on a generated file, promote it with `keep_output` and
-  reference the `asset:` name instead.
-- `prompt:` — `prompt:name` or `prompt:folder/name` is a stored prompt's
-  `text`, rooted at the prompt library. That text may not itself begin with any of these
-  prefixes; the engine rejects such a prompt rather than resolving twice.
-  The library is also the worked-example shelf: a template's default prompt
-  is usually a `prompt:` reference, and the text behind it is a caption
-  written to whatever spec that model was trained on. Before writing a
-  prompt for a family, read the one that is already there —
+  The same route is the only way to hand the engine a file made on the client,
+  such as a cut stitched locally: it has no run for `output:` or `export_job`
+  to name.
+- `output:` - `output:<workflow identity>/<run id>/<file>` is a file an earlier
+  run wrote, confined to the output root. `latest` in the run-id position picks
+  the newest run that holds that file; `v<N>` picks the run the gallery labels
+  `v<N>` (`list_gallery`'s `version`), and only that run. A run id is not stable
+  against pruning: to depend on a generated file, promote it with `keep_output`
+  and reference the `asset:` name instead.
+- `prompt:` - `prompt:name` or `prompt:folder/name` is a stored prompt's
+  `text`, rooted at the prompt library. That text may not itself begin with any
+  of these prefixes. A template's default prompt is usually a `prompt:`
+  reference to a caption written to the spec its model was trained on, so read
+  the existing one before writing a prompt for a family:
   `list_prompts(intended_model="ltx-2.5")` for the shelf,
-  `get_prompt("ltx2/fox_dawn_choir")` for a body. The listing leaves the
-  bodies out by default and reports each one's `text_chars`; asking for all
-  of them at once is more than a client will accept.
-- `item:` — only inside a step that carries `for_each`: `item:` is the
-  entry the member was made for, `item:field` one field of an object entry,
-  spliced in whole whatever its type — a string, a number, a list of
-  references. See "One step per entry" below.
-- `gather:` — `gather:shot` is the result of *every* member of the
-  `for_each` step `shot`, in list order, as one list. Inside a list it splices
-  into it. It is how a step downstream of a fan-out reads the whole group;
-  `previous_result:shot` naming a `for_each` step is an error that says so.
-
-After a long inline run that is worth keeping, `get_job_workflow(job_id)`
-returns the realized workflow — the definition with the arguments, seed and
-prompts of that run pinned into it — and `save_workflow` gives it a name, so
-the next run is by name rather than by pasting JSON again. `export_job(job_id)`
-bundles the whole run (workflow, manifest, job row, the media on both sides)
-into a directory on the server plus a zip URL, for a run worth committing or
-handing to someone else.
-
-A reference is resolved wherever it appears in the arguments, including inside
-a nested object or list — not only at the top level. It is always the *whole*
-value: `"variable:base_prompt"` resolves, `"variable:base_prompt, in fog"` asks
-for a variable named `base_prompt, in fog` and fails the run. Nothing is
-interpolated around a reference. To vary a fixed prompt across steps, write
-each full prompt out, or put the shared text in a variable and let a step's
-argument override it whole. A `variable:` reference that names nothing the
-workflow declares is a validation error, not a warning: once a `variables`
-block exists the engine refuses an undeclared reference, so it is a run that
-cannot start.
-
-When several steps share a block of text — a character's description and voice
-repeated in every shot of a dialogue short — the answer is composition rather
-than interpolation: write the shared text once as a variable and assemble each
-step's prompt with a [`compose_text`](TASKS.md#compose_text) task, whose parts
-are whole references joined in order. The shot then references the composed
-result (`"prompt": "previous_result:shot_1_prompt"`), so changing the voice
-changes it in every shot instead of in however many copies were made by hand.
+  `get_prompt("ltx2/fox_dawn_choir")` for a body. The listing leaves the bodies
+  out by default and reports each one's `text_chars`; asking for all of them at
+  once is more than a client will accept.
+- `item:` - only inside a step that carries `for_each`: `item:` is the entry
+  the member was made for, `item:field` one field of an object entry, spliced in
+  whole whatever its type. See "One step per entry" below.
+- `gather:` - `gather:shot` is the result of *every* member of the `for_each`
+  step `shot`, in list order, as one list. Inside a list it splices into it.
+  `previous_result:shot` naming a `for_each` step is an error that says to use
+  `gather:`.
+
+A reference is always the *whole* value: `"variable:base_prompt"` resolves,
+`"variable:base_prompt, in fog"` asks for a variable named `base_prompt, in fog`
+and fails. Nothing is interpolated around a reference. When several steps share
+a block of text (a character's description repeated in every shot), write it
+once as a variable and assemble each step's prompt with a
+[`compose_text`](TASKS.md#compose_text) task, whose parts are whole references
+joined in order; the shot then reads `"prompt": "previous_result:shot_1_prompt"`.
+
+After an inline run worth keeping, `get_job_workflow(job_id)` returns the
+realized workflow (the definition with that run's arguments, seed and prompts
+pinned into it), and `save_workflow` gives it a name. `export_job(job_id)`
+bundles the whole run (workflow, manifest, job row, input and output media) into
+a directory on the server plus a zip URL.
 
 ### Types and escaping
 
 Any key ending in `_type` or `_dtype`, or named `dtype`, has its string value
-loaded as a Python object: `"FluxPipeline"` from `diffusers`, a dotted name
-(`"torch.bfloat16"`, `"sdnq.SDNQConfig"`) by full module path. Wrapping a value
-in braces keeps it a plain string — `"{nf4}"` is the string `nf4`. Getting this
-wrong fails at load time, after validation has already passed, so a value that
-is meant as text under one of those keys must be braced.
+loaded as a Python object: a bare name (`"FluxPipeline"`) from `diffusers`, a
+dotted name (`"torch.bfloat16"`, `"sdnq.SDNQConfig"`) by full module path.
+Wrapping a value in braces keeps it a plain string: `"{nf4}"` is the string
+`nf4`. Getting this wrong fails at load time, after validation has already
+passed, so a value meant as text under one of those keys must be braced. Two
+keys are exempt and always stay strings: `content_type` and `offload_type`
+(braces are accepted there too).
+
+On a server without `--trust-workflows`, a dotted name must sit in one of the
+packages `dw/trust.py` allows (`diffusers`, `torch`, `transformers`, `sdnq`,
+`torchao` and the other quantization backends, `dw`), and must resolve to a
+class defined there, or to a `torch.dtype` under a `dtype` key. Anything else is
+refused at load.
 
 ### What a variable is allowed to be
 
-A model's own rule about a value belongs in the workflow, not in engine code
-(CLAUDE.md) and not in a consumer's head. `variable_constraints` declares it
-per variable, in the same field names a chain step's `frame_snap` uses:
+A model's own rule about a value belongs in the workflow, not in engine code.
+`variable_constraints` declares it per variable, in the same field names a chain
+step's `frame_snap` uses:
 
 ```json
 "variable_constraints": {
@@ -419,40 +383,35 @@ it is refused. The bounds are checked against the value the run will use, so
 they hold for the rounded number: on the rule above `108` is accepted (it
 becomes `124`) and `346` is refused (it would become `362`).
 
-Checked three times, for the reasons the task-argument domains are: in
-`validation_errors`, so `POST /api/validate`, `validate_workflow` and the
-pre-queue check all refuse a bad value at `arguments.<name>` or
-`variables.<name>` for free; at run time before anything loads, which is the
-backstop for a value the static pass cannot see (an inline workflow, a value
-a parent passed down); and in the catalog, where `list_workflows` and
-`get_workflow(variables_only=true)` report the rule beside the default - the
-half that stops the next caller picking a number the model refuses.
+The rule is checked at validation (`POST /api/validate`, `validate_workflow` and
+the pre-queue check refuse a bad value at `arguments.<name>` or
+`variables.<name>`), again at run time before anything loads (for a value the
+static pass cannot see, such as one a parent workflow passed down), and is
+reported in the catalog beside the default by `list_workflows` and
+`get_workflow(variables_only=true)`.
 
-State the rule once. Where a template both declares a constraint and snaps a
-chain, the chain's `frame_snap` names it rather than repeating the numbers:
+Where a template both declares a constraint and snaps a chain, the chain's
+`frame_snap` names the constraint rather than repeating the numbers:
 
 ```json
 "frame_snap": "constraint:num_frames"
 ```
 
-Two limits, both accepted. A constraint cannot express a bound that depends
-on another variable (a maximum that is `fps * seconds` where a template
-exposes `fps`), and it reaches a top-level variable only - not a field inside
-a list entry, so a `for_each` template whose entries each carry their own
-`num_frames` is unconstrained and relies on the run-time check.
+A constraint cannot express a bound that depends on another variable (a maximum
+of `fps * seconds`), and it reaches a top-level variable only, not a field inside
+a list entry: a `for_each` template whose entries carry their own `num_frames`
+relies on the run-time check.
 
 ### A workflow takes only the keys the engine reads
 
 The workflow object itself, `step`, `task`, `workflow`,
-`pipeline_reference` and `result` are closed: a property the engine does not
-read is a validation error naming the object and the key, not a warning.
+`pipeline_reference`, `result`, `chain` and a pipeline's `configuration` are
+closed: a property the engine does not read is a validation error naming the
+object and the key, not a warning. A component's own `configuration` is not
+closed, so a misspelt key there is ignored.
 There is no `when`, no `retry`, no `select` - if a draft reaches for one, the
-shape it wants is a different arrangement of steps, not a flag. The error
-exists because a plausible invented key used to validate cleanly and then do
-nothing, so the expensive work ran with the input silently having had no
-effect - a mistyped `sedd` left the run unseeded while validation advised
-setting a seed, and a mistyped `subfoldr` put the deliverable at the run
-root rather than in `final/`.
+shape it wants is a different arrangement of steps, not a flag. A mistyped
+`sedd` or `subfoldr` is refused rather than silently ignored.
 
 `pipeline` is closed to the same rule with one opening: any key whose value
 is a *component definition* - an object carrying `from_pretrained_arguments` -
@@ -495,18 +454,17 @@ and `from_pretrained_arguments.workflow` - and project it against the
 ceiling a catalog template declared for that same pipeline. Past it, the
 answer stays `valid: true` and carries a warning of kind
 `vram_projection_inherited` that names the source template and the heaviest
-step (for a `for_each`, the heaviest member). It warns rather than
-refuses: your offload and quantization config may differ from the
-template's. Cut frames, size or references until it goes quiet, or declare
-your own `vram_estimate`, which always wins and is judged exactly as a
-template's. Under the ceiling it is not always silent: the template's
-estimate was calibrated on its own offload and quantization, so a workflow
-that sets either differently (no `offload` where the template sets
-`model`, say) gets one warning of kind `vram_config_differs` naming both
-settings and the projection - real usage may be higher. Over the ceiling the
-one `vram_projection_inherited` warning names that difference instead. A pipeline the catalog declares no ceiling for gets no warning -
-silence there is not a clearance - and H3's `t2va` and `ref2va` are
-different identities with different ceilings.
+step (for a `for_each`, the heaviest member). It warns rather than refuses,
+since your offload and quantization may differ from the template's. Cut
+frames, size or references until it goes quiet, or declare your own
+`vram_estimate`, which always wins and is judged exactly as a template's.
+Under the ceiling, a workflow whose offload or quantization differs from the
+template's (no `offload` where the template sets `model`, say) gets one
+`vram_config_differs` warning naming both settings and the projection, since
+real usage may be higher; over the ceiling the `vram_projection_inherited`
+warning names that difference instead. A pipeline the catalog declares no
+ceiling for gets no warning, which is not a clearance. H3's `t2va` and
+`ref2va` are different identities with different ceilings.
 
 The projection is `base_gb`, plus `bytes_per_voxel` times the product of
 the `voxel_variables`, plus `gb_per_reference` per non-null reference, plus
@@ -534,16 +492,15 @@ which `get_server_info` reports as `trust_workflows`.
 ### Several `previous_result` references multiply
 
 When one step carries two or more `previous_result` references, the engine runs
-that step once for every combination — a cartesian product. Four images and
+that step once for every combination, a cartesian product: four images and
 three masks is twelve iterations, not three pairs. Past 10000 combinations the
-run is refused outright.
+run is refused.
 
-This is deliberate: it is how one prompt fans out over a set. The consequence
-is that a *pairing* — shot *i* with speaker *i*, prompt *i* with portrait *i* —
-cannot be expressed with two references on one step. Write it as one step per
-pair, each referencing exactly the two things it pairs, or gather the pairs
-upstream so each is a single result. A step that seems to need a "zip" is the
-signal to restructure the workflow, not to add another reference.
+So a *pairing* (shot *i* with speaker *i*, prompt *i* with portrait *i*) cannot
+be expressed with two references on one step. Write one step per pair, each
+referencing exactly the two things it pairs, gather the pairs upstream so each
+is a single result, or use two `for_each` steps over one list, which pair by
+entry (below).
 
 ### One step per entry: `for_each`
 
@@ -583,21 +540,18 @@ and downstream
 ```
 
 Before the run starts, the engine replaces the `for_each` step with one
-ordinary step per entry, named `shot@wide_open`, `shot@closeup` — the
-entry's `name`, or its index for an entry without one. Those are the names
-the manifest, the job's events and the gallery show, and `@` is reserved
-for them: a hand-written step name may not contain it. An entry's `name`
-must be unique in its list and match `^[a-zA-Z_][a-zA-Z0-9_-]*$`. Give
-entries names: the step cache keys on the member name, so a shot inserted
-in the middle of a named list leaves every other shot cached, while an
-indexed list shifts every later shot onto a different entry and regenerates
-it.
+ordinary step per entry, named `shot@wide_open`, `shot@closeup`: the entry's
+`name`, or its index for an entry without one. Those are the names the
+manifest, the job's events and the gallery show, and `@` is reserved for them:
+a hand-written step name may not contain it. An entry's `name` must be unique
+in its list and match `^[a-zA-Z_][a-zA-Z0-9_-]*$`. Give entries names: the step
+cache keys on the member name, so a shot inserted into a named list leaves every
+other shot cached, while in an indexed list every later shot regenerates.
 
 `item:field` is the whole value of that field, so an entry can carry
-anything a step argument can — including a `references` list whose length
-differs by shot, with `from_previous_result` and `asset:` strings inside
-it. Nothing is interpolated: `"item:prompt"` is the field, `"shot: item:prompt"`
-is a literal string.
+anything a step argument can, including a `references` list whose length
+differs by shot. Nothing is interpolated: `"item:prompt"` is the field,
+`"shot: item:prompt"` is a literal string.
 
 A field an entry may leave out is read with the object form
 `{"item": "singer", "default": "variable:singer_reference"}` (the field's bare
@@ -624,58 +578,42 @@ Two `for_each` steps over the *same* list are paired by key — the entry's
 `name`, or its index for an entry without one: inside `shot@closeup`, a
 reference to another `for_each` step `slice` over the same `shots` list
 resolves to `slice@closeup`. That is how a shot reads the audio
-slice cut for it when slicing and generating are two steps. It is the one
-pairing the engine has; `for_each` runs over exactly one list, and there is
-no zip and no loop index. Same-key pairing is the only way one member reads another
-member of the same list's expansion: a member never reads a sibling entry of
-its own list (below).
-
-That auto-pairing is at the *step* level only, where `from_previous_result`
-(or `previous_result:`) sits directly in the `for_each` step's own
-arguments. Inside an **item** — an entry field the step reads with
-`item:`, such as a `references` entry — nothing pairs it for you, because the
-entry is data the caller wrote and the engine does not know which list it
-came from. Spell the member out: `{"reference_type": "…",
-"from_previous_result": "slice@closeup"}`, not `"slice"`. Getting this wrong
-fails the run with a precise error naming the members that do exist, but the
-guide says it here first.
+slice cut for it when slicing and generating are two steps. It is the only
+pairing the engine has: `for_each` runs over exactly one list, with no zip and
+no loop index.
+
+That pairing applies only where `from_previous_result` (or `previous_result:`)
+sits directly in the `for_each` step's own arguments. Inside an **item** (an
+entry field the step reads with `item:`, such as a `references` entry) nothing
+pairs it, because the entry is data the caller wrote. Spell the member out:
+`{"reference_type": "…", "from_previous_result": "slice@closeup"}`, not
+`"slice"`. Getting it wrong fails the run with an error naming the members that
+exist.
 
 Members of one list can't read each other. A literal reference to another
 entry of the member's own list (`"image": "previous_result:shot@accuse"`
-inside `shot@deflect`, spelled in the template or carried in an entry, with
-either spelling and with or without a `.property`) is refused at validate:
-it would make the list's order part of its meaning, and no derivation — "shot
-2 starts where shot 1 ended," a still pulled from shot 1's last frame with
-`get_last_frame` — can sit between two members of one list. When one entry
-depends on another, use separate runs: generate the one being derived from
-in its own run (with the derivation, such as a `get_last_frame` step, after
-it), then pass that run's file to the next run as an `output:` reference in
+inside `shot@deflect`, in the template or in an entry, either spelling, with or
+without a `.property`) is refused at validate. When one entry depends on
+another (shot 2 starts from shot 1's last frame), use separate runs: generate
+the first in its own run, with any derivation such as a `get_last_frame` step
+after it, then pass that run's file to the next run as an `output:` reference in
 the dependent entry. Another `for_each` step over the same list reading a
 different key (`trim@deflect` reading `shot@accuse`) stays legal: every member
 of `shot` has finished before `trim` starts.
 
 Limits: a list has at most 32 entries, and an empty list is a validation
-error — the step would run nothing. Validation realizes a `constant:`
-default before checking it, so a list defaulted to a constant validates the
-same way it will run. `release_pipeline` on a `for_each`
-step releases after the *last* member. Each entry is a full generation, so
-quote the cost before running a list-driven workflow: the listing's `lists`
+error. Validation realizes a `constant:` default before checking it, so a list
+defaulted to a constant validates the way it will run. `release_pipeline` on a
+`for_each` step releases after the *last* member. An entry key no step reads is
+a validation warning at the entry's path.
+
+Each entry is a full generation, so quote the cost first. The listing's `lists`
 block names the fields an entry takes and the steps over it, and its `cost`
-carries `per_entry` once one entry has been measured. `validate_workflow`
-with your `arguments` answers with a `plan` whose `estimate` already does
-that arithmetic (`basis: per_entry`); without `per_entry` it extrapolates
-the stored total linearly over your list (`basis: derived` - an estimate
-rather than a measurement) and reports the stored total unchanged only
-when your list is the one it was measured with (`basis: catalog`). Ahead of
-all of those it quotes this box's own finished runs of the shape you are
-about to run when it has any (`basis: observed`, with `runs` saying how
-many) - quote the plan's figure and say which basis it has. An
-entry key no step reads is a validation warning at the entry's path, so a
-misspelt field is caught before the run. Then
-`validate_workflow` with the
-`arguments` you will run with: it expands your list, not the template's
-default, resolves the variables your entries name, and reports a duplicate
-name or a missing field at the entry's path.
+carries `per_entry` once one entry has been measured. `validate_workflow` with
+the `arguments` you will run with expands your list (not the template's
+default), resolves the variables your entries name, reports a duplicate name
+or a missing field at the entry's path, and answers with a `plan` priced for
+your list (the `basis` values are in "The loop", step 4).
 
 Every error carries a path in the file you wrote, not in the expanded step
 list: a bad reference inside a member is reported at the `for_each` step's
@@ -690,118 +628,105 @@ by the source, and `validate_workflow` names the entries to add or drop.
 
 ### The loop
 
-1. `validate_workflow` — free and instant. It reports every schema error at
-   once, each with the JSON path it sits at, plus warnings for argument names
-   that do not appear in the real pipeline signature. It also catches a
-   `previous_result:` (or `from_previous_result`) that names no earlier step,
-   which is what renaming a step half way through leaves behind. Pass the
-   `arguments` you are going to run with as well: a name the workflow no
-   longer declares, a value that will not coerce to the declared type, and an
-   `asset:`, `prompt:` or `output:` reference that names nothing in this
-   workspace each come back at `arguments.<name>`, for free, instead of after
-   the model has loaded. Without them the verdict is about the stored
-   definition and its stock defaults - `checked_arguments` in the answer says
-   which it was.
-2. Fix everything reported, including the warnings: a passing validation does
-   not mean the pipeline accepts the arguments, and a typo against a real
-   `__call__` shows up only as one of those warnings. The server only computes
-   signature warnings for a schema-valid draft — while schema errors remain it
-   returns `warnings: []`, so validate again after fixing them to see the
-   warnings.
-3. `save_workflow` — validates again on the way in and returns the catalog
+1. `validate_workflow` - free and instant. It reports every schema error at
+   once, each at its JSON path, plus warnings for argument names that are not
+   in the real pipeline signature, and a `previous_result:` (or
+   `from_previous_result`) that names no earlier step. Pass the `arguments` you
+   will run with: an undeclared name, a value that will not coerce to the
+   declared type, and an `asset:`, `prompt:` or `output:` reference that names
+   nothing in this workspace each come back at `arguments.<name>`. Without them
+   the verdict is about the stored definition and its defaults;
+   `checked_arguments` in the answer says which it was.
+2. Fix everything reported, including the warnings: a typo against a real
+   `__call__` shows up only as a warning. Signature warnings are computed only
+   for a schema-valid draft (while schema errors remain, `warnings` is `[]`),
+   so validate again after fixing the errors.
+3. `save_workflow` - validates again on the way in and returns the catalog
    metadata the saved draft will carry.
-4. `run_workflow` with `acknowledged_cost=true`, after telling the user what it
-   costs. Without the acknowledgement the call is refused. The figure to tell
-   them is the `plan` on the validate answer - `estimate.minutes` with its
-   `basis`, and every `downloads_required` entry named as its own line item,
-   since weights not on this box are minutes and gigabytes the cost block
-   never counted. `basis` says where the figure came from, and that is what
-   decides how to quote it: `observed` is this box's own finished runs of
-   this shape (the cold median over `runs` of them, preferred over any
-   curated figure) - "about N minutes, measured over M runs"; `per_entry` is
-   a measured per-entry rate re-priced for your list; `catalog` is a
-   measured total for a run whose lists are the ones it was measured with;
-   `derived` is that total extrapolated over a list you changed the length
-   of, or scaled along the steps, canvas or frames you moved off the
-   defaults it was measured at (`scaled` names each axis that moved) - say
-   it is an estimate; `inherited` is an inline workflow priced
-   from this box's runs of the catalog template with the same pipeline
-   (`inherited_from`) - say it is an approximation, and relay the warning
-   if offload, quantization or frame count differ; `other_device` is a figure from another
-   accelerator - say so too; `unknown` is no figure at all. `gb` on a
-   `downloads_required` entry is null when the hub could not be asked, and
-   `steps`/`list_entries` say how many members the list actually produced.
-   Then pass that plan back:
-   `acknowledged_cost={"fingerprint": plan.fingerprint, "minutes":
-   plan.estimate.minutes, "downloads": [...]}` - the server refuses with 409
-   if the run's shape changed since the quote, and the refusal carries the
-   new plan to quote from. `true` is for a plan that was null. When `basis`
-   is `unknown`: a workflow you wrote
-   or copied carries no `cost` of its own, but the pipeline inside it usually
-   does: `list_workflows(include_models=true)` finds the `models/` entry that
-   loads the same checkpoint, and its per-image figure times the number of
-   images is the number to quote. Say "a few minutes" only when no entry with
-   that pipeline has been measured.
-5. `wait_for_job` rather than a polling loop, asking for the job's
-   `plan.estimate` (in seconds, plus a margin) as `timeout_seconds`, so one
-   call can cover the whole run. The server caps a single call, and the
-   tool's description states the cap: the reply's
-   `timeout_applied_seconds` and `timeout_capped` say what you got, and
-   `waited_seconds` how long it took. Call it again if it returns
-   `still_running: true`. Without `timeout_seconds` a call waits 20 seconds. A
-   running job's `progress` carries the step being run; the phase
-   (`loading`, `generating`, `decoding`, `saving`) with the model it names in
-   `phase_detail`; `seconds_in_phase`, time spent in that phase; and
-   `seconds_since_event`, time since the last progress event - a number that
-   climbs while `denoise_step` stays put is the "nothing is happening" read.
-   It also carries `denoise_step`/`denoise_total_steps`,
-   null until the denoise loop starts; judge a slow run against a stuck one
-   by whether `denoise_step` has moved since a poll minutes ago, not by
-   silence past a fixed threshold. A null `denoise_step` under `generating`
-   is the pipeline's lead-in - encoding the prompt and every reference -
-   which emits nothing and can run for many minutes when a video reference
-   is among them; gaps between denoise steps are uneven too where a
+4. `run_workflow` with `acknowledged_cost`, after telling the user what it
+   costs; without it the call is refused. The figure to tell them is the
+   `plan` on the validate answer: `estimate.minutes` with its `basis`, and
+   every `downloads_required` entry as its own line item, since weights not on
+   this box are minutes and gigabytes the estimate does not count. `basis`
+   decides how to quote it:
+   - `observed` - this box's own finished runs of this shape (the cold median
+     over `runs` of them, preferred over any curated figure): "about N
+     minutes, measured over M runs".
+   - `per_entry` - a measured per-entry rate re-priced for your list.
+   - `catalog` - a measured total for a run whose lists are the ones it was
+     measured with.
+   - `derived` - that total extrapolated over a list of another length, or
+     scaled along the steps, canvas or frames you moved off its defaults
+     (`scaled` names each axis): say it is an estimate.
+   - `inherited` - an inline workflow priced from this box's runs of the
+     catalog template with the same pipeline (`inherited_from`): say it is an
+     approximation, and relay the warning if offload, quantization or frame
+     count differ.
+   - `other_device` - a figure from another accelerator: say so.
+   - `unknown` - no figure. A workflow you wrote carries no `cost`, but
+     `list_workflows(include_models=true)` finds the `models/` entry that loads
+     the same checkpoint, and its per-image figure times the number of images
+     is the number to quote. Say "a few minutes" only when no entry with that
+     pipeline has been measured.
+
+   `gb` on a `downloads_required` entry is null when the hub could not be
+   asked, and `steps`/`list_entries` say how many members the list produced.
+   Pass the plan back as `acknowledged_cost={"fingerprint": plan.fingerprint,
+   "minutes": plan.estimate.minutes, "downloads": [...]}`: the server refuses
+   with 409 if the run's shape changed since the quote, and the refusal carries
+   the new plan. `true` is for a plan that was null.
+5. `wait_for_job` rather than a polling loop, with the job's `plan.estimate`
+   (in seconds, plus a margin) as `timeout_seconds`, so one call can cover the
+   whole run. The server caps a single call (the tool's description states the
+   cap); the reply's `timeout_applied_seconds` and `timeout_capped` say what you
+   got, and `waited_seconds` how long it took. Call again on
+   `still_running: true`. Without `timeout_seconds` a call waits 20 seconds.
+
+   A running job's `progress` carries the step; the phase (`loading`,
+   `generating`, `decoding`, `saving`) with the model in `phase_detail`;
+   `seconds_in_phase`; `seconds_since_event`, the time since the last progress
+   event; and `denoise_step`/`denoise_total_steps`, null until the denoise loop
+   starts. Judge a slow run from a stuck one by whether `denoise_step` has moved
+   since a poll minutes ago, not by silence past a fixed threshold. A null
+   `denoise_step` under `generating` is the pipeline's lead-in (encoding the
+   prompt and every reference), which emits nothing and can run for many
+   minutes with a video reference; gaps between denoise steps are uneven where a
    transformer block cache is configured. Both are normal, and the model
-   family's own skill carries the measured figures. `denoise_total_steps` is
-   the schedule the pipeline actually runs, which is not always the
-   `num_inference_steps` that was asked for: MiniMax H3's scheduler counts
-   sigma grid points including the terminal zero, so it runs N-1 model
-   evaluations for N (9 reports 8, 20 reports 19) - the vendor's convention,
-   not a dropped step; raising the number still buys the steps it looks
-   like it does.
-6. `get_output_image` to look at what was actually made, and say whether it
-   answers the request. Nothing before this step establishes that it does.
+   family's skill carries the measured figures. `denoise_total_steps` is the
+   schedule the pipeline actually runs: MiniMax H3's scheduler counts sigma
+   grid points including the terminal zero, so it runs N-1 model evaluations
+   for N (9 reports 8, 20 reports 19). That is the vendor's convention, not a
+   dropped step.
+6. `get_output_image` to look at what was made, and say whether it answers
+   the request. Nothing before this step establishes that it does.
    `get_output_frames` looks at a video and `get_output_audio` listens to a
    soundtrack. A text-only client reads levels and clipping from
    `get_gallery_metadata` instead (`media` peak and mean, `findings`;
    `envelope=true` for where).
 
-   To confirm the words a clip speaks - a text-only client can't consume the
-   `AudioContent` block `get_output_audio` returns - run
-   `templates/check-script` against its script rather than reading a
-   transcript by eye. `validate_workflow(name="templates/check-script",
-   arguments={"input_audio": "output:<name>", "lines": [...]})` first (free;
-   it takes an audio file or a video's muxed soundtrack directly, and
-   `lines` is the script in order, H3 markup allowed, `{text, shot}` to name
-   a line's shot (plus `start_frame` when the cut repeats that shot's name), `[]` for a take that should be silent), then
-   `run_workflow(..., acknowledged_cost={"fingerprint": ..., "minutes": ...,
-   "downloads": [...]})` bound to that plan with `wait_seconds=60`, then
-   `get_output_text` on the result, and `delete_output(job_id=...)` the
-   scratch run afterward. Its `findings` are the places to listen: a
-   dropped or changed line (`line_mismatch`, with `heard`), a markup word
-   spoken aloud, speech in a shot meant to be silent, a last word clipped
-   by the cut. Words Whisper invents over silence or music are under
-   `discarded`, not findings. This workflow's plan comes back
-   `basis: "unknown"` with `minutes: null` - nothing is curated or observed
-   for it - so quote what it actually takes rather than the plan: seconds,
-   not minutes (a few seconds per clip in practice). Four calls and a short
-   wait, not a GPU-spending read tool - keep the normal queue rather than
-   adding one.
+   To confirm the words a clip speaks (a text-only client can't consume the
+   `AudioContent` block `get_output_audio` returns), run
+   `templates/check-script` against its script.
+   `validate_workflow(name="templates/check-script",
+   arguments={"input_audio": "output:<name>", "lines": [...]})` first: it takes
+   an audio file or a video's muxed soundtrack, and `lines` is the script in
+   order, H3 markup allowed, `{text, shot}` to name a line's shot (plus
+   `start_frame` when the cut repeats that shot's name), `[]` for a take that
+   should be silent. Then `run_workflow(..., acknowledged_cost={"fingerprint":
+   ..., "minutes": ..., "downloads": [...]})` bound to that plan with
+   `wait_seconds=60`, `get_output_text` on the result, and
+   `delete_output(job_id=...)` for the scratch run. Its `findings` are the
+   places to listen: a dropped or changed line (`line_mismatch`, with `heard`),
+   a markup word spoken aloud, speech in a shot meant to be silent, a last word
+   clipped by the cut. Words Whisper invents over silence or music are under
+   `discarded`, not findings. The plan comes back `basis: "unknown"` with
+   `minutes: null`; quote seconds, not minutes (a few seconds per clip).
 7. Getting the files to the user's machine. `download_output` and `export_job`
    write on the machine running `dw.serve`, which over a remote `--mcp`
    endpoint is the GPU box. The last mile of every deliverable is the `url`
-   each `list_gallery` entry carries (or `export_job`'s `zip_url`), fetched
-   with the same bearer token the MCP connection uses:
+   each `list_gallery` entry carries (or `export_job`'s `zip_url`). `/outputs`
+   and `/exports` are served without the token (only `/api/*` and `/mcp` are
+   gated), so the header below is harmless but not required:
 
        curl -H "Authorization: Bearer $DW_API_TOKEN" \
             -o exports/still.png "http://<box>:8765/outputs/ltx2/Gyre/20260910-.../still.png"
@@ -937,59 +862,44 @@ can name a subfolder of the run directory for its files:
 
 The convention is two names: `final` for a step whose output the user will
 be shown, `intermediate` for everything else. The engine treats no name
-specially and applies no default - a step that says nothing writes to the
-run's root as it always has - but the gallery, `get_job` and `list_gallery`
-all carry the value, so a consumer that follows the convention can tell the
-deliverable from the scratch without knowing the workflow. Mark every saving
-step of a multi-step workflow; a one-step workflow needs nothing.
-
-The shipped templates follow it: every template with two or more saving
-steps marks each one, so a workflow copied from a template starts with the
-roles in place.
+specially and applies no default (a step that names none writes to the run's
+root), but the gallery, `get_job` and `list_gallery` all carry the value, so a
+consumer can tell the deliverable from the scratch without knowing the
+workflow. Mark every saving step of a multi-step workflow; a one-step workflow
+needs nothing. Every shipped template marks each of its saving steps, with at
+least one `final`.
 
 The value is a relative path of any depth (`shots/act-1`), may be a
 `variable:` or, inside a `for_each` step, an `item:` reference, and follows
-the `output:` segment rule - each segment starts with a letter, digit or
-underscore; `..`, a backslash and a leading `.` are refused - so every
+the `output:` segment rule (each segment starts with a letter, digit or
+underscore; `..`, a backslash and a leading `.` are refused), so every
 subfolder written is one a later workflow can name:
 `output:dialogue-short/latest/final/episode.mp4`. A bad value is a
-validation error at its JSON path. `file_base_name` is a name, not a path:
-a separator there is refused, and `subfolder` is the way to place a file. It
-replaces the name the engine would derive from the workflow and step rather
-than prefixing it, so `"file_base_name": "episode"` in a `final` subfolder
-writes `final/episode-0.0.mp4` - name each step that sets one differently, or
-the second collides and picks up a `-2`.
-
-A step that saves nothing and which no later step reads does not run at
-all: the engine drops it before the first step executes and warns once per
-dropped step. That is how a template whose portraits can be supplied as
-`asset:` files stops paying for the steps that would have drawn them. It
-follows from what the definition says, never from a value produced during
-the run, so it is decided at validate time too - the `plan` a validate call
-answers with counts only the steps that will run and lists the rest under
-`elided_steps`. Four things keep a step: a `result` with a `content_type`
-and `save` not `false`, being the last step, being read by a later step
-(`previous_result:`, `gather:`, a `pipeline_reference`, a shared component),
-or being read by a step that is itself kept - elision is transitive. If a
-step you meant to run is named in the warnings, a reference to it is
-misspelled somewhere later or it needs a `result`.
+validation error at its JSON path. `file_base_name` is a name, not a path: a
+separator there is refused (see [Result Configuration](#result-configuration)
+for how it names files).
+
+A step that saves nothing and that no later step reads does not run at all;
+see [A step nothing reads does not run](#a-step-nothing-reads-does-not-run).
 
 ### Composing a stored workflow
 
 A step with a `workflow` block runs another workflow as one step of this one,
 with `arguments` handed down as that workflow's variables. Its `path` is read
-the way `run_workflow`'s `workflow_path` is - a catalog name from
-`list_workflows`, with or without `.json`, a path relative to the file that
-names it, or `builtin:name.json` - and resolves beside the referencing file
-first, then in this workspace's `workflows/`, then in each read-only source
-the server lists. A stored template is composed by its catalog name; copying
-it into the workspace to reach it is no longer necessary, and a copy silently
-stops tracking the original.
-
-Declare a `result` on the composing step and the composed output is saved
-there, once, under that step's name and subfolder - the composed workflow's
-own last step does not write a second copy. Its other steps write into the
-same run directory, prefixed with the composing step's name.
+the way `run_workflow`'s `workflow_path` is: a catalog name as `list_workflows`
+reports it (`templates/minimax/reference-to-video`), with or without `.json`; a
+path relative to the file that names it (`../models/x.json`); or
+`builtin:name.json` for the packaged fragments in `dw/workflows/`. A name
+resolves beside the referencing file first, then in this workspace's
+`workflows/`, then in each read-only source the server lists, so a stored
+template is composed by its catalog name rather than copied (a copy stops
+tracking the original). A path that lands outside every source is refused.
+
+Declare a `result` (with a `content_type`) on the composing step and the
+composed output is saved there, once, under that step's name and subfolder;
+the child's own last step does not write a second copy. Without one, the child
+saves its own output. The child's other steps write into the same run
+directory, prefixed with the composing step's name.
 
 `validate_workflow` resolves the path, so a name that reaches nothing is an
 error at `steps[N].workflow.path` before anything is queued; it also validates
@@ -1010,37 +920,30 @@ supplied stills" — rather than what technique it demonstrates. A first sentenc
 longer than that is truncated with an ellipsis in every listing.
 
 Declare `shape`, `traits` or `summary` at the top level only when derivation
-gets it wrong; a declaration that merely repeats the derivation is noise that
-rots when the rules change, and the repo's catalog tests refuse it. `cost` is
-never derived — leave it absent until a run has been measured. A `cost` entry
-is measured at the workflow's variable defaults: a run that moves
-`num_inference_steps`, `width`/`height` or `num_frames` (per entry in a list,
-summed) is quoted scaled in proportion to them, so a figure measured at some
-other size or step count is re-priced to the defaults before it is written
-(dialogue-short's 42 min at 1344x768 is 21.2 at its 960x544). That makes it
-the catalog's verified marker as well: an entry carrying `cost` has been run
-to completion on the device it names, and one without has only been authored
-— its description may still say what it has not been able to check (VRAM at
-a size, whether a format carries what the pipeline returns), and the first
-run is the one that finds out.
-
-`cost_drivers` is the other half of saying what a workflow costs, and it *is*
-for derivation: the variables that move the wall clock — a frame count, a
-step count, a segment count, the list a `for_each` runs over — never a prompt
+gets it wrong; the repo's catalog tests refuse a declaration that repeats the
+derivation. `cost` is never derived: leave it absent until a run has been
+measured. A `cost` entry is measured at the workflow's variable defaults, and a
+run that moves `num_inference_steps`, `width`/`height` or `num_frames` (per
+entry in a list, summed) is quoted scaled in proportion to them, so a figure
+measured at another size or step count is re-priced to the defaults before it
+is written (dialogue-short's 42 min at 1344x768 is 21.2 at its 960x544). An
+entry carrying `cost` has been run to completion on the device it names; one
+without has only been authored, and its first run is the one that finds out
+what its description could not check.
+
+`cost_drivers` names the variables that move the wall clock (a frame count, a
+step count, a segment count, the list a `for_each` runs over), never a prompt
 or a seed. The server buckets its own finished runs by those values and
-reports the result as `observed` beside the curated `cost`, so a 345-frame
-run never informs a 124-frame figure and a list driver buckets on its length.
-Declaring none is not neutral: the figure then falls back to runs that
-overrode nothing at all, which most real runs do, so a measured workflow with
-no drivers keeps answering "unknown". A driver the estimate cannot scale
-along - anything but steps, width, height and frames, such as a weights
-dtype, a LoRA name or an audio duration - still splits the buckets (a text
-value buckets by its exact value). When a run moves one off its default, the
-curated figure is kept but carries `low_confidence: true`, and `differs`
-names the drivers, since it was measured at the default. Each name must be a variable the
-workflow declares — `tests/test_observed_cost.py` sweeps the catalog for one
-that is not, since a driver bucketing on nothing looks exactly like a driver
-that works.
+reports the result as `observed` beside the curated `cost`, so a 345-frame run
+never informs a 124-frame figure, and a list driver buckets on its length.
+With no drivers, the figure falls back to runs that overrode nothing at all, so
+a measured workflow with no drivers mostly answers "unknown". A driver the
+estimate cannot scale along (anything but steps, width, height and frames,
+such as a weights dtype, a LoRA name or an audio duration) still splits the
+buckets, a text value by its exact value. When a run moves one off its
+default, the curated figure is kept but carries `low_confidence: true`, and
+`differs` names the drivers. Each name must be a variable the workflow
+declares (`tests/test_observed_cost.py` sweeps the catalog for that).
 
 ## Assessing a run's output
 
@@ -1194,7 +1097,7 @@ already made: each entry in `videos` is `output:` + the run's
 }
 ```
 
-Supported content types: `image/jpeg`, `image/png`, `image/webp`, `image/gif`, `video/mp4`, `audio/wav`, `audio/flac`, `audio/mpeg` (mp3), `audio/ogg`, `audio/opus`, `audio/aiff`, `application/json`, `text/plain` (plus the common aliases `audio/x-wav`, `audio/mp3`, `audio/vorbis`).
+Supported content types: `image/jpeg`, `image/png`, `image/webp`, `image/gif`, `video/mp4`, `audio/wav`, `audio/flac`, `audio/mpeg` (mp3), `audio/ogg`, `audio/opus`, `audio/aiff`, `application/json`, `text/plain` (plus the aliases `audio/x-wav`, `audio/x-flac`, `audio/mp3`, `audio/vorbis`). Any other `audio/` or `video/` type is a validation error, and so is a type a browser runs as a page (`text/html`, `image/svg+xml`, `application/xml` and the like) (`dw/content_types.py`).
 
 A task command's implementation declares what it hands back - most answer an
 `artifact` (a file `result` saves in one of the media content types above),
@@ -1205,7 +1108,7 @@ some (`judge`) answer a bare `scalar` that cannot be saved at all, and some
 may not carry a `result` at all. Both are checked in validation, by the
 command's own declared kind rather than a name match.
 
-`subfolder` places the step's files in a subfolder of the run directory - see *Saying which output is the deliverable* above. `file_base_name` is the base name the step's files are written under, replacing the name derived from the workflow and step; it may not contain a path separator.
+`subfolder` places the step's files in a subfolder of the run directory - see *Saying which output is the deliverable* above.
 
 For video, `"fps"` is the rate the file is written at. It is rarely needed:
 frames that know their own rate carry it - a video read from a file or an
@@ -1250,21 +1153,22 @@ where `base_name` is `{workflow_id}-{step_name}.{step_index}` unless the step's
 `file_base_name`, which replaces it entirely. `step_index` is the step's position in the
 workflow, `result_index` counts the argument-combination iterations the step ran (see
 cartesian product, above), and `artifact_index` counts multiple artifacts within one result
-(`num_images_per_prompt > 1`, or a dict result saved key by key). The derived name is what
-makes two steps' files distinct, so when you replace it on more than one step in the same
-subfolder, give each a different name - otherwise the second one gets a `-2` counter.
+(`num_images_per_prompt > 1`, or a dict result saved key by key); a JSON result is one
+`{base_name}-{result_index}.json`. In a composed child the composing step's name leads
+`base_name`. `file_base_name` may not
+contain a path separator; `"file_base_name": "episode"` in a `final` subfolder writes
+`final/episode-0.0.mp4`. The derived name is what makes two steps' files distinct, so when
+you replace it on more than one step in the same subfolder, give each a different name -
+otherwise the second one gets a `-2` counter.
 
 ## Pipeline Configuration
 
-A step's `configuration` is dw's own vocabulary rather than the model's — each key drives
-a different call — so it is a closed set: a name the schema does not declare fails
-validation instead of being ignored. That matters most for the keys it would otherwise
-be quietest about. A misspelled `offload` used to validate, load, and run with no
-offloading at all, surfacing as an out-of-memory error with nothing pointing at the
-spelling; it now fails before the first model loads. Model-side values that are not part
-of this vocabulary have blocks of their own: `from_pretrained_arguments` for the
-constructor, `arguments` for the call, and `configs` for a modular pipeline's block
-configs.
+A pipeline step's `configuration` is dw's own vocabulary rather than the model's, so it
+is a closed set: a name the schema does not declare (a misspelt `offload`, say) fails
+validation instead of being ignored. Model-side values have blocks of their own:
+`from_pretrained_arguments` for the constructor, `arguments` for the call, and
+`configs` for a modular pipeline's block configs. A component's `configuration` (under
+`transformer`, `vae` and the like) is not closed.
 
 ### Memory Offloading
 
@@ -1365,9 +1269,9 @@ pins its tensors to one device is the usual reason.
 }
 ```
 
-> **Renamed:** this setting was `do_not_send_to_device`. The old name is no longer
-> recognized - a workflow still using it will load the component and then move it to the
-> device anyway, since an unknown key is ignored rather than rejected. Rename the key.
+> **Renamed:** this setting was `do_not_send_to_device`, which is no longer recognized.
+> In a pipeline `configuration` the old name is a validation error; in a component's
+> `configuration` it is ignored and the component is moved to the device anyway.
 
 #### On-demand components
 
@@ -1477,26 +1381,25 @@ later step needing one of them reloads it.
 
 Before the first step executes, the engine drops any step whose result no later step
 reads and which writes no file, and warns once per dropped step saying which and why.
-`dialogue-short` cast from portraits that already exist used to run its two Z-Image
-steps anyway and throw the pictures away - about a minute of GPU per episode on
-something nothing looked at (#122).
+That is how a template whose portraits can be supplied as `asset:` files skips the steps
+that would have drawn them (#122). Elision follows from the definition, never from a
+value produced during the run, so it is decided at validate time too.
 
 Four things keep a step:
 
-- **it saves** - a `result` with a `content_type`, and `save` not `false`. A workflow
-  whose whole point is writing three images references nothing, so this is the rule that
-  keeps elision from being destructive. `"save": false` is how a step says it is
-  scaffolding.
+- **it saves** - a `result` with a `content_type`, and `save` not `false`.
+  `"save": false` is how a step says it is scaffolding.
 - **it is the last step** - it is the run's answer, whatever it declares.
 - **something reads it** - `previous_result:`/`from_previous_result` (including
   `previous_result:step.property`), a `gather:` (which is a list of those by the time
   this runs), a `pipeline_reference` naming it, or a `reused_components` entry naming a
   component it shares.
-- Elision is transitive, so dropping a step can drop the step it read in turn.
+- **a kept step reads it** - elision is transitive, so dropping a step can drop the step
+  it read in turn.
 
 `release_pipeline` on an elided step moves onto the last surviving step before it when
-that step loaded the same pipeline, and `release_models` moves unconditionally - a
-release that vanished with its step would leak the memory it existed to free. The plan a
+that step loaded the same pipeline, and `release_models` moves unconditionally, so the
+release still happens. The plan a
 validate call answers with is computed after elision, so `steps`, `downloads_required`
 and the cost it quotes are the work that will actually happen, and it lists what was
 dropped under `elided_steps`; the run manifest records the same list.
@@ -1674,11 +1577,9 @@ on a Mac and back again:
 ```
 
 Only the backend is translated. A device index survives when the backend matches, so
-`cuda:1` on a single-GPU CUDA box remains the error it always was; when the backend does
-not match, the index is dropped and the warning says so — a workflow that meant to spread
-work across two accelerators will not on a machine that has one. `"device": "cpu"` is
-never translated, since pinning a step to the CPU is how a GPU-specific problem gets
-ruled out.
+`cuda:1` on a single-GPU CUDA box is an error; when the backend does not match, the index
+is dropped and the warning says so. `"device": "cpu"` is never translated, since pinning
+a step to the CPU is how a GPU-specific problem gets ruled out.
 
 ### Modular Pipelines
 
@@ -1814,24 +1715,24 @@ Hold plus the reference is the `music-video` default: the template passes each s
 as a `MiniMaxH3AudioReference` and its `hold_audio` variable defaults to
 `previous_result:slice`; set it to null for the reference alone. The `match_audio` chains
 (`chain-matched-to-audio`, `chain-matched-and-aligned`) keep `hold_audio` null by default,
-since a chain has no per-shot slice to name; set it to an audio file to hold. Measured on
-lem (#808: `music-video`, 4 shots of 124 frames, two seeds and so two songs, offset of each
-shot's own audio against its source slice by `measure_sync`), the reference alone put one of
-four shots 1.37 s late (seed 42) and another 0.18 s early (seed 7), with correlation 0.35 to
-0.96; reference plus hold measured 0.00 s on all eight shots, correlation 0.95 to 1.00. The
-earlier A/B (#619: `chain-matched-to-audio`, a 10 s sung track, seed 42, one run per arm)
-found a held track with *image-only* references kept the soundtrack exact but the mouth was
-open at about 3 of 6 sung-word onsets, against 6 of 6 with the reference alone, and read as
-speech to camera rather than singing. #808 measured the audio's offset, not mouth onsets,
-so how closely the mouth follows a held-plus-referenced song is still a visual check. Hold
-is also for picture that must fit audio exactly, such as motion cut to music.
-On `fl2va`, hold is the locked-frame lip-sync route (#985: `image-to-video`'s fl2va, a first
-frame plus a held 5.17 s song, int8 turbo, two seeds): frame 0 kept the picture's pixels and
-the mouth followed the song as in an audio-referenced Ref2VA shot, at about a fifth of the
-denoise time of Ref2VA `[keyframe completion + audio reuse]`, which keeps only the framing.
-`refine_strength` also uses hold, to keep the base pass's own audio, which was generated
-jointly with that video - a different case from lip sync to supplied audio (see the refine
-section below).
+since a chain has no per-shot slice to name; set it to an audio file to hold. What was
+measured:
+
+- **Reference plus hold keeps the audio in sync** (#808, `music-video`, 4 shots of 124
+  frames at two seeds, `measure_sync` against each source slice): 0.00 s offset on all
+  eight shots, correlation 0.95 to 1.00. The reference alone put one shot 1.37 s late and
+  another 0.18 s early, correlation 0.35 to 0.96. That measured the audio, not the mouth.
+- **Hold with image-only references** keeps the soundtrack exact but weakens the singing
+  (#619, a 10 s sung track): the mouth was open at about 3 of 6 sung-word onsets against 6
+  of 6 with the audio reference alone, and read as speech to camera.
+- **On `fl2va`, hold is the locked-frame lip-sync route** (#985, a first frame plus a held
+  5.17 s song): frame 0 kept the picture's pixels and the mouth followed the song as in an
+  audio-referenced Ref2VA shot, at about a fifth of the denoise time of Ref2VA
+  `[keyframe completion + audio reuse]`, which keeps only the framing.
+
+Hold is also for picture that must fit audio exactly, such as motion cut to music.
+`refine_strength` uses hold differently, to keep the base pass's own jointly generated
+audio (see the refine section below).
 
 ### H3: holding a clip with `guides`
 
@@ -1899,28 +1800,26 @@ footage, as its frames are.
 
 A guide holds its span's look as well as its composition and motion: the frames under it
 come out close to a copy of the clip, whatever the prompt says about style. A full-length
-guide at frame 0 therefore reproduces the take rather than restyling it - measured on lem,
-a 124-frame guide with the prompt "the same scene as a hand-painted watercolour
-animation" came back photoreal (#648). The prompt steers the frames the guides leave
-free. Restyling a whole take needs a LoRA trained on guides (#612), not the guide alone.
+guide at frame 0 reproduces the take rather than restyling it (a 124-frame guide prompted
+as "a hand-painted watercolour animation" came back photoreal, #648). The prompt steers
+only the frames the guides leave free; restyling a whole take needs a LoRA trained on
+guides (#612).
 
 With no guides (or `guides: []`) the step runs the stock layout and draws the same noise
-from its seed as before. Repeat runs at one seed are bit-identical only on a server with
-`cudnn_deterministic: true` (`ACCELERATION.md`, *TF32 and cuDNN*); with the default
-`false`, two runs at seed 42 match frame for frame by eye, but their audio levels differ by
-a few dB.
+from its seed as an unguided step. Repeat runs at one seed are bit-identical only on a
+server with `cudnn_deterministic: true` (`ACCELERATION.md`, *TF32 and cuDNN*); with the
+default `false`, two runs match frame for frame by eye but their audio levels differ by a
+few dB.
 
 #### Chaining with a guide: `continuity: "guide"`
 
 A chain on an H3 `t2va` or `fl2va` step can carry its seam with a guide instead of a
 keyframe: `"chain": {"segments": 3, "continuity": "guide", "guide_frames": 22}`. Every
 segment after the first gets the previous segment's last P = `guide_frames` frames as a
-guide at frame 0 (appended to the step's own `guides`; the carried clip counts as a guide, so a step
-that also writes its own `guides` may list at most three of them with
-`continuity: "guide"` - validation says so before the run rather than after the first
-segment), with `"audio": true` when
+guide at frame 0, appended to the step's own `guides`, with `"audio": true` when
 `carry_audio` is (the default), so motion and voice run on across the seam rather than
-restarting from a still. On `fl2va`, `image` is set to the guide's first frame. The
+restarting from a still. The carried clip counts toward the four-guide limit, so a step
+that writes its own `guides` may list at most three; validation says so before the run. On `fl2va`, `image` is set to the guide's first frame. The
 next segment opens with a near-copy of those P frames, so P frames are trimmed from its
 head: N segments of F frames give F + (N - 1)(F - P) frames - 3 x 124 at P = 22 is 328.
 
@@ -1940,37 +1839,30 @@ head: N segments of F frames give F + (N - 1)(F - P) frames - 3 x 124 at P = 22
 ### Promoting an H3 take to 768p in latent space: upscale_h3_latents and decode_h3_latents
 
 Once a 960x544 MiniMax-H3 take reads the way it should, `upscale_h3_latents` and
-`decode_h3_latents` promote it to 1344x768 without denoising it again - cheaper than a
-native 768p render, since only a small 3D-convolution network and a VAE decode run,
-not the transformer. These two tasks run no refine pass, so the result
-is sharper than the 544p take but cannot show detail the base pass never generated; the
-refine section below adds one.
-Measured on a 124-frame crowd scene, it took 7.8 min against a native 768p render's
-12.7 min, and the faces came out soft and waxy where the native render's were distinct.
-So this is a measurement path, not a catalog template: the catalog's native 768p render
-is `templates/minimax/video-with-audio-768p`, and it is the one to use when faces matter.
+`decode_h3_latents` promote it to 1344x768 without denoising it again: only a small
+3D-convolution network and a VAE decode run, not the transformer. With no refine pass the
+result is sharper than the 544p take but cannot show detail the base pass never generated;
+the refine section below adds one. Measured on a 124-frame crowd scene, it took 7.8 min
+against a native 768p render's 12.7 min, and the faces came out soft and waxy where the
+native render's were distinct. When faces matter, use the catalog's native 768p render,
+`templates/minimax/video-with-audio-768p`.
 
 Target `width`/`height` must be multiples of 16, each between 1x and 4x the base
 latents' own size, and within the upscale cap of 1920x1088 (or portrait 1088x1920);
 validation refuses a literal size off the grid or over the cap before the run. Above
 1344x768 the plate is for a tiled refine ([Refining above 768p in
 tiles](#refining-above-768p-in-tiles)), and upscale-only it is soft. The latents
-pass from `base` to `up` to `decode` entirely in memory. A `previous_result:` property
+pass from `base` to `up` to `decode` in memory. A `previous_result:` property
 on a modular step names a key of the dict its `output` list returns, spelled as the
-pipeline spells it: `base.latents`, `base.audio` and `base.sampling_rate` - not
-`sample_rate`, which is what the saved AudioVideo calls it, and which the dict does
-not carry. A property no result carries is an error rather than an empty list, so a
-misspelt one fails the step instead of skipping the mux that reads it. The base
-step carries no `result` at all, so nothing beyond its return value is written -
-upscaling only makes sense for a take chosen from something already reviewed, so the
-544p pass that produced it is not itself a deliverable here.
-
-The upscaler weights (~691 MB, `LBH-123-AI/Minimax_h3_latent_Upscaler`, Apache-2.0,
-read at a pinned revision) and the H3 VAE download on first use, the same as any other
-model. A validate's `plan.downloads_required` lists the upscaler file as a file entry
-(`{repo, filename, revision, ...}`) while it is not cached, and the VAE is covered by
-the pipeline step's whole `MiniMaxAI/MiniMax-H3` entry, so a box that has run the base
-pass before but never this task is told of the upscaler download beforehand.
+pipeline spells it: `base.latents`, `base.audio` and `base.sampling_rate` (not
+`sample_rate`, which the dict does not carry). A property no result carries is an
+error, so a misspelt one fails the step. The base step carries no `result`, so the 544p
+pass is not written.
+
+The upscaler weights (`LBH-123-AI/Minimax_h3_latent_Upscaler`, read at a pinned
+revision) and the H3 VAE download on first use. While the upscaler file is not cached, a
+validate's `plan.downloads_required` lists it as a file entry (`{repo, filename,
+revision, ...}`); the VAE is covered by the pipeline step's `MiniMaxAI/MiniMax-H3` entry.
 
 ```json
 {
@@ -2178,12 +2070,11 @@ step with `"video": "previous_result:base.videos"` and the same `audio`/`sample_
 (and `fit`), saved with `"result": {"content_type": "video/mp4", "subfolder":
 "intermediate"}`. `base.videos` is the pipeline's batch - a list holding the one video -
 and `pair_audio` unwraps a batch of one (a batch of several is refused, since one track
-goes under one video). The H3 step hands its `videos` on carrying the model's 24 fps,
-the way the tiled refine's do, so `pair_audio` fits the track against 5.17 s and the
-file is written at 24 fps without being told. Frames that carry no rate - a plain list
-from a task - need `pair_audio`'s own `fps` argument, not the result's: the result's
-`fps` is applied by the writer after `fit` has already measured the frames at the 8 fps
-fallback, so it would re-time the picture under a track padded to three times its length.
+goes under one video). The H3 step's `videos` carry the model's 24 fps, so `pair_audio`
+fits the track against 5.17 s and the file is written at 24 fps without being told.
+Frames that carry no rate (a plain list from a task) need `pair_audio`'s own `fps`
+argument: `fit` measures the frames before the writer applies a declared rate, so
+without it the track is padded to the 8 fps fallback's length, three times too long.
 
 ### Refining the upscaled latents: refine_strength
 
@@ -2200,819 +2091,241 @@ track. This is a different use of hold from the one above: it keeps the audio th
 generated jointly with that video, rather than fitting picture to supplied audio. For lip
 sync to supplied audio see the hold section above (#619, #795).
 
+The workflow is the one above with `decode` and `mux` replaced by a `refine` step.
+`refine` carries a copy of the `base` step's whole `pipeline` block - the same
+`configuration`, `from_pretrained_arguments`, `loras`, `scheduler` and `audio_scheduler`,
+so the loaded model is reused - with these `arguments`, and the step saves the result:
+
 ```json
 {
-    "id": "H3LatentUpscaleRefine",
-    "description": "Promote a MiniMax-H3 take from 960x544 to 1344x768 in latent space, then refine it at low strength.",
-    "variables": {
-        "prompt": "prompt:minimax/fox_dawn_context_ir",
-        "num_frames": 124,
-        "num_inference_steps": 9,
-        "video_shift": 12.0,
-        "audio_shift": 3.0,
-        "weights_dtype": "{int4}",
-        "lora_scale": 1.0,
-        "lora_alpha": null,
-        "lora_model_name": "lightx2v/Minimax-h3-Turbo",
-        "lora_weight_name": "minimax_h3_fl2v_turbo_8step_v1.0_bf16.safetensors",
-        "lora_adapter_name": "turbo",
-        "seed": 42
+    "name": "refine",
+    "pipeline": {
+        "arguments": {
+            "prompt": "variable:prompt",
+            "num_frames": "variable:num_frames",
+            "width": 1344,
+            "height": 768,
+            "num_inference_steps": 5,
+            "output": ["videos", "audio", "sampling_rate"],
+            "latents": "previous_result:up",
+            "refine_strength": 0.2,
+            "hold_audio": "previous_result:base.audio"
+        }
     },
-    "variable_constraints": {
-        "num_frames": {
-            "modulus": 17,
-            "remainder": 5,
-            "min_frames": 124,
-            "max_frames": 345,
-            "snap": "up",
-            "reason": "the video VAE encodes 17 * n + 5 frames, and MiniMax-H3 generates between 5 and 15 seconds at 24 fps"
+    "result": { "content_type": "video/mp4", "fps": 24, "subfolder": "final" }
+}
+```
+
+`num_inference_steps` is the number of sigma points, ending at 0, spaced by the
+scheduler's `shift`, so a refine runs `num_inference_steps - 1` denoise evaluations. Here
+`5` points at shift 12 (the example's `video_shift`) and strength 0.2 are sigma `0.2,
+0.157, 0.110, 0.058, 0`: 4 evaluations. The time a refine takes scales with `num_inference_steps`. `refine_strength`
+sets where it starts, not how many steps run: a higher strength moves the picture further
+from the upscaled take, a lower one stays closer to it.
+
+Validation refuses a `refine_strength` that is not a number between 0 and 1 (exclusive;
+a [regional edit](#h3-regional-edit-with-source_video-and-a-mask) also takes 1.0), a
+refine with no `latents` or no `hold_audio`, a step that is not H3, and a
+`num_inference_steps` below 2.
+
+To refine a canvas above 1344x768, such as a 1920x1088 plate, see [Refining above 768p in
+tiles](#refining-above-768p-in-tiles). To give the schedule's sigma points yourself rather
+than spacing them by `shift`, see [Explicit refine sigmas](#explicit-refine-sigmas).
+
+### Explicit refine sigmas
+
+A recipe published with its own schedule cannot be reached by spacing points with
+`shift`. `refine_sigmas` gives the schedule itself: the sigma points, from
+`refine_strength` down to 0, used verbatim for both the video and audio schedules, with no
+shift applied. NVlabs' Open-H3-Regenerate-2K recipe starts from
+`(1 - 0.909375)·up + 0.909375·noise` and runs 3 Euler steps on four points:
+
+```json
+"arguments": {
+    "latents": "previous_result:up",
+    "hold_audio": "previous_result:base.audio",
+    "refine_strength": 0.909375,
+    "num_inference_steps": 4,
+    "refine_sigmas": [0.909375, 0.857143, 0.666667, 0]
+}
+```
+
+`refine_strength` still sets the re-noise, and the list replaces only the spacing. The
+step count is the number of points, so `num_inference_steps` is required beside the list
+and must equal its length: four points are 3 denoise evaluations. Validation, and the
+check before the call, refuse a `refine_sigmas` that:
+
+- is not a list of numbers, or has fewer than 2 points;
+- is not strictly decreasing;
+- does not start at `refine_strength` (within 1e-6);
+- does not end at exactly 0;
+- has a length other than `num_inference_steps`, or comes with no `num_inference_steps`;
+- comes with no `refine_strength`.
+
+A step without `refine_sigmas` runs the shift-spaced schedule. `refine_sigmas` works with
+`refine_tiles` too: every tile runs the same list.
+
+### H3: regional edit with source_video and a mask
+
+To change part of a take and keep the rest, pass a video as `source_video` on an H3
+`t2va`, `fl2va` or `ref2va` step. It is a path, `asset:`, `output:` or `previous_result:`
+reference to a video, and it stands in for the `latents` of a refine: the step VAE-encodes
+it over the generated rows and re-denoises from `refine_strength`, which is required and
+in (0, 1]: 1.0 regenerates the masked region from pure noise, and about 0.9 replaces an
+object in it (see [Choosing refine_strength for a regional
+edit](#choosing-refine_strength-for-a-regional-edit)). (Without a `source_video` the refine's range stays (0, 1).) With no
+mask the whole source is refined.
+
+```json
+{
+    "source_video": "asset:fox_dawn.mp4",
+    "mask_image": "asset:fox_mask.png",
+    "mask_grow": 1,
+    "refine_strength": 0.9,
+    "hold_audio": "asset:fox_dawn.mp4",
+    "prompt": "the fox holds a pine cone in its mouth, same light and motion"
+}
+```
+
+A `mask_video` (one frame per frame) or a `mask_image` (one mask for every frame) limits
+what is regenerated. Pass one, not both. White regenerates and black is kept, binarized
+at luma 128. The mask is max-pooled onto the transformer's patch grid, 16 px times the
+patch size per cell, so any white pixel marks its whole cell, and `mask_grow` widens it by
+that many cells (a whole number, 0 or more, default 1). In time the grain is the latent
+frame: about 4 pixel frames each (3.4 - every 17 frames encode to 5 latent frames), times
+the patch's time size, so a `mask_video` switching on or off mid-cell marks the whole cell. After every denoise step each
+other row is written back as the source noised to that step's sigma, with the refine's own
+noise, so at the end it is the source latent. The decode still round-trips it through the
+VAE, so kept pixels are near-identical to the source, not bit-identical. A higher
+`refine_strength` replaces what the mask covers, a lower one keeps its layout.
+
+#### Choosing refine_strength for a regional edit
+
+The useful range sits high. Measured on one take (a 960x544, 124-frame H3 shot of a woman
+holding a cup, the mask over the middle of the frame around her and the cup, `mask_grow` 1), seed 1062, 5 turbo steps,
+`video_shift` 6, with a prompt asking for a bright red raincoat:
+
+| `refine_strength` | Inside the mask | Inside-mask SSIM vs the source |
+|---|---|---|
+| 0.85 or less | A light rework: re-detailed, the source's objects and colours stay | 0.86 at 0.8, 0.83 at 0.85 |
+| 0.9 | A held object is replaced (the cup became a domed sundae cup); the same person, pose and clothing | 0.78 |
+| 0.95 | Clothing is reworked too (a hooded navy jacket) and the object replaced; face, hair and pose mostly hold | 0.65 |
+| 1.0 | A fresh generation: the prompt lands in full, but a different person and composition | 0.44 |
+
+Outside the mask held at every strength (SSIM about 0.98 on a crop outside it). So:
+0.9 to replace an object, 0.95 to rework clothing, 0.85 or less for a light rework. A large
+recolour of a big region, such as a dark top to a bright red coat, did not land below 1.0,
+and 1.0 gives up the region's identity: that is a fresh generation, not a regional edit.
+
+Why the numbers sit high: `refine_strength` is the starting sigma σ₀ on the **shifted**
+grid, the same as for any refine (and `refine_tiles`). The scheduler's shift maps the
+unshifted position u to σ = shift·u / (1 + (shift − 1)·u), so the start point on the
+unshifted schedule is u = s / (shift − (shift − 1)·s). At `video_shift` 6 that is
+0.8 → 0.40, 0.85 → 0.49, 0.9 → 0.60 and 0.95 → 0.76: a strength of 0.8 re-noises less than
+its number suggests. The bands above hold for those conditions only. Another
+`video_shift` or step count moves them, the way the formula says: a lower shift brings u
+closer to s, so the same change comes at a lower strength. A [`refine_sigmas`](#explicit-refine-sigmas)
+list is run as given, with no shift, so its points are the schedule itself.
+
+The source and the mask must match the step's `width` x `height`, and a `source_video` or
+`mask_video` its `num_frames`. A step that leaves width, height or `num_frames` out renders
+the source's. An all-black mask is refused, as it would regenerate nothing. Validation reads
+a source or mask that resolves already (a path, `asset:` or `output:`) and refuses a size,
+frame count or empty mask there, the way it reads guide videos; a `previous_result:` is
+checked at the call.
+
+`hold_audio` is still required with `refine_strength`, since the refine holds the
+soundtrack. It is normally the `source_video` itself: a video path (`.mp4`, `.mov`, `.mkv`,
+`.webm`, `.avi`) is held by its own soundtrack, so the edit keeps the take's audio. A video
+with no audio stream is refused. An audio file (`.wav`, `.mp3`, `.flac`, `.ogg`) or an
+earlier step's audio-carrying result works too.
+
+Validation and the call both refuse:
+
+- `source_video` with `latents`, or on a chained step;
+- both mask kinds, `mask_video` and `mask_image`, on one step;
+- a mask with no `source_video`, or `mask_grow` with no mask;
+- `source_video` with no `refine_strength`, or one outside (0, 1];
+- `source_video` on a pipeline that is not H3 (`mask_image` there is left alone, since
+  inpaint pipelines own that name);
+- a `source_video` whose frame count (and so the step's `num_frames`, inferred or
+  given) is off the `17n + 5` grid: the pipeline would snap the count up and pad the
+  source, and the mask would drift in time - trim or pad the clip to the grid first
+  (the message names both neighbours). An inferred `width` and `height` still have to
+  meet the family's canvas rules (multiples of 32), which the template's variables
+  carry and an inferred size does not check;
+- any of it with `refine_tiles`, which does not combine with a regional edit.
+
+### Refining above 768p in tiles
+
+H3's refine runs at 1344x768 at most, and `upscale_h3_latents` reaches 1920x1088 (or
+1088x1920). An upscale-only plate above 768p is soft: `upscale_h3_latents` to 1920x1088
+alone, decoded, is not a 1080p render, since the interpolation adds no detail. To put the
+detail in, give the refine step `refine_tiles`, which refines a `latents` canvas bigger than
+H3's own in overlapping tiles. It is a second argument to `refine_strength`, not a
+replacement: the strength, the held audio and the step count mean what they did above.
+
+```json
+{"refine_tiles": {"width": 1344, "height": 768, "overlap": 256}}
+```
+
+The step plans the tiles on the 32 px grid: the fewest per axis whose overlap is at least
+the requested one, spread evenly, the last flush to the edge. A 1920x1088 canvas at
+1344x768 with overlap 256 is 2x2, tiles at x 0 and 576, y 0 and 320. The noise is drawn
+once for the whole canvas from the step's generator and cropped per tile, so overlapping
+tiles start from the same noise. Each tile is denoised through H3 without its decode block,
+and the prompt is encoded once. The tiles are blended in latent space with a linear feather
+across each overlap, the weights normalized, and the blended canvas is decoded once with VAE
+spatial tiling. The step returns the full-canvas `latents`, the video and stage 1's held
+audio, once. Its `videos` carry H3's 24 fps, so the `pair_audio` below writes them at
+that rate without being told. The job's events show one log line per tile and one decoding phase.
+
+It is slow. A 1920x1088 refine is roughly 4 tiles, each a 768p refine, plus a 1080p decode:
+about 12 minutes measured on an RTX 3090 for 124 frames (the recipe below, with its 544p
+base), against about 9.6 minutes for a native 768p take. The validate projection prices the denoise at the tile's size and the decode at the
+canvas's, so a tiled refine does not warn as if it denoised 1080p at once.
+
+The workflow is the refine one above with four changes. The variables set the 768p turbo
+LoRA and its schedule: `"num_inference_steps": 5`, `"video_shift": 6.0` and
+`"lora_weight_name": "minimax_h3_fl2v_turbo_4step_v1.2_768p_bf16.safetensors"` (as
+`workflows/templates/minimax/video-with-audio-768p.json` does, alpha left to the file);
+`base` and `refine` both read them. `up` targets `"width": 1920, "height": 1088`. The
+`refine` step's `arguments` drop `width` and `height` and add `refine_tiles`, and it saves
+nothing itself. A `mux` step pairs the refined video with the held audio and saves it:
+
+```json
+{
+    "name": "refine",
+    "pipeline": {
+        "arguments": {
+            "prompt": "variable:prompt",
+            "num_frames": "variable:num_frames",
+            "num_inference_steps": "variable:num_inference_steps",
+            "output": ["videos", "audio", "sampling_rate"],
+            "latents": "previous_result:up",
+            "refine_strength": 0.2,
+            "refine_tiles": { "width": 1344, "height": 768, "overlap": 256 },
+            "hold_audio": "previous_result:base.audio"
+        }
+    }
+},
+{
+    "name": "mux",
+    "task": {
+        "command": "pair_audio",
+        "arguments": {
+            "video": "previous_result:refine.videos",
+            "audio": "previous_result:base.audio",
+            "sample_rate": "previous_result:base.sampling_rate",
+            "fit": "video"
         }
     },
-    "seed": "variable:seed",
-    "steps": [
-        {
-            "name": "base",
-            "pipeline": {
-                "configuration": {
-                    "component_type": "ModularPipeline",
-                    "pre_load_modules": [
-                        "sdnq"
-                    ],
-                    "load_components": {
-                        "dtype": "torch.bfloat16",
-                        "quantization_config": {
-                            "transformer": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "use_quantized_matmul": true,
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        "proj_in",
-                                        "audio_proj_in",
-                                        "context_embedder",
-                                        "time_embedder",
-                                        "time_proj",
-                                        "token_refiner",
-                                        "norm_out",
-                                        "proj_out",
-                                        "audio_proj_out"
-                                    ]
-                                }
-                            },
-                            "text_encoder": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        ".model.visual",
-                                        "lm_head"
-                                    ]
-                                }
-                            },
-                            "vae": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "{int8}",
-                                    "quant_conv": true,
-                                    "use_quantized_matmul_conv": true,
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false
-                                }
-                            }
-                        }
-                    },
-                    "components": {
-                        "transformer": {
-                            "group_offload": {
-                                "offload_type": "block_level",
-                                "num_blocks_per_group": 2,
-                                "use_stream": true,
-                                "record_stream": true,
-                                "low_cpu_mem_usage": true
-                            }
-                        },
-                        "text_encoder": {
-                            "remove_modules": [
-                                "lm_head"
-                            ]
-                        },
-                        "text_encoder.model": {
-                            "truncate_layers": {
-                                "language_model.layers": 51
-                            },
-                            "group_offload": {
-                                "offload_type": "leaf_level"
-                            }
-                        },
-                        "vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        },
-                        "audio_vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        }
-                    }
-                },
-                "from_pretrained_arguments": {
-                    "model_name": "MiniMaxAI/MiniMax-H3",
-                    "workflow": "t2va"
-                },
-                "loras": [
-                    {
-                        "model_name": "variable:lora_model_name",
-                        "weight_name": "variable:lora_weight_name",
-                        "adapter_name": "variable:lora_adapter_name",
-                        "scale": "variable:lora_scale",
-                        "alpha": "variable:lora_alpha"
-                    }
-                ],
-                "scheduler": {
-                    "shift": "variable:video_shift"
-                },
-                "audio_scheduler": {
-                    "shift": "variable:audio_shift"
-                },
-                "arguments": {
-                    "prompt": "variable:prompt",
-                    "num_frames": "variable:num_frames",
-                    "width": 960,
-                    "height": 544,
-                    "num_inference_steps": "variable:num_inference_steps",
-                    "output": [
-                        "videos",
-                        "audio",
-                        "sampling_rate",
-                        "latents"
-                    ]
-                }
-            }
-        },
-        {
-            "name": "up",
-            "task": {
-                "command": "upscale_h3_latents",
-                "arguments": {
-                    "latents": "previous_result:base.latents",
-                    "width": 1344,
-                    "height": 768
-                }
-            }
-        },
-        {
-            "name": "refine",
-            "pipeline": {
-                "configuration": {
-                    "component_type": "ModularPipeline",
-                    "pre_load_modules": [
-                        "sdnq"
-                    ],
-                    "load_components": {
-                        "dtype": "torch.bfloat16",
-                        "quantization_config": {
-                            "transformer": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "use_quantized_matmul": true,
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        "proj_in",
-                                        "audio_proj_in",
-                                        "context_embedder",
-                                        "time_embedder",
-                                        "time_proj",
-                                        "token_refiner",
-                                        "norm_out",
-                                        "proj_out",
-                                        "audio_proj_out"
-                                    ]
-                                }
-                            },
-                            "text_encoder": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        ".model.visual",
-                                        "lm_head"
-                                    ]
-                                }
-                            },
-                            "vae": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "{int8}",
-                                    "quant_conv": true,
-                                    "use_quantized_matmul_conv": true,
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false
-                                }
-                            }
-                        }
-                    },
-                    "components": {
-                        "transformer": {
-                            "group_offload": {
-                                "offload_type": "block_level",
-                                "num_blocks_per_group": 2,
-                                "use_stream": true,
-                                "record_stream": true,
-                                "low_cpu_mem_usage": true
-                            }
-                        },
-                        "text_encoder": {
-                            "remove_modules": [
-                                "lm_head"
-                            ]
-                        },
-                        "text_encoder.model": {
-                            "truncate_layers": {
-                                "language_model.layers": 51
-                            },
-                            "group_offload": {
-                                "offload_type": "leaf_level"
-                            }
-                        },
-                        "vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        },
-                        "audio_vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        }
-                    }
-                },
-                "from_pretrained_arguments": {
-                    "model_name": "MiniMaxAI/MiniMax-H3",
-                    "workflow": "t2va"
-                },
-                "loras": [
-                    {
-                        "model_name": "variable:lora_model_name",
-                        "weight_name": "variable:lora_weight_name",
-                        "adapter_name": "variable:lora_adapter_name",
-                        "scale": "variable:lora_scale",
-                        "alpha": "variable:lora_alpha"
-                    }
-                ],
-                "scheduler": {
-                    "shift": "variable:video_shift"
-                },
-                "audio_scheduler": {
-                    "shift": "variable:audio_shift"
-                },
-                "arguments": {
-                    "prompt": "variable:prompt",
-                    "num_frames": "variable:num_frames",
-                    "width": 1344,
-                    "height": 768,
-                    "num_inference_steps": 5,
-                    "output": [
-                        "videos",
-                        "audio",
-                        "sampling_rate"
-                    ],
-                    "latents": "previous_result:up",
-                    "refine_strength": 0.2,
-                    "hold_audio": "previous_result:base.audio"
-                }
-            },
-            "result": {
-                "content_type": "video/mp4",
-                "fps": 24,
-                "subfolder": "final"
-            }
-        }
-    ]
-}
-```
-
-`num_inference_steps` is the number of sigma points, ending at 0, spaced by the
-scheduler's `shift`, so a refine runs `num_inference_steps - 1` denoise evaluations. Here
-`5` points at shift 12 (the example's `video_shift`) and strength 0.2 are sigma `0.2,
-0.157, 0.110, 0.058, 0`: 4 evaluations. The time a refine takes scales with `num_inference_steps`. `refine_strength`
-sets where it starts, not how many steps run: a higher strength moves the picture further
-from the upscaled take, a lower one stays closer to it.
-
-Validation refuses a `refine_strength` that is not a number between 0 and 1 (exclusive;
-a [regional edit](#h3-regional-edit-with-source_video-and-a-mask) also takes 1.0), a
-refine with no `latents` or no `hold_audio`, a step that is not H3, and a
-`num_inference_steps` below 2.
-
-To refine a canvas above 1344x768, such as a 1920x1088 plate, see [Refining above 768p in
-tiles](#refining-above-768p-in-tiles). To give the schedule's sigma points yourself rather
-than spacing them by `shift`, see [Explicit refine sigmas](#explicit-refine-sigmas).
-
-### Explicit refine sigmas
-
-A recipe published with its own schedule cannot be reached by spacing points with
-`shift`. `refine_sigmas` gives the schedule itself: the sigma points, from
-`refine_strength` down to 0, used verbatim for both the video and audio schedules, with no
-shift applied. NVlabs' Open-H3-Regenerate-2K recipe starts from
-`(1 - 0.909375)·up + 0.909375·noise` and runs 3 Euler steps on four points:
-
-```json
-"arguments": {
-    "latents": "previous_result:up",
-    "hold_audio": "previous_result:base.audio",
-    "refine_strength": 0.909375,
-    "num_inference_steps": 4,
-    "refine_sigmas": [0.909375, 0.857143, 0.666667, 0]
-}
-```
-
-`refine_strength` still sets the re-noise, and the list replaces only the spacing. The
-step count is the number of points, so `num_inference_steps` is required beside the list
-and must equal its length: four points are 3 denoise evaluations. Validation, and the
-check before the call, refuse a `refine_sigmas` that:
-
-- is not a list of numbers, or has fewer than 2 points;
-- is not strictly decreasing;
-- does not start at `refine_strength` (within 1e-6);
-- does not end at exactly 0;
-- has a length other than `num_inference_steps`, or comes with no `num_inference_steps`;
-- comes with no `refine_strength`.
-
-A step without `refine_sigmas` runs the shift-spaced schedule as before. It works with
-`refine_tiles` too: every tile runs the same list.
-
-### H3: regional edit with source_video and a mask
-
-To change part of a take and keep the rest, pass a video as `source_video` on an H3
-`t2va`, `fl2va` or `ref2va` step. It is a path, `asset:`, `output:` or `previous_result:`
-reference to a video, and it stands in for the `latents` of a refine: the step VAE-encodes
-it over the generated rows and re-denoises from `refine_strength`, which is required and
-in (0, 1]: 1.0 regenerates the masked region from pure noise, and about 0.9 replaces an
-object in it (see [Choosing refine_strength for a regional
-edit](#choosing-refine_strength-for-a-regional-edit)). (Without a `source_video` the refine's range stays (0, 1).) With no
-mask the whole source is refined.
-
-```json
-{
-    "source_video": "asset:fox_dawn.mp4",
-    "mask_image": "asset:fox_mask.png",
-    "mask_grow": 1,
-    "refine_strength": 0.9,
-    "hold_audio": "asset:fox_dawn.mp4",
-    "prompt": "the fox holds a pine cone in its mouth, same light and motion"
-}
-```
-
-A `mask_video` (one frame per frame) or a `mask_image` (one mask for every frame) limits
-what is regenerated. Pass one, not both. White regenerates and black is kept, binarized
-at luma 128. The mask is max-pooled onto the transformer's patch grid, 16 px times the
-patch size per cell, so any white pixel marks its whole cell, and `mask_grow` widens it by
-that many cells (a whole number, 0 or more, default 1). In time the grain is the latent
-frame: about 4 pixel frames each (3.4 - every 17 frames encode to 5 latent frames), times
-the patch's time size, so a `mask_video` switching on or off mid-cell marks the whole cell. After every denoise step each
-other row is written back as the source noised to that step's sigma, with the refine's own
-noise, so at the end it is the source latent. The decode still round-trips it through the
-VAE, so kept pixels are near-identical to the source, not bit-identical. A higher
-`refine_strength` replaces what the mask covers, a lower one keeps its layout.
-
-#### Choosing refine_strength for a regional edit
-
-The useful range sits high. Measured on one take (a 960x544, 124-frame H3 shot of a woman
-holding a cup, the mask over the middle of the frame around her and the cup, `mask_grow` 1), seed 1062, 5 turbo steps,
-`video_shift` 6, with a prompt asking for a bright red raincoat:
-
-| `refine_strength` | Inside the mask | Inside-mask SSIM vs the source |
-|---|---|---|
-| 0.85 or less | A light rework: re-detailed, the source's objects and colours stay | 0.86 at 0.8, 0.83 at 0.85 |
-| 0.9 | A held object is replaced (the cup became a domed sundae cup); the same person, pose and clothing | 0.78 |
-| 0.95 | Clothing is reworked too (a hooded navy jacket) and the object replaced; face, hair and pose mostly hold | 0.65 |
-| 1.0 | A fresh generation: the prompt lands in full, but a different person and composition | 0.44 |
-
-Outside the mask held at every strength (SSIM about 0.98 on a crop outside it). So:
-0.9 to replace an object, 0.95 to rework clothing, 0.85 or less for a light rework. A large
-recolour of a big region, such as a dark top to a bright red coat, did not land below 1.0,
-and 1.0 gives up the region's identity: that is a fresh generation, not a regional edit.
-
-Why the numbers sit high: `refine_strength` is the starting sigma σ₀ on the **shifted**
-grid, the same as for any refine (and `refine_tiles`). The scheduler's shift maps the
-unshifted position u to σ = shift·u / (1 + (shift − 1)·u), so the start point on the
-unshifted schedule is u = s / (shift − (shift − 1)·s). At `video_shift` 6 that is
-0.8 → 0.40, 0.85 → 0.49, 0.9 → 0.60 and 0.95 → 0.76: a strength of 0.8 re-noises less than
-its number suggests. The bands above hold for those conditions only. Another
-`video_shift` or step count moves them, the way the formula says: a lower shift brings u
-closer to s, so the same change comes at a lower strength. A [`refine_sigmas`](#explicit-refine-sigmas)
-list is run as given, with no shift, so its points are the schedule itself.
-
-The source and the mask must match the step's `width` x `height`, and a `source_video` or
-`mask_video` its `num_frames`. A step that leaves width, height or `num_frames` out renders
-the source's. An all-black mask is refused, as it would regenerate nothing. Validation reads
-a source or mask that resolves already (a path, `asset:` or `output:`) and refuses a size,
-frame count or empty mask there, the way it reads guide videos; a `previous_result:` is
-checked at the call.
-
-`hold_audio` is still required with `refine_strength`, since the refine holds the
-soundtrack. It is normally the `source_video` itself: a video path (`.mp4`, `.mov`, `.mkv`,
-`.webm`, `.avi`) is held by its own soundtrack, so the edit keeps the take's audio. A video
-with no audio stream is refused. An audio file (`.wav`, `.mp3`, `.flac`, `.ogg`) or an
-earlier step's audio-carrying result works too.
-
-Validation and the call both refuse:
-
-- `source_video` with `latents`, or on a chained step;
-- both mask kinds, `mask_video` and `mask_image`, on one step;
-- a mask with no `source_video`, or `mask_grow` with no mask;
-- `source_video` with no `refine_strength`, or one outside (0, 1];
-- `source_video` on a pipeline that is not H3 (`mask_image` there is left alone, since
-  inpaint pipelines own that name);
-- a `source_video` whose frame count (and so the step's `num_frames`, inferred or
-  given) is off the `17n + 5` grid: the pipeline would snap the count up and pad the
-  source, and the mask would drift in time - trim or pad the clip to the grid first
-  (the message names both neighbours). An inferred `width` and `height` still have to
-  meet the family's canvas rules (multiples of 32), which the template's variables
-  carry and an inferred size does not check;
-- any of it with `refine_tiles`, which does not combine with a regional edit.
-
-### Refining above 768p in tiles
-
-H3's refine runs at 1344x768 at most, and `upscale_h3_latents` now reaches 1920x1088 (or
-1088x1920). An upscale-only plate above 768p is soft: `upscale_h3_latents` to 1920x1088
-alone, decoded, is not a 1080p render, since the interpolation adds no detail. To put the
-detail in, give the refine step `refine_tiles`, which refines a `latents` canvas bigger than
-H3's own in overlapping tiles. It is a second argument to `refine_strength`, not a
-replacement: the strength, the held audio and the step count mean what they did above.
-
-```json
-{"refine_tiles": {"width": 1344, "height": 768, "overlap": 256}}
-```
-
-The step plans the tiles on the 32 px grid: the fewest per axis whose overlap is at least
-the requested one, spread evenly, the last flush to the edge. A 1920x1088 canvas at
-1344x768 with overlap 256 is 2x2, tiles at x 0 and 576, y 0 and 320. The noise is drawn
-once for the whole canvas from the step's generator and cropped per tile, so overlapping
-tiles start from the same noise. Each tile is denoised through H3 without its decode block,
-and the prompt is encoded once. The tiles are blended in latent space with a linear feather
-across each overlap, the weights normalized, and the blended canvas is decoded once with VAE
-spatial tiling. The step returns the full-canvas `latents`, the video and stage 1's held
-audio, once. Its `videos` carry H3's 24 fps, so the `pair_audio` below writes them at
-that rate without being told. The job's events show one log line per tile and one decoding phase.
-
-It is slow. A 1920x1088 refine is roughly 4 tiles, each a 768p refine, plus a 1080p decode:
-about 12 minutes measured on an RTX 3090 for 124 frames (the recipe below, with its 544p
-base), against about 9.6 minutes for a native 768p take. The validate projection prices the denoise at the tile's size and the decode at the
-canvas's, so a tiled refine does not warn as if it denoised 1080p at once.
-
-```json
-{
-    "id": "H3LatentUpscaleRefineTiles",
-    "description": "Promote a MiniMax-H3 take from 960x544 to a 1920x1088 canvas in latent space, refine it in overlapping 1344x768 tiles, and pair the held audio.",
-    "variables": {
-        "prompt": "prompt:minimax/fox_dawn_context_ir",
-        "num_frames": 124,
-        "num_inference_steps": 5,
-        "video_shift": 6.0,
-        "audio_shift": 3.0,
-        "weights_dtype": "{int4}",
-        "lora_scale": 1.0,
-        "lora_alpha": null,
-        "lora_model_name": "lightx2v/Minimax-h3-Turbo",
-        "lora_weight_name": "minimax_h3_fl2v_turbo_4step_v1.2_768p_bf16.safetensors",
-        "lora_adapter_name": "turbo",
-        "seed": 42
-    },
-    "variable_constraints": {
-        "num_frames": {
-            "modulus": 17,
-            "remainder": 5,
-            "reason": "the video VAE encodes 17 * n + 5 frames"
-        }
-    },
-    "seed": "variable:seed",
-    "steps": [
-        {
-            "name": "base",
-            "pipeline": {
-                "configuration": {
-                    "component_type": "ModularPipeline",
-                    "pre_load_modules": [
-                        "sdnq"
-                    ],
-                    "load_components": {
-                        "dtype": "torch.bfloat16",
-                        "quantization_config": {
-                            "transformer": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "use_quantized_matmul": true,
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        "proj_in",
-                                        "audio_proj_in",
-                                        "context_embedder",
-                                        "time_embedder",
-                                        "time_proj",
-                                        "token_refiner",
-                                        "norm_out",
-                                        "proj_out",
-                                        "audio_proj_out"
-                                    ]
-                                }
-                            },
-                            "text_encoder": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        ".model.visual",
-                                        "lm_head"
-                                    ]
-                                }
-                            },
-                            "vae": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "{int8}",
-                                    "quant_conv": true,
-                                    "use_quantized_matmul_conv": true,
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false
-                                }
-                            }
-                        }
-                    },
-                    "components": {
-                        "transformer": {
-                            "group_offload": {
-                                "offload_type": "block_level",
-                                "num_blocks_per_group": 2,
-                                "use_stream": true,
-                                "record_stream": true,
-                                "low_cpu_mem_usage": true
-                            }
-                        },
-                        "text_encoder": {
-                            "remove_modules": [
-                                "lm_head"
-                            ]
-                        },
-                        "text_encoder.model": {
-                            "truncate_layers": {
-                                "language_model.layers": 51
-                            },
-                            "group_offload": {
-                                "offload_type": "leaf_level"
-                            }
-                        },
-                        "vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        },
-                        "audio_vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        }
-                    }
-                },
-                "from_pretrained_arguments": {
-                    "model_name": "MiniMaxAI/MiniMax-H3",
-                    "workflow": "t2va"
-                },
-                "loras": [
-                    {
-                        "model_name": "variable:lora_model_name",
-                        "weight_name": "variable:lora_weight_name",
-                        "adapter_name": "variable:lora_adapter_name",
-                        "scale": "variable:lora_scale",
-                        "alpha": "variable:lora_alpha"
-                    }
-                ],
-                "scheduler": {
-                    "shift": "variable:video_shift"
-                },
-                "audio_scheduler": {
-                    "shift": "variable:audio_shift"
-                },
-                "arguments": {
-                    "prompt": "variable:prompt",
-                    "num_frames": "variable:num_frames",
-                    "width": 960,
-                    "height": 544,
-                    "num_inference_steps": "variable:num_inference_steps",
-                    "output": [
-                        "videos",
-                        "audio",
-                        "sampling_rate",
-                        "latents"
-                    ]
-                }
-            }
-        },
-        {
-            "name": "up",
-            "task": {
-                "command": "upscale_h3_latents",
-                "arguments": {
-                    "latents": "previous_result:base.latents",
-                    "width": 1920,
-                    "height": 1088
-                }
-            }
-        },
-        {
-            "name": "refine",
-            "pipeline": {
-                "configuration": {
-                    "component_type": "ModularPipeline",
-                    "pre_load_modules": [
-                        "sdnq"
-                    ],
-                    "load_components": {
-                        "dtype": "torch.bfloat16",
-                        "quantization_config": {
-                            "transformer": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "use_quantized_matmul": true,
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        "proj_in",
-                                        "audio_proj_in",
-                                        "context_embedder",
-                                        "time_embedder",
-                                        "time_proj",
-                                        "token_refiner",
-                                        "norm_out",
-                                        "proj_out",
-                                        "audio_proj_out"
-                                    ]
-                                }
-                            },
-                            "text_encoder": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "variable:weights_dtype",
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false,
-                                    "modules_to_not_convert": [
-                                        ".model.visual",
-                                        "lm_head"
-                                    ]
-                                }
-                            },
-                            "vae": {
-                                "configuration": {
-                                    "config_type": "sdnq.SDNQConfig"
-                                },
-                                "arguments": {
-                                    "weights_dtype": "{int8}",
-                                    "quant_conv": true,
-                                    "use_quantized_matmul_conv": true,
-                                    "quantization_device": "cuda",
-                                    "return_device": "cpu",
-                                    "dequantize_fp32": false
-                                }
-                            }
-                        }
-                    },
-                    "components": {
-                        "transformer": {
-                            "group_offload": {
-                                "offload_type": "block_level",
-                                "num_blocks_per_group": 2,
-                                "use_stream": true,
-                                "record_stream": true,
-                                "low_cpu_mem_usage": true
-                            }
-                        },
-                        "text_encoder": {
-                            "remove_modules": [
-                                "lm_head"
-                            ]
-                        },
-                        "text_encoder.model": {
-                            "truncate_layers": {
-                                "language_model.layers": 51
-                            },
-                            "group_offload": {
-                                "offload_type": "leaf_level"
-                            }
-                        },
-                        "vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        },
-                        "audio_vae": {
-                            "device": "cuda",
-                            "residency": "on_demand"
-                        }
-                    }
-                },
-                "from_pretrained_arguments": {
-                    "model_name": "MiniMaxAI/MiniMax-H3",
-                    "workflow": "t2va"
-                },
-                "loras": [
-                    {
-                        "model_name": "variable:lora_model_name",
-                        "weight_name": "variable:lora_weight_name",
-                        "adapter_name": "variable:lora_adapter_name",
-                        "scale": "variable:lora_scale",
-                        "alpha": "variable:lora_alpha"
-                    }
-                ],
-                "scheduler": {
-                    "shift": "variable:video_shift"
-                },
-                "audio_scheduler": {
-                    "shift": "variable:audio_shift"
-                },
-                "arguments": {
-                    "prompt": "variable:prompt",
-                    "num_frames": "variable:num_frames",
-                    "num_inference_steps": "variable:num_inference_steps",
-                    "output": [
-                        "videos",
-                        "audio",
-                        "sampling_rate"
-                    ],
-                    "latents": "previous_result:up",
-                    "refine_strength": 0.2,
-                    "refine_tiles": {
-                        "width": 1344,
-                        "height": 768,
-                        "overlap": 256
-                    },
-                    "hold_audio": "previous_result:base.audio"
-                }
-            }
-        },
-        {
-            "name": "mux",
-            "task": {
-                "command": "pair_audio",
-                "arguments": {
-                    "video": "previous_result:refine.videos",
-                    "audio": "previous_result:base.audio",
-                    "sample_rate": "previous_result:base.sampling_rate",
-                    "fit": "video"
-                }
-            },
-            "result": {
-                "content_type": "video/mp4",
-                "fps": 24,
-                "subfolder": "final"
-            }
-        }
-    ]
+    "result": { "content_type": "video/mp4", "fps": 24, "subfolder": "final" }
 }
 ```
 
-The refine step uses the 768p turbo LoRA with its own schedule (`video_shift` 6, `audio_shift`
-3, alpha left to the file), as `workflows/templates/minimax/video-with-audio-768p.json` does. It
-carries no `width` or `height`: the canvas is the `latents`. Naming them is allowed, and must
-equal the canvas, which is the latent height x width x 16.
+The refine step carries no `width` or `height`: the canvas is the `latents`. Naming them is
+allowed, and they must equal the canvas, which is the latent height x width x 16.
 
 Validation and the run refuse, naming `refine_tiles`:
 
@@ -3162,6 +2475,13 @@ each already names something pinned by the asset library or by the manifest's
 SHA-256 recorded in the manifest. The manifest also lists which stored prompts
 were inlined, since inlining loses the name.
 
+The file is a valid workflow: `python -m dw.run workflow.json`, or its contents as
+`run_workflow`'s `inline_workflow`, runs it again. Its `asset:` and `output:` names
+resolve against the server's or CLI's own libraries, not the run directory, so it
+reproduces the run only where those libraries are the ones the original run used.
+Writing it is best effort, like the manifest, and `--output-layout flat` writes no run
+directory, so neither file.
+
 A step that joins shots (`concat_videos`, `dissolve_videos`, or a pipeline
 step with a `chain`) also records where each one landed, as `shots` on its
 manifest entry (and on its `step_end` event):
@@ -3205,15 +2525,6 @@ say, or a video read back from a file. `get_gallery_metadata` reports the
 recorded shots as `media.shots`, and `get_output_frames(seams=true)` uses them
 when you pass no `boundaries`.
 
-The file is a valid workflow, and running it again is `python -m dw.run
-workflow.json` or handing its contents to `run_workflow` as `inline_workflow`
-— but either way the `asset:` and `output:` names in it resolve against the
-server's or CLI's own libraries, not against the run directory, so doing this
-from inside that directory reproduces the run only when its libraries are the
-ones the original run used too. Writing the file is best effort, exactly like
-the manifest — a run that produced its files has succeeded either way — and
-`--output-layout flat` writes no run directory, so it writes neither file.
-
 Any of the three levels accepts a `variable:` reference, which is how a seed becomes
 settable per run without editing the file:
 
@@ -3240,25 +2551,19 @@ not reproduce from the seed it was given.
 
 The step cache that lets a reproduced step skip re-running (see *Runs* in
 [Workspaces](WORKSPACES.md#runs)) is scoped to the output directory a run
-writes into, which on `dw.serve` is the pinned workspace's own `outputs/`.
-Two workspaces holding what looks like the same prior run - same workflow,
-same seed, same arguments - do not share a cache entry, so
-`validate_workflow`'s `plan.cached_steps` answers for the workspace the call
-is pinned to, not for every workspace that happens to hold a matching run.
-Deleting a workspace takes its cache entries with it, the same as deleting
-its `outputs/` directory would.
-
-The step cache is also per card and per workflow identity. Identity is the stored
-workflow's name, or an inline workflow's `id`; entries are keyed on the definition `id`
-and step name (`StepCache.get` in `dw/step_cache.py`), and an inline job's identity is
-`("inline", id)` (`workflow_identity` in `dw/worker_protocol.py`). Switching identity on a
-card empties that card's cache (the worker's `_cleanup_all` calls `step_cache.clear()`),
-so:
+writes into, which on `dw.serve` is the workspace's own `outputs/`. Two workspaces
+holding the same prior run do not share a cache entry, so `validate_workflow`'s
+`plan.cached_steps` answers for the workspace the call is pinned to. Deleting a
+workspace takes its cache entries with it.
+
+The step cache is also per card and per workflow identity: the stored workflow's path,
+or an inline workflow's `id` (`workflow_identity` in `dw/worker_protocol.py`). Switching
+identity on a card empties that card's step cache (the worker's `_cleanup_all`), so:
 
 - running any other workflow on the card in between starts the next run cold: A, then B,
-  then A runs the second A cold. This is the common case. On a multi-card server
-  warm-identity routing (`choose_slot` in `dw/server/pool.py`) mostly avoids it unless
-  that card is busy; on a single card it happens every time.
+  then A runs the second A cold. On a multi-card server warm-identity routing
+  (`choose_slot` in `dw/server/pool.py`) mostly avoids it unless that card is busy; on a
+  single card it happens every time.
 - a renamed copy, a new inline `id`, or switching between a stored workflow and its
   inline copy also starts cold.
 
@@ -3270,16 +2575,15 @@ step reads re-runs that step. To check before running, `validate_workflow`'s
 
 ## Type System
 
-Dynamic type conversion applies to certain values:
+- Keys ending in `_type` or `_dtype`, or named `dtype`: `"torch.bfloat16"` becomes
+  `torch.bfloat16`, and `"sdnq.SDNQConfig"` loads the class by import.
+- Braces keep a value a string: `"{nf4}"` stays `"nf4"`.
+- `content_type` and `offload_type` always stay strings.
+- `constant:` values are read from Python rather than copied into the workflow
+  (below).
 
-- Keys ending in `_type` or `_dtype`, or named `dtype`: `"torch.bfloat16"` becomes `torch.bfloat16`
-- Dotted names: `"sdnq.SDNQConfig"` loads the class via importlib
-- Escape with braces to keep as string: `"{nf4}"` stays as `"nf4"`
-- `content_type` and `offload_type` are exempt even though they end in `_type` - they
-  name a category, not a Python type, so their value always stays a plain string (the
-  `{}` escape is accepted but not required for these two keys)
-- Values prefixed with `constant:` are read from python rather than copied into the
-  workflow: `"constant:diffusers.pipelines.ltx2.utils.DISTILLED_SIGMA_VALUES"`
+The full rule, including what an untrusted server refuses, is in
+[Types and escaping](#types-and-escaping).
 
 ### Constant References
 
@@ -3307,15 +2611,11 @@ is declared by its default:
 "variables": { "negative_prompt": "constant:diffusers.pipelines.ltx2.utils.DEFAULT_NEGATIVE_PROMPT" }
 ```
 
-A constant is data. The name has to resolve to a value - anything callable is refused,
-because a type is named with a `*_type` argument and constructed there, and reaching a
-function this way would be evaluating python rather than referencing it. Mutable values
-are copied, so a pipeline that consumes its schedule in place cannot edit the library's
-constant for the rest of the session.
-
-The value the library declares is the value the workflow gets, which is the point: a
-constant that changes upstream changes here, and one that is renamed or moved fails
-loudly rather than leaving a stale copy behind.
+A constant is data: anything callable is refused (a type is named with a `*_type` key
+instead). Mutable values are copied, so a pipeline that consumes its schedule in place
+cannot edit the library's constant for the rest of the session. A constant that changes
+upstream changes here, and one that is renamed or moved fails the run rather than
+leaving a stale copy behind.
 
 ### Prompt References
 
@@ -3348,21 +2648,18 @@ the server's prompt enhancer uses it to preselect a preset. One spelling per fam
 beside `minimax-music3` hides half a shelf, and `tests/test_prompt_library.py` sweeps
 the repo's library for a variant.
 
-The library's location is resolved in order: the `DW_PROMPT_DIR` environment
-variable (which `--prompt-dir` on `dw.serve` sets), then
-`./prompts` in the working directory when it exists, then the first `prompts/`
-folder found walking up from the workflow file's own directory - which is how
-a repo workflow run from any working directory still reaches the library beside
-it. `dw.serve` resolves the directory once at startup with this same order
-(anchored at its workflow directory) and pins it for every job, so the Prompts
-page and `prompt:` resolution always agree on one library.
-References are rooted at that one directory - not at the workflow file - so
-the same reference means the same text from every workflow. Like `constant:`, a reference
-resolves anywhere in a workflow's arguments, including a `variables` default, and it
-always resolves to exactly one string: it never multiplies a step's iterations the way
-`previous_result:` references do. A prompt's text may not itself begin with a
-reference prefix such as `variable:` - the engine refuses it rather than resolving
-text as syntax.
+The library's location is resolved in order (`discover_library` in `dw/workspace.py`):
+the `DW_PROMPT_DIR` environment variable (which `--prompt-dir` on `dw.serve` sets); a
+workspace named explicitly (`--workspace`, `DW_WORKSPACE` or the setting), whose
+`prompts/` it is; `./prompts` in the working directory when it exists; the first
+`prompts/` folder found walking up from the workflow file's own directory; and last the
+workspace's `prompts/`. `dw.serve` resolves it once at startup and pins it for every
+job, so the Prompts page and `prompt:` resolution agree on one library. References are
+rooted at that directory, not at the workflow file, so the same reference means the same
+text from every workflow. A reference resolves anywhere in a workflow's arguments,
+including a `variables` default, always to exactly one string: it never multiplies a
+step's iterations. A prompt's text may not itself begin with a reference prefix such as
+`variable:`.
 
 ### Asset References
 
@@ -3383,15 +2680,13 @@ so a workflow and the media it reads do not have to live in the same place:
 ```
 
 A reference names a file with its extension, at most four folders deep, and resolves to
-that file's path - so it works under any argument that accepts a path: `image`, `video`,
-a `from_file`, a list of any of them, or a task argument that names a file. What loads
-the path is unchanged; only where the path comes from is.
+that file's path, so it works under any argument that accepts a path: `image`, `video`,
+a `from_file`, a list of any of them, or a task argument that names a file.
 
-The library's location is resolved in order: the `DW_ASSET_DIR` environment variable
-(which `--asset-dir` on `dw.serve` sets), then the workspace's
-`assets/` when a workspace was named explicitly, then `./assets` in the working
-directory when it exists, then the first `assets/` folder found walking up from the
-workflow file's own directory. See [Workspaces](WORKSPACES.md).
+On `dw.serve` each job reads the asset library of the workspace it runs in. Elsewhere
+the location is resolved in the prompt library's order, with `DW_ASSET_DIR`
+(`--asset-dir`) and `assets/` in place of `DW_PROMPT_DIR` and `prompts/`. See
+[Workspaces](WORKSPACES.md).
 
 A reference can only name a file inside the library: `..`, an absolute path, or a
 symlink pointing out of it are all refused. Browser uploads land in the library's
@@ -3400,9 +2695,8 @@ an upload still resolves on the next run.
 
 ### Output References
 
-Multi-stage work — generate stills, then animate them; generate a score, then mux it —
-used to mean copying files out of the output directory and back in beside the next
-workflow. An `output:` reference names what an earlier run wrote, directly:
+For multi-stage work (generate stills, then animate them in another run), an `output:`
+reference names what an earlier run wrote:
 
 ```json
 "image": "output:ltx2/Gyre/latest/Gyre-still.0-0.0.png",
@@ -3411,17 +2705,13 @@ workflow. An `output:` reference names what an earlier run wrote, directly:
 
 The name is a path under the output directory — the workflow's identity, the run, and
 the file (see [Runs](WORKSPACES.md#runs)). Writing `latest` where the run id goes
-resolves to the newest run of that workflow *that holds the file*, which is what lets a
-second-stage workflow name the first stage's product without being edited after every
-run - and keeps working when the newest run failed part way, or reused every step from
-the cache and so wrote nothing of its own but a manifest. Runs sort by their id, which
-starts with a UTC timestamp, so "newest" needs no file timestamps and survives a
-directory being copied. `v<N>` in the same position names the run whose version is N -
-the `v4` the gallery labels its files with - so the number a person was told is a name
-a workflow can take. Unlike `latest` it picks exactly one run: `v4` not holding the file
-is an error, not a reason to try `v3`. `latest` and `v<N>` only select a run where run
-directories are; a workflow or file that happens to be called either is still named as
-itself.
+resolves to the newest run of that workflow *that holds the file*, so it keeps working
+when the newest run failed part way or reused every step from the cache and wrote only
+a manifest. Runs sort by their id, which starts with a UTC timestamp. `v<N>` in the same
+position names the run whose version is N (the `v4` the gallery labels its files with).
+Unlike `latest` it picks exactly one run: `v4` not holding the file is an error, not a
+reason to try `v3`. `latest` and `v<N>` select a run only in the run-id position; a
+workflow or file that happens to be called either is still named as itself.
 
 Like `asset:`, a reference resolves to a path and then whatever loads paths loads it, so
 it works under `image`, `video`, a `from_file`, or a list of them. The audio tasks take
@@ -3617,16 +2907,11 @@ rate of their own. A mono track needs no preparation: an mp4 audio stream
 takes stereo and nothing else, so saving duplicates the single channel into
 two and emits a warning saying it did.
 
-The track and the frames are two lengths a workflow used to have to keep equal by
-hand. `"fit": "video"` derives one from the other instead: the track is cut to
-exactly the frames it is laid over, or padded with silence and warned about when it
-is shorter than they are. That is what a soundtrack over a cut whose length is an
-argument needs - nothing in a workflow can multiply a list's length by a frame
-count, so `music-video.json` sliced a fixed 496 frames of song (it now cuts
-each shot to its own `cut_frames`) while its cut
-followed a `shots` list, and a two-shot run wrote 10.3 s of picture into a 20.7 s
-container and reported `succeeded` with no warnings (#142). Left unset the track is
-used as it is and a disagreement is warned about rather than passing in silence.
+`"fit": "video"` makes the track fit the frames: it is cut to exactly the frames it is
+laid over, or padded with silence and warned about when it is shorter. Use it for a
+soundtrack under a cut whose length is an argument (a `shots` list), since nothing in a
+workflow can multiply a list's length by a frame count (#142). Left unset, the track is
+used as it is and a length mismatch is warned about.
 
 Which shape a pipeline argument wants is the pipeline's business, and the two LTX-2
 paths differ: a keyframe condition is mapped from 0-255, so it takes the `video_frames`
diff --git a/docs/WORKSPACES.md b/docs/WORKSPACES.md
index 4bfdf31d..740aecc7 100644
--- a/docs/WORKSPACES.md
+++ b/docs/WORKSPACES.md
@@ -12,10 +12,9 @@ they generate.
   outputs/      generated files
 ```
 
-This exists so that day-to-day work does not have to live inside a checkout of
-this repository. The examples under the repo's `workflows/` are still examples
-— a corpus to read, copy and run — but they are not where your own workflows
-belong, and generated media does not belong in a source tree at all.
+It keeps day-to-day work out of a checkout of this repository: the repo's
+`workflows/` is a corpus of examples to read, copy and run, and generated
+media does not belong in a source tree.
 
 ## Which directory is used
 
@@ -29,14 +28,13 @@ the server's workspace. First match wins:
 4. the working directory, when it holds any of `workflows/`, `prompts/` or `outputs/`
 5. `~/diffusers-workspace`
 
-Rule 4 is why nothing changes when you work from a checkout: the repository
-root holds all three, so it resolves to itself and every default lands exactly
-where it always has. Only a working directory with none of those folders falls
-through to the home workspace.
+Rule 4 means a checkout serves itself: the repository root holds all three
+folders. Rules 1-3 are *explicit*; rules 4 and 5 are inferred, which matters
+for the prompt library (below).
 
-Nothing is created just by resolving. A command that is about to write creates
-what it needs — `dw.serve` creates the workspace's `workflows/` so the UI has
-somewhere to save, and a run creates its output directory.
+Resolving creates nothing. `dw.serve` creates the workspace's `workflows/`
+(unless `--workflow-dir` was given) and `assets/` at start, and a run creates
+its output directory.
 
 `dw.run --workspace NAME` is a different thing: it names one of the server's
 workspaces (the `default` one, or a named one beside it), not a directory. A
@@ -44,19 +42,16 @@ path there is refused as a workspace name rather than resolved.
 
 ## Overriding one folder
 
-The per-directory flags are `dw.serve`'s, and each overrides exactly one
-folder of the workspace:
+`--workflow-dir`, `--prompt-dir`, `--asset-dir` and `--output-dir` on
+`dw.serve` each override one folder of the workspace. `dw.run` takes none of
+them; every job it queues writes where the server does.
 
 ```bash
 python -m dw.serve --workspace ~/studio --output-dir /mnt/big-disk/renders
 python -m dw.serve --prompt-dir ~/shared-prompts
 ```
 
-`dw.run` takes none of them - every job it queues writes where the server
-does.
-
-`--output-dir` is the one people reach for most: video work fills disks, and
-the outputs folder is the one worth putting on another volume.
+`--output-dir` is the usual one: video work fills disks.
 
 ## Working in a workspace
 
@@ -86,26 +81,28 @@ The server reports what it resolved at `GET /api/server`, under
 
 ## The prompt library
 
-`prompt:` references resolve to the workspace's `prompts/` when a workspace was
-named explicitly (rules 1–3 above, or `DW_PROMPT_DIR`, which still wins over
-everything). A workspace that was merely inferred from the working directory
-does not preempt the older discovery — `./prompts`, then the nearest `prompts/`
-above the workflow file — so a repository workflow keeps reaching the library
-it lives beside. When the workspace is explicit, its `prompts/` becomes the library
-even if it does not exist yet, so a checkout's `./prompts` is no longer found once
-a standing workspace setting (like `DW_WORKSPACE` or `"workspace"` in settings.json)
-is in place; `--prompt-dir` and `DW_PROMPT_DIR` still override it. This follows
-the "explicit wins" rule. See [Prompt References](WORKFLOW_GUIDE.md#prompt-references).
+`dw.serve` resolves the prompt library once at start and pins it for every
+job, first match wins:
+
+1. `--prompt-dir`, then `DW_PROMPT_DIR`
+2. the workspace's `prompts/`, when the workspace is explicit (rules 1-3),
+   even if it does not exist yet
+3. `./prompts` in the working directory
+4. the nearest `prompts/` at or above the workflow directory
+5. the workspace's `prompts/`
+
+So a standing `DW_WORKSPACE` or `"workspace"` setting stops a checkout's
+`./prompts` being found; an inferred workspace does not. See
+[Prompt References](WORKFLOW_GUIDE.md#prompt-references).
 
 ## Assets
 
 `assets/` is the input-media library. A workflow argument written as
 `asset:name.ext` (or `asset:folder/name.ext`) resolves to that file's path,
-rooted at the library rather than at the workflow file — so a workflow and the
-media it reads no longer have to sit in the same folder. `--asset-dir` and
-`DW_ASSET_DIR` override the folder, and browser uploads land in
-`assets/uploads/`, coming back as `asset:uploads/<name>` (and served for
-preview under `/inputs/`, since the SPA's own bundles own `/assets/`).
+rooted at the library rather than at the workflow file. `--asset-dir`
+overrides the folder. Browser uploads land in `assets/uploads/` and come back
+as `asset:uploads/<name>`, served for preview under `/inputs/` (the UI's own
+bundles own `/assets/`).
 
 A generated file becomes an input the same way: **Keep as asset** in the
 gallery (`POST /api/assets/keep`, `keep_output` over MCP) links or copies it
@@ -175,9 +172,9 @@ deleting a prompt that came from a read-only library is refused with a 403,
 and saving one writes a copy into your workspace the way saving an example
 workflow does.
 
-The packaged workflows in `dw/workflows/` are deliberately *not* on the path.
-They are the pieces a `builtin:` sub-workflow step names, resolved by the
-engine where that step is read — not workflows to browse or run on their own.
+The packaged workflows in `dw/workflows/` are *not* on the path: they are
+what a `builtin:` sub-workflow step names (`GET /api/builtin-workflows` lists
+them), not workflows to browse or run on their own.
 
 ## Runs
 
@@ -312,29 +309,22 @@ it is working in - `dw.run --workspace NAME` among them.
     workflows/  assets/  outputs/  <- the 'scratch' workspace
 ```
 
-The root's own three folders are the workspace named `default`, so a server
-that has never heard of named workspaces behaves exactly as it did. A named
-workspace is a sibling directory holding the same three folders — and *not* a
-`prompts/`, because there is one prompt library: `prompt:` is shared by
-reference, and a prompt duplicated per workspace would resolve to different
-text depending on where a workflow happened to be saved. `workflows`,
-`prompts`, `assets` and `outputs` are reserved names for that reason.
-
-Three more names are reserved beside `workflows`, `prompts`, `assets` and
-`outputs`: `exports`, `common` and `loras`. `loras/` is the LoRA catalog,
-shared by every workspace like `prompts/`; a workspace already named `loras`
-stops being listed on upgrade, and the server logs a warning at start naming
-its directory. `POST /api/jobs/{id}/export` gathers one
-finished job into `<root>/exports/<job id>/`, and that folder is never mistaken
-for a workspace.
-
-**The shared asset library.** `common/assets/` is the one place an asset can
-live that belongs to no single workspace. Assets are otherwise per workspace,
-which is right for the inputs of one piece of work and wrong for a recurring
-cast: a character's portrait and voice clip uploaded while making episode one
-were invisible from the workspace episode four was made in, and the only way
-through was to copy the files in. It sits on every workspace's asset search
-path behind that workspace's own library, so:
+The root's own three folders are the workspace named `default`. A named
+workspace is a sibling directory holding the same three folders and no
+`prompts/`: there is one prompt library, because a `prompt:` reference
+duplicated per workspace would resolve to different text depending on where
+a workflow was saved.
+
+Reserved names, which no workspace can take: `workflows`, `prompts`,
+`assets`, `outputs`, `exports` (where `POST /api/jobs/{id}/export` gathers a
+job, `<root>/exports/<job id>/`), `common` and `loras` (the LoRA catalog,
+shared like `prompts/`). A pre-existing workspace named `loras` stops being
+listed, and the server logs a warning at start naming its directory.
+
+**The shared asset library.** `common/assets/` holds assets that belong to
+no single workspace, such as a recurring cast's portraits and voice clips.
+It sits on every workspace's asset search path behind that workspace's own
+library, so:
 
 - `asset:cast/priya.png` resolves in the workspace first, then in the shared
   library, then in any read-only examples library — a workspace's own name
@@ -349,21 +339,20 @@ path behind that workspace's own library, so:
 It holds assets only. A prompt is already shared, and workflows and outputs
 belong to the work that made them.
 
-This is what lets two agents share the server's GPUs without sharing a
-namespace: each takes a workspace, and neither can save over the other's
-workflows or delete the other's renders. The queue is shared across
-workspaces and the engine runs one job per GPU (`dw.serve --devices`), so two
-agents' jobs run side by side when there is a free card each fits, and queue
-otherwise.
+Two agents can share the server's GPUs without sharing a namespace: each
+takes a workspace, and neither can save over the other's workflows or delete
+the other's renders. The queue is shared and the engine runs one job per GPU
+(`dw.serve --devices`), so their jobs run side by side when each fits a free
+card, and queue otherwise.
 
 **How a client picks one.** Every scoped route takes an optional
-`?workspace=<name>`; omitting it means `default`, which is why every
-pre-workspace call still means what it meant.
+`?workspace=<name>`; omitting it means `default`. `GET /api/jobs` spans every
+workspace unless one is named.
 
 | Client | How |
 | --- | --- |
 | Web UI | The sidebar lists every workspace; the selected one is named in the hash (`#/ws/<name>/...`), so a link and a reload both land where they say. The choice is remembered in `localStorage` as a fallback for a route that names none (Shared, Server), and Server → Status adds a filter over the all-workspaces queue — job history spans every workspace there and says which one each job ran in |
-| MCP | `list_workspaces`, then `use_workspace(name)`. It is a session default rather than an argument on each call, so switching is one visible step in the transcript instead of a flag that can be forgotten on the call where it mattered |
+| MCP | `list_workspaces`, then `use_workspace(name)`: a session default, so switching is one visible step in the transcript. Most tools also take `workspace=` per call; over a shared `/mcp` mount see [REMOTE.md](REMOTE.md#claude-code-no-local-install) |
 | HTTP | `?workspace=` on the route, or `"workspace"` in a `POST /api/jobs` body |
 | Web UI (create/delete) | The sidebar's `+ new` creates one; a workspace's own Overview page deletes it (disabled for `default`) |
 
@@ -373,7 +362,8 @@ while it runs — including through a rerun, and when its files are served back
 from history.
 
 **Creating and deleting.** `POST /api/workspaces` (`create_workspace` over
-MCP) makes one; creating does not switch to it. Deleting removes everything in
+MCP) makes one; creating does not switch to it (over MCP, unless
+`use=true`). Deleting removes everything in
 it, so it refuses until acknowledged and answers first with what it would
 remove — file counts and bytes per folder. The default workspace cannot be
 deleted (it holds the shared prompt library, and there has to be somewhere to
@@ -468,7 +458,7 @@ dw already has.
    that clips notes still shows which variant is which. Call `wait_for_job`
    until the variant finishes before submitting the next. The server holds a
    second large variant until the host can hold it beside the first (see
-   [SERVER.md](SERVER.md)), so waiting is for ordering, not for memory.
+   [SERVER.md](SERVER.md#jobs-api)), so waiting is for ordering, not for memory.
 
 5. **Only the latest run of a step is cached.** The step cache holds one
    entry per workflow step: its most recent run. A variant whose step and
@@ -501,16 +491,3 @@ dw already has.
 8. **Keep the winner.** `keep_output` the winning file under a stable
    `asset:` name, and put the conclusion (the answer, and the variant that
    gave it) in the workspace note.
-
-## Where this is going
-
-The resolver, the workflow search path with writes confined to the writable
-root, run directories with an on-disk manifest, `asset:` and `output:`
-references, and server-side named workspaces are all implemented. A further
-stage was designed but deliberately not built: a client-side workspace (a
-laptop directory, under version control) that mirrors into a read-only
-server workspace, so an agent could author offline and only push at submit
-time. It stayed on the drawing board because source control of creative work
-is not this project's job — that is already handled on the client, by the
-user, with the tools they already use — which is what makes a mirroring
-layer unnecessary rather than merely speculative.
diff --git a/docs/img/ui-caches-dark.png b/docs/img/ui-caches-dark.png
new file mode 100644
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YBQmz9bIxW};rtiOHJEc%WVY*n0AZOp^#A|>

literal 0
HcmV?d00001

diff --git a/plugins/dw/README.md b/plugins/dw/README.md
index f364e7c6..36bbf06e 100644
--- a/plugins/dw/README.md
+++ b/plugins/dw/README.md
@@ -2,9 +2,10 @@
 
 Skills for Claude Code that teach an agent to compose a model family's
 workflows on a [diffusers-workflow](https://github.com/dkackman/diffusers-workflow)
-server. They assume the `dw` MCP server is already registered
-(see the repo README, "Drive it from an agent"); a skill's first move is
-`get_server_info`.
+server. They assume the `dw` MCP server is already registered (see the repo
+README's "Getting started" and [docs/MCP.md](../../docs/MCP.md)); a skill's
+first move is `get_server_info`. Five skills cover one model family each; the
+last four are cross-cutting shapes that orchestrate them.
 
 Install once, from Claude Code:
 
@@ -40,13 +41,16 @@ Name the skill: the H3 repo also ships eight style packs
 alongside it. When a request wants one of those looks, install that pack the
 same way, by name; the `minimax-h3` skill's `references/style.md` lists them.
 
-Each skill quotes catalog names and numeric rules that `tests/test_plugin_skills.py`
-holds to the catalog and to the diffusers module that enforces them. A
-skill's `SKILL.md` stays under 12 KB because it loads whenever the skill
-triggers; detail only some requests need is in its `references/`, read on
-demand, and held to the same tests. The
-plugin's version is the engine's; the release script bumps both.
+`tests/test_plugin_skills.py` holds each skill's catalog names and numeric
+rules to the catalog and to the diffusers module that enforces them, and
+requires every skill directory to be named here and in the repo's `CLAUDE.md`.
+A skill's `SKILL.md` stays at or under 12 KiB because it loads whenever the
+skill triggers; detail only some requests need is in its `references/`, read
+on demand and held to the same tests. The plugin's version
+(`.claude-plugin/plugin.json`) is the engine's; `scripts/release.sh` bumps
+both. The marketplace entry is `.claude-plugin/marketplace.json` at the repo
+root.
 
 Adding a family: copy a skill, follow its outline, add the family's rules to
-the test, cite the vendor. The repo's `model-family-onboarding` skill
-(`.claude/skills/`) is the full lifecycle.
+the test, cite the vendor, and name it here and in `CLAUDE.md`. The repo's
+`model-family-onboarding` skill (`.claude/skills/`) is the full lifecycle.
diff --git a/ui/README.md b/ui/README.md
index 18741031..f9a680b7 100644
--- a/ui/README.md
+++ b/ui/README.md
@@ -1,20 +1,33 @@
 # diffusers-workflow UI
 
-Svelte 5 + Vite single-page app over the dw server's JSON API.
+Svelte 5 + Vite single-page app over the dw server's JSON API. Needs Node >= 24.20.0 (`ui/.nvmrc`). Rules for new UI are in `ui/CLAUDE.md`.
 
 ## Build (what `python -m dw.serve` serves at `/`)
 
 ```bash
-export PATH=$HOME/.local/node/bin:$PATH   # user-local Node 24.20.0 LTS (no system install)
 cd ui
 npm install
 npm run build        # -> ui/dist, auto-detected by the server
 ```
 
+The server serves `ui/dist` in a checkout, else the copy packaged into the wheel (`dw/server/ui`).
+
 ## Develop
 
 ```bash
 python -m dw.serve                  # API on :8765
-npm run dev                         # Vite dev server on :5173, proxies /api and /outputs
+npm run dev                         # Vite dev server on :5173, proxies /api and /outputs to :8765
+```
+
+## Checks
+
+```bash
 npm run check                       # svelte-check + tsc
+npm run lint                        # eslint
+npm run format                      # prettier --write
+npm test                            # vitest
+npx playwright test                 # e2e; starts its own server on :8971
+npm run preflight                   # all of the above, plus the ratchet and build
 ```
+
+Server response types in `src/lib/generated/` are generated, never written by hand: `python scripts/dump_openapi.py`, then `npm run gen:api`.

From a6eae109229448c647698207dc6a0da32274be4a Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:09:03 -0600
Subject: [PATCH 33/74] fix(templates): controlnet's Canny thresholds sit
 beside image, not inside it

fetch_image keeps only a media dict's `location`, so low_threshold,
high_threshold, detect_resolution and image_resolution nested in the
`image` dict were dropped and the detector ran on its defaults. Beside
`image` they reach CannyDetector through the task's other arguments. No
other catalog workflow nests task arguments in a media dict.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 workflows/templates/controlnet.json | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/workflows/templates/controlnet.json b/workflows/templates/controlnet.json
index 8c68b2a7..619655ab 100644
--- a/workflows/templates/controlnet.json
+++ b/workflows/templates/controlnet.json
@@ -12,12 +12,12 @@
                 "command": "canny",
                 "arguments": {
                     "image": {
-                        "location": "https://pbs.twimg.com/media/GezfaKzaQAEwLJJ?format=jpg&name=medium",
-                        "low_threshold": 50,
-                        "high_threshold": 200,
-                        "detect_resolution": 1024,
-                        "image_resolution": 1024
-                    }
+                        "location": "https://pbs.twimg.com/media/GezfaKzaQAEwLJJ?format=jpg&name=medium"
+                    },
+                    "low_threshold": 50,
+                    "high_threshold": 200,
+                    "detect_resolution": 1024,
+                    "image_resolution": 1024
                 }
             },
             "result": {

From 49def2099b2ab1eff0acf474489851f2a59fe62b Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:09:03 -0600
Subject: [PATCH 34/74] fix(assess): shot_span_overrun findings use severity
 "warn"

It was the only assessment finding spelled "warning", the validator's
word, outside assessment_rules.SEVERITIES ("info", "warn"), so a reader
filtering findings by severity missed it. The overrun test now pins the
severity to the vocabulary, and TASKS.md says "warn".

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/TASKS.md        | 2 +-
 dw/tasks/assess.py   | 2 +-
 tests/test_assess.py | 5 ++++-
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/TASKS.md b/docs/TASKS.md
index ac404664..19a0091f 100644
--- a/docs/TASKS.md
+++ b/docs/TASKS.md
@@ -2336,7 +2336,7 @@ Guards beyond the threshold:
   `loop_audio` -> `mix_audio`.
 
 A `shots` record reaching past the file's own length is a separate finding,
-`shot_span_overrun` (severity `warning`), on all three probes; the record is
+`shot_span_overrun` (severity `warn`), on all three probes; the record is
 clipped to the file before the rules above run.
 `validate_workflow` reports the same mistake ahead of the run when the
 video's length is already knowable (a `shots` argument against an
diff --git a/dw/tasks/assess.py b/dw/tasks/assess.py
index 3a60fb70..545b4628 100644
--- a/dw/tasks/assess.py
+++ b/dw/tasks/assess.py
@@ -472,7 +472,7 @@ def _shot_span_findings(probe, records, media):
         findings.append(
             {
                 "rule": "shot_span_overrun",
-                "severity": "warning",
+                "severity": "warn",
                 "at": {
                     "shot": shot.get("name"),
                     "index": index,
diff --git a/tests/test_assess.py b/tests/test_assess.py
index 96dd9ace..9c99342e 100644
--- a/tests/test_assess.py
+++ b/tests/test_assess.py
@@ -13,7 +13,7 @@
 import pytest
 from PIL import Image
 
-from dw.assessment_rules import RULES_BY_NAME
+from dw.assessment_rules import RULES_BY_NAME, SEVERITIES
 from dw.media_types import AudioVideo
 from dw.runs import MANIFEST_FILE_NAME, shots_beside
 from dw.shots import shot_record
@@ -995,6 +995,9 @@ def test_analyze_seams_warns_and_reports_a_finding(self, monkeypatch):
         assert len(overrun) == 1
         assert overrun[0]["at"]["shot"] == "b"
         assert overrun[0]["value"]["frames"] == 176
+        # one of the assessment vocabulary's severities, not the validator's
+        # "warning" - a reader filtering findings by severity would miss it
+        assert overrun[0]["severity"] in SEVERITIES
 
         assert any(w[1].get("kind") == "shot_span_overrun" for w in warnings)
         assert any(w[1].get("shots") == ["b"] for w in warnings)

From b1a00527ab58821103d29b3771a59b20cef63384 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:51:58 -0600
Subject: [PATCH 35/74] docs: #1168 - proposal for skipping Klein's
 prompt-upsampler block

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 docs/proposals/klein-skip-prompt-upsampler.md | 40 +++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 docs/proposals/klein-skip-prompt-upsampler.md

diff --git a/docs/proposals/klein-skip-prompt-upsampler.md b/docs/proposals/klein-skip-prompt-upsampler.md
new file mode 100644
index 00000000..97e3d7f2
--- /dev/null
+++ b/docs/proposals/klein-skip-prompt-upsampler.md
@@ -0,0 +1,40 @@
+# Skipping Klein's prompt-upsampler block (#1168)
+
+## Problem
+
+`Flux3DistilledAutoBlocks` starts with a `prompt_upsampler` block
+(`Flux3AutoPromptUpsampleStep`, an `AutoPipelineBlocks` with triggers `["image", None]`). The
+`None` branch is the default, so it runs on every call: no pipeline input makes it a no-op.
+Its only documented skip is `blocks.sub_blocks.pop("prompt_upsampler")`.
+
+Commit 3de1506c stopped loading the 18.8 GB upsampler (it overflowed a 24 GB card), so
+`components.prompt_upsampler` and its processor are `None` and every Klein text-to-image
+call fails with `'NoneType' object has no attribute 'apply_chat_template'`. Loading it again
+would reintroduce the OOM, so the fix has to drop the block.
+
+dw has no syntax for that (`load_components`, `configs`, `components` and `arguments` cannot
+touch the block graph), so this is a new concept and is parked.
+
+## Options
+
+1. **Pipeline `configuration.remove_blocks: ["prompt_upsampler"]`** (recommended). After
+   the pipeline is built, pop each named top-level `sub_blocks` entry; an unknown name is a
+   validation error. Declared in the Klein workflows, so model knowledge stays out of engine
+   code. Touches the schema, `dw/pipeline_processors/pipeline.py` and docs.
+2. A Klein-specific pipeline processor, like the H3 ones. Smaller surface but puts model
+   knowledge in engine code, against CLAUDE.md.
+3. Keep loading the upsampler and offload it (CPU offload or quantize). Fits only with
+   slower runs, and the upsampler would still rewrite the already-long captions the
+   templates pass.
+
+## Affected templates
+
+`flux3/text-to-image`, `image-edit`, `multi-reference`, `layout-boxes`,
+`upsample-then-generate` and the `flux3-klein` model entry. The edit templates and
+`upsample-then-generate` already upsample in a separate step, so they also double-upsampled
+before; popping the block fixes that too.
+
+## Caveat
+
+`layout-boxes` passes the caption through as written; `boxes` as an input for the
+in-pipeline upsampler is not used by any template, so popping loses nothing.

From 15e881f280905fd220da0df55dc85b2950514dbe Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 07:54:24 -0600
Subject: [PATCH 36/74] fix(check-script): #1172 - closed/open compounds don't
 cost line similarity

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 dw/tasks/script_findings.py | 28 +++++++++++++++++++++++++++-
 tests/test_script_check.py  | 17 +++++++++++++++++
 2 files changed, 44 insertions(+), 1 deletion(-)

diff --git a/dw/tasks/script_findings.py b/dw/tasks/script_findings.py
index 1ed2540a..d9026460 100644
--- a/dw/tasks/script_findings.py
+++ b/dw/tasks/script_findings.py
@@ -177,7 +177,33 @@ def line_similarity(expected, heard):
     """How alike two word lists are, 0..1. Two empty lists are identical."""
     if not expected and not heard:
         return 1.0
-    return difflib.SequenceMatcher(None, expected, heard, autojunk=False).ratio()
+    plain = difflib.SequenceMatcher(None, expected, heard, autojunk=False).ratio()
+    merged_e = _merge_compounds(expected, heard)
+    merged_h = _merge_compounds(heard, expected)
+    if merged_e == expected and merged_h == heard:
+        return plain
+    merged = difflib.SequenceMatcher(None, merged_e, merged_h, autojunk=False).ratio()
+    return max(plain, merged)
+
+
+def _merge_compounds(words, other):
+    """`words` with adjacent words joined where the join is a word of `other`:
+    a compound written open on one side and closed on the other ('candle
+    crumbs' / 'candlecrumbs') is a spelling choice, not a different line."""
+    targets = set(other)
+    out = []
+    i = 0
+    while i < len(words):
+        for span in (3, 2):
+            joined = "".join(words[i : i + span])
+            if i + span <= len(words) and joined in targets:
+                out.append(joined)
+                i += span
+                break
+        else:
+            out.append(words[i])
+            i += 1
+    return out
 
 
 def tail_level(mono, sample_rate, end=None):
diff --git a/tests/test_script_check.py b/tests/test_script_check.py
index c2cf02f7..ea25010e 100644
--- a/tests/test_script_check.py
+++ b/tests/test_script_check.py
@@ -1640,3 +1640,20 @@ def test_clean_cut_is_one_pass(self):
         answer, calls = self._run(whole, [])
         self.assertEqual(len(calls), 1)
         self.assertEqual(answer["transcription_pass"], "whole_cut")
+
+
+def test_line_similarity_closed_compound_is_not_a_mismatch():
+    from dw.tasks.script_findings import line_similarity
+
+    expected = ["those", "are", "candle", "crumbs"]
+    assert line_similarity(expected, ["those", "are", "candlecrumbs"]) == 1.0
+    assert line_similarity(["ice", "cream"], ["icecream"]) == 1.0
+    assert line_similarity(["those", "are", "candlecrumbs"], expected) == 1.0
+
+
+def test_line_similarity_wrong_short_line_still_fails():
+    from dw.tasks.script_findings import line_similarity
+
+    expected = ["those", "are", "candle", "crumbs"]
+    assert line_similarity(expected, ["these", "are", "bread", "crumbs"]) < 0.6
+    assert line_similarity(expected, ["no", "way"]) < 0.6

From 7d8e9db6f9197eaf34a967e25e4533af739d81f6 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 08:13:13 -0600
Subject: [PATCH 37/74] fix(skill): #1178 - script-to-video face-drift
 reference and action, in fix-ladder

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 plugins/dw/skills/script-to-video/SKILL.md                | 2 +-
 .../dw/skills/script-to-video/references/fix-ladder.md    | 8 ++++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/plugins/dw/skills/script-to-video/SKILL.md b/plugins/dw/skills/script-to-video/SKILL.md
index 80545899..dc70284f 100644
--- a/plugins/dw/skills/script-to-video/SKILL.md
+++ b/plugins/dw/skills/script-to-video/SKILL.md
@@ -145,7 +145,7 @@ shot's budget below.
 Before the keep, run the beat review in `references/beat-review.md` on
 the shot's `get_output_frames` frames.
 Fix a shot in the order and within the caps of `references/fix-ladder.md`: three failed attempts, then ask.
-Check a recurring face with `templates/face-drift-check`.
+Face drift (`templates/face-drift-check`): see `references/fix-ladder.md`.
 For H3, lint the prompt first: `minimax-h3/references/prompt-lint.md`.
 Each shot's files are organized by `subfolder` - typically `final`
 for the deliverable video or `intermediate` for test frames; read the
diff --git a/plugins/dw/skills/script-to-video/references/fix-ladder.md b/plugins/dw/skills/script-to-video/references/fix-ladder.md
index ac439d9c..afd684b3 100644
--- a/plugins/dw/skills/script-to-video/references/fix-ladder.md
+++ b/plugins/dw/skills/script-to-video/references/fix-ladder.md
@@ -38,3 +38,11 @@ and its caps are adapted from open-higgsfield-skills (MIT).
 
 A fix is judged by looking: `get_output_frames` on the new take, then the
 beat review in `references/beat-review.md`.
+
+## Face drift
+
+`templates/face-drift-check` takes as its `reference` the character's cast
+portrait from casting, the one the shots were conditioned on. A `face_drift`
+finding is a flag to look at with `get_output_frames`, not a verdict. On a
+confirmed drift, re-roll the shot on this ladder, conditioned on the cast
+reference; the re-roll counts against the shot's budget.

From 72af5decf786954f617b2222cbf79a3d50460695 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 08:22:38 -0600
Subject: [PATCH 38/74] feat(h3): #1173 - latent_anchor and chain.anchor
 segment_1

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/guides.py                              |  64 ++++++++-
 dw/introspection.py                       |   2 +
 dw/pipeline_processors/chain.py           |  41 +++++-
 dw/pipeline_processors/components.py      |   3 +
 dw/pipeline_processors/h3_anchor.py       | 167 ++++++++++++++++++++++
 dw/pipeline_processors/h3_anchor_steps.py |  97 +++++++++++++
 dw/pipeline_processors/h3_rules.py        |  47 ++++++
 dw/pipeline_processors/pipeline.py        |  23 +++
 dw/validation.py                          |   8 +-
 dw/workflow_schema.json                   |  17 +++
 10 files changed, 464 insertions(+), 5 deletions(-)
 create mode 100644 dw/pipeline_processors/h3_anchor.py
 create mode 100644 dw/pipeline_processors/h3_anchor_steps.py

diff --git a/dw/guides.py b/dw/guides.py
index 36f13f74..51df4c1b 100644
--- a/dw/guides.py
+++ b/dw/guides.py
@@ -24,6 +24,12 @@
 chain.py), so `guide_chain_errors` refuses it where those guides would be: off
 an H3 t2va/fl2va step, with a `guide_frames` other than 22 or 39, or with a
 `carry_frames` it would not read (`guide_chain_problems`, shared with the run).
+
+The latent statistics anchor (#1166) is refused here too, by `anchor_errors`:
+a `latent_anchor` argument, or a chain's `anchor: "segment_1"`, off an H3
+t2va/fl2va step, and a chain `anchor` that is unknown or paired with a
+continuity other than guide or last_frame (`chain_anchor_problem`, shared with
+the run).
 """
 
 from . import references
@@ -35,6 +41,7 @@
 from .for_each import render_path
 from .pipeline_processors.h3_guides import default_num_frames
 from .pipeline_processors.h3_rules import (
+    ANCHOR_NONE,
     CHAIN_CONTINUITY_MODES,
     GUIDE_CHAIN_DEFAULT,
     GUIDE_CHAIN_FRAMES,
@@ -42,7 +49,9 @@
     GUIDE_CHAIN_RULE,
     GUIDE_CONTINUITY,
     GUIDE_LIMIT,
+    LATENT_ANCHOR_INPUT,
     RENDER_GRID,
+    chain_anchor_problem,
     guide_chain_problems,
     guide_end_problem,
     guide_frame_problem,
@@ -310,4 +319,57 @@ def guide_lengths(pipeline, base_dir=None, probe=None):
     return lengths
 
 
-__all__ = ["guide_chain_errors", "guide_lengths", "guides_errors"]
+def _takes_anchor_problem(pipeline, argument):
+    """Why this step cannot take the latent anchor, or None: it is not
+    MiniMax-H3 (`h3_rules.not_h3`), or it loads ref2va, which has no anchor
+    step. A pipeline or workflow that is not literal is left to the run."""
+    h3_problem = not_h3(pipeline, argument)
+    if h3_problem:
+        return h3_problem
+    from_pretrained = pipeline.get(FROM_PRETRAINED_KEY)
+    workflow = (
+        from_pretrained.get(WORKFLOW_KEY) if isinstance(from_pretrained, dict) else None
+    )
+    if workflow in REFERENCE_WORKFLOWS:
+        return f"{argument} runs on MiniMax-H3 t2va or fl2va only, not ref2va"
+    return None
+
+
+def anchor_errors(workflow_definition, source_indices=None):
+    """Every latent statistics anchor refused before the run, as
+    [{path, message}]: a `latent_anchor` argument on a step that cannot take
+    it, and a chain `anchor` that is unknown, paired with a continuity it
+    cannot run under, or set on a step that cannot take it."""
+    errors = []
+    for _, _, pipeline, source, where in references.iter_steps(
+        workflow_definition.get("steps"), source_indices, "pipeline"
+    ):
+        base = ("steps", source, "pipeline")
+        arguments = pipeline.get("arguments")
+        if isinstance(arguments, dict) and arguments.get(LATENT_ANCHOR_INPUT) is not None:
+            problem = _takes_anchor_problem(pipeline, LATENT_ANCHOR_INPUT)
+            if problem:
+                errors.append(
+                    {
+                        "path": render_path(base + ("arguments", LATENT_ANCHOR_INPUT)),
+                        "message": f"{problem}{where}",
+                    }
+                )
+        chain = pipeline.get("chain")
+        if not isinstance(chain, dict):
+            continue
+        path = render_path(base + ("chain", "anchor"))
+        problem = chain_anchor_problem(chain)
+        if problem:
+            errors.append({"path": path, "message": f"{problem}{where}"})
+            continue
+        anchor = chain.get("anchor", ANCHOR_NONE)
+        if anchor == ANCHOR_NONE or references.is_ref(references.UNRESOLVED, anchor):
+            continue
+        problem = _takes_anchor_problem(pipeline, "chain anchor 'segment_1'")
+        if problem:
+            errors.append({"path": path, "message": f"{problem}{where}"})
+    return errors
+
+
+__all__ = ["anchor_errors", "guide_chain_errors", "guide_lengths", "guides_errors"]
diff --git a/dw/introspection.py b/dw/introspection.py
index 162ab110..4d78fb72 100644
--- a/dw/introspection.py
+++ b/dw/introspection.py
@@ -366,6 +366,7 @@ def _modular_block_parameters(cls):
         return []
     import importlib
 
+    from .pipeline_processors.h3_anchor import insert_latent_anchor
     from .pipeline_processors.h3_guides import insert_guides
     from .pipeline_processors.h3_hold import insert_audio_hold
     from .pipeline_processors.h3_rules import REFINE_NOISE
@@ -381,6 +382,7 @@ def _modular_block_parameters(cls):
         pipeline = blocks_class().init_pipeline()
         insert_audio_hold(pipeline)
         insert_guides(pipeline)
+        insert_latent_anchor(pipeline)
         block_inputs = pipeline._blocks.inputs
     except Exception as error:
         # The signature still answers without them
diff --git a/dw/pipeline_processors/chain.py b/dw/pipeline_processors/chain.py
index 33a6a94c..cfe897da 100644
--- a/dw/pipeline_processors/chain.py
+++ b/dw/pipeline_processors/chain.py
@@ -52,15 +52,20 @@
 from ..task_domains import frames_to_samples
 from ..tasks.joins import equal_power_crossfade_join
 from ..tasks.video_utils import extract_frame, frames_as_pil_list
+from .h3_anchor import anchor_refusal
 from .h3_guides import guides_refusal
 from .h3_hold import hold_audio_reference
 from .h3_rules import (
+    ANCHOR_NONE,
+    ANCHOR_SEGMENT_1,
     CHAIN_CONTINUITY_MODES,
     GUIDES_INPUT,
     GUIDE_CHAIN_DEFAULT,
     GUIDE_CHAIN_RULE,
     GUIDE_CONTINUITY,
     HOLD_AUDIO_INPUT,
+    LATENT_ANCHOR_INPUT,
+    chain_anchor_problem,
     guide_chain_problems,
 )
 
@@ -428,6 +433,8 @@ def run_chain(pipeline, chain_definition, arguments):
     continuity = CONTINUITY_MODES[config.continuity](config)
     if config.continuity == GUIDE_CONTINUITY:
         _check_guide_chain(pipeline, arguments, chain_definition, config)
+    if config.anchor == ANCHOR_SEGMENT_1:
+        _check_anchor_chain(pipeline)
 
     # With save_segments, each completed segment is written to disk and its
     # frames freed, bounding memory to one segment - a crash leaves the
@@ -438,6 +445,10 @@ def run_chain(pipeline, chain_definition, arguments):
     audio = None  # joined generated audio, (channels, samples) float32
     audio_rate = None
     carry = None
+    # Segment 1's decoded frames, the latent anchor of every later segment
+    # under `anchor: "segment_1"` (#1166) - the same frame objects the output
+    # holds, so keeping them costs no copy on an unspilled chain
+    anchor_frames = None
     # One shot per segment, measured as the picture and track grow (#378);
     # the frames are counted rather than read off `frames`, which a spilled
     # chain never fills
@@ -447,7 +458,7 @@ def run_chain(pipeline, chain_definition, arguments):
     try:
         for segment in config.plan:
             segment_arguments = _segment_arguments(
-                arguments, config, segment, continuity, carry
+                arguments, config, segment, continuity, carry, anchor_frames
             )
 
             logger.info(
@@ -463,6 +474,8 @@ def run_chain(pipeline, chain_definition, arguments):
 
             carry = continuity.extract(artifact)
             segment_frames = frames_as_pil_list(artifact)
+            if config.anchor == ANCHOR_SEGMENT_1 and segment.index == 0:
+                anchor_frames = segment_frames
             segment_audio, segment_rate = _generated_audio(artifact)
             # A segment's own generated audio can run a codec-padding sliver
             # short of the frames it was asked for - the same gap #197 fixed for
@@ -558,9 +571,13 @@ def _spilled_frames(config, spill):
     )
 
 
-def _segment_arguments(arguments, config, segment, continuity, carry):
+def _segment_arguments(
+    arguments, config, segment, continuity, carry, anchor_frames=None
+):
     """One segment's arguments: the step's, with this segment's prompt, its
-    slice of a match_audio track, and the previous segment's carry."""
+    slice of a match_audio track, the previous segment's carry, and - under
+    `anchor: "segment_1"`, on every segment after the first - segment 1's
+    frames as its `latent_anchor`."""
     segment_arguments = dict(arguments)
 
     if config.prompts:
@@ -579,6 +596,8 @@ def _segment_arguments(arguments, config, segment, continuity, carry):
 
     if segment.index > 0:
         continuity.inject(segment_arguments, carry, config.segment_argument)
+        if anchor_frames is not None:
+            segment_arguments[LATENT_ANCHOR_INPUT] = anchor_frames
     return segment_arguments
 
 
@@ -601,6 +620,16 @@ def _record_seam(shot, segment, config, applied):
     )
 
 
+def _check_anchor_chain(pipeline):
+    """Refuse an `anchor: "segment_1"` chain whose loaded pipeline has no
+    latent anchor step - not MiniMax-H3 t2va/fl2va - before segment 1 renders."""
+    loaded = getattr(pipeline, "pipeline", None)
+    if loaded is not None:
+        refusal = anchor_refusal(loaded)
+        if refusal:
+            raise ValueError(f"Chain anchor 'segment_1': {refusal}")
+
+
 def _check_guide_chain(pipeline, arguments, chain_definition, config):
     """Refuse a guide chain the H3 guides cannot take before the first segment
     renders, and note once in the job log the settings it does not read."""
@@ -658,6 +687,12 @@ def __init__(
                 f"Unknown chain continuity '{self.continuity}' - expected one of {known}"
             )
 
+        # Segment 1 as every later segment's latent statistics anchor (#1166)
+        problem = chain_anchor_problem(chain_definition)
+        if problem:
+            raise ValueError(f"Chain {problem}")
+        self.anchor = chain_definition.get("anchor", ANCHOR_NONE)
+
         self.segment_argument = chain_definition.get("segment_argument", "image")
         self.carry_frames = chain_definition.get("carry_frames", None)
         if self.carry_frames is not None:
diff --git a/dw/pipeline_processors/components.py b/dw/pipeline_processors/components.py
index f8b67880..c4537c83 100644
--- a/dw/pipeline_processors/components.py
+++ b/dw/pipeline_processors/components.py
@@ -27,6 +27,7 @@
     captured_mag_calibration,
     get_cache_transformer,
 )
+from .h3_anchor import insert_latent_anchor
 from .h3_guides import insert_guides
 from .h3_hold import core_denoise_sequences, insert_audio_hold
 from .h3_mask import insert_mask_blocks
@@ -719,6 +720,8 @@ def from_pretrained():
             insert_audio_hold(component)
             # ...and `guides` on t2va and fl2va
             insert_guides(component)
+            # ...and `latent_anchor` on t2va and fl2va (h3_anchor.py)
+            insert_latent_anchor(component)
             # ...and `source_video` with a mask, a regional edit (h3_mask.py)
             insert_mask_blocks(component)
             # ...and the decode-free graph a `refine_tiles` step runs per tile
diff --git a/dw/pipeline_processors/h3_anchor.py b/dw/pipeline_processors/h3_anchor.py
new file mode 100644
index 00000000..c740da23
--- /dev/null
+++ b/dw/pipeline_processors/h3_anchor.py
@@ -0,0 +1,167 @@
+"""MiniMax-H3's latent statistics anchor (#1166): `latent_anchor` rescales the
+generated video latent, channel by channel, to the mean and standard deviation of
+an anchor clip's latent.
+
+A long guide chain drifts in colour and contrast from segment to segment: each
+segment's latent statistics wander a little from the last one's, and the guide
+carries the drift on. With `latent_anchor` set, the anchor clip is VAE-encoded on
+the render's canvas exactly as a guide is (`fit_guide_frames` and diffusers'
+`encode_vae_condition`), its per-channel mean and std are taken over every latent
+position, and the generated latent is moved onto them:
+
+    x' = (x - mean_x) / std_x * std_a + mean_a      (per VAE latent channel)
+
+The step (`DwH3LatentAnchorStep`, dw/pipeline_processors/h3_anchor_steps.py) runs
+right after `after_denoise` has unpacked the video rows and before decode, so it
+reads the normalized video latent the decoder denormalizes; audio latents are
+never touched. A full match: there is no strength knob.
+
+The block is inserted at load into every t2va/fl2va core-denoise sequence
+(`insert_latent_anchor`) and passes the latent through untouched when no
+`latent_anchor` is given, so a run without one is bit-identical to stock. ref2va
+gets no block, and `anchor_refusal` names why.
+
+The concept follows H3MD Director's segment colour anchoring (concept only, no
+code; docs/ATTRIBUTIONS.md). The block module imports diffusers when it loads, so
+it is imported in `anchor_block()` only, as h3_guides does its blocks (#790).
+"""
+
+import logging
+
+import torch
+
+from .h3_hold import core_denoise_sequences
+from .h3_guides import guide_frames_array
+from .h3_rules import (
+    ANCHOR_AFTER,
+    ANCHOR_BLOCK,
+    LATENT_ANCHOR_INPUT,
+    snap_guide_length,
+)
+
+logger = logging.getLogger("dw")
+
+# The floor a generated channel's std is held to, so a flat channel is moved
+# onto the anchor's mean rather than divided by zero
+STD_FLOOR = 1e-6
+# The dims a (batch, channel, frames, height, width) latent is reduced over
+CHANNEL_DIMS = (0, 2, 3, 4)
+
+
+def channel_statistics(latents):
+    """(mean, std) per channel of a (B, C, T, H, W) latent, each shaped
+    (1, C, 1, 1, 1), in float32 - over every batch item and latent position."""
+    values = latents.to(torch.float32)
+    mean = values.mean(dim=CHANNEL_DIMS, keepdim=True)
+    std = values.std(dim=CHANNEL_DIMS, keepdim=True, unbiased=False)
+    return mean, std
+
+
+def match_channel_statistics(latents, anchor_mean, anchor_std):
+    """`latents` (B, C, T, H, W) rescaled per channel to `anchor_mean` and
+    `anchor_std`: `(x - mean_x) / std_x * std_a + mean_a`, computed in float32
+    and returned in the latent's own dtype and device."""
+    if latents.dim() != 5:
+        raise ValueError(
+            f"latent_anchor works on a (batch, channel, frames, height, width) "
+            f"video latent, got shape {tuple(latents.shape)}"
+        )
+    values = latents.to(torch.float32)
+    mean, std = channel_statistics(values)
+    anchor_mean = anchor_mean.to(values.device, torch.float32).reshape(1, -1, 1, 1, 1)
+    anchor_std = anchor_std.to(values.device, torch.float32).reshape(1, -1, 1, 1, 1)
+    if anchor_mean.shape[1] != values.shape[1]:
+        raise ValueError(
+            f"latent_anchor has {anchor_mean.shape[1]} latent channels and the "
+            f"render {values.shape[1]}"
+        )
+    matched = (values - mean) / std.clamp_min(STD_FLOOR) * anchor_std + anchor_mean
+    return matched.to(latents.dtype)
+
+
+def anchor_block():
+    """The anchor step class (imports diffusers on first use)."""
+    from .h3_anchor_steps import DwH3LatentAnchorStep
+
+    return DwH3LatentAnchorStep
+
+
+def _anchor_sequences(pipeline):
+    """[(prefix, sequence)] for the core-denoise sequences that generate fresh
+    video - t2va and fl2va; ref2va, whose layout is its own, is left out."""
+    found = []
+    for prefix, sequence in core_denoise_sequences(pipeline):
+        names = list(sequence.sub_blocks)
+        if prefix + ANCHOR_AFTER not in names:
+            continue
+        if prefix + "prepare_latents_ref2va" in names:
+            continue
+        found.append((prefix, sequence))
+    return found
+
+
+def insert_latent_anchor(pipeline):
+    """Put the anchor step right after `after_denoise` in every t2va/fl2va
+    core-denoise sequence. Idempotent; a no-op on any other pipeline. Returns
+    whether the pipeline now takes `latent_anchor`."""
+    inserted = False
+    block = None
+    for prefix, sequence in _anchor_sequences(pipeline):
+        sub_blocks = sequence.sub_blocks
+        names = list(sub_blocks)
+        if prefix + ANCHOR_BLOCK not in names:
+            block = block or anchor_block()
+            sub_blocks.insert(
+                prefix + ANCHOR_BLOCK, block(), names.index(prefix + ANCHOR_AFTER) + 1
+            )
+        inserted = True
+    return inserted
+
+
+def takes_latent_anchor(pipeline):
+    """Whether a loaded pipeline has the anchor step in a core sequence."""
+    return any(
+        prefix + ANCHOR_BLOCK in sequence.sub_blocks
+        for prefix, sequence in core_denoise_sequences(pipeline)
+    )
+
+
+def anchor_refusal(pipeline):
+    """Why this pipeline cannot take `latent_anchor`, or None."""
+    if takes_latent_anchor(pipeline):
+        return None
+    return (
+        f"{LATENT_ANCHOR_INPUT} is a MiniMax-H3 t2va/fl2va argument, and "
+        f"{type(pipeline).__name__} has no '{ANCHOR_AFTER}' step it follows"
+    )
+
+
+def prepared_anchor(where, value, base_dir):
+    """A `latent_anchor` value as `(n, h, w, 3)` uint8 frames cut to a
+    whole-latent length - from a video path or reference, or frames (a step's
+    result, a list of images, an array or tensor).
+
+    Raises:
+        ValueError: If the value is not frames of a video
+    """
+    if isinstance(value, str):
+        # argument_media imports diffusers, which h3_guides waits for (#790)
+        from ..argument_media import fetch_video
+
+        value = fetch_video(value, base_dir)
+    try:
+        frames = guide_frames_array(value)
+    except ValueError as error:
+        raise ValueError(f"{where}: {error}".replace("a guide's", "the")) from error
+    return frames[: snap_guide_length(frames.shape[0])]
+
+
+__all__ = [
+    "anchor_block",
+    "anchor_refusal",
+    "channel_statistics",
+    "insert_latent_anchor",
+    "match_channel_statistics",
+    "prepared_anchor",
+    "takes_latent_anchor",
+]
diff --git a/dw/pipeline_processors/h3_anchor_steps.py b/dw/pipeline_processors/h3_anchor_steps.py
new file mode 100644
index 00000000..77fa5eea
--- /dev/null
+++ b/dw/pipeline_processors/h3_anchor_steps.py
@@ -0,0 +1,97 @@
+"""The MiniMax-H3 latent statistics anchor step dw puts after `after_denoise`.
+
+What the anchor is, and where the step goes, is dw/pipeline_processors/h3_anchor.py,
+which inserts it. This module imports diffusers when it loads, so nothing imports
+it at module level - `h3_anchor.anchor_block()` imports it on first use.
+"""
+
+import logging
+
+import numpy as np
+import torch
+from diffusers.models import AutoencoderKLMiniMaxH3
+from diffusers.modular_pipelines.minimax_h3 import encoders
+from diffusers.modular_pipelines.modular_pipeline import ModularPipelineBlocks
+from diffusers.modular_pipelines.modular_pipeline_utils import (
+    ComponentSpec,
+    InputParam,
+    OutputParam,
+)
+
+from .h3_anchor import channel_statistics, match_channel_statistics
+from .h3_guide_steps import fit_guide_frames
+from .h3_rules import LATENT_ANCHOR_INPUT, snap_guide_length
+
+logger = logging.getLogger("dw")
+
+
+def anchor_statistics(components, frames, height, width):
+    """(mean, std) per latent channel of `frames` ((n, h, w, 3) uint8), encoded
+    on the (height, width) canvas the way a guide is: covered and centre-cropped
+    onto it, cut to a whole-latent length, and VAE-encoded into the normalized
+    latent space the generated latents live in."""
+    frames = frames[: snap_guide_length(frames.shape[0])]
+    pixels = fit_guide_frames(frames, height, width)
+    pixels = torch.from_numpy(np.ascontiguousarray(pixels))
+    device = components._execution_device
+    # Through the module, so the encode is the one installed when it runs
+    encoded = encoders.encode_vae_condition(
+        components.vae,
+        pixels.to(device).permute(3, 0, 1, 2)[None],
+        components.pixel_mean,
+        components.pixel_std,
+        components.keyframe_encode_seed,
+    )
+    return channel_statistics(encoded)
+
+
+class DwH3LatentAnchorStep(ModularPipelineBlocks):
+    model_name = "minimax-h3"
+
+    @property
+    def description(self):
+        return (
+            "dw: rescales each channel of the unpacked video latents to the mean and std of `latent_anchor`'s "
+            "latent - the anchor clip VAE-encoded on the canvas - before decode. Audio is untouched. Does "
+            "nothing without `latent_anchor`."
+        )
+
+    @property
+    def expected_components(self):
+        return [ComponentSpec("vae", AutoencoderKLMiniMaxH3)]
+
+    @property
+    def inputs(self):
+        return [
+            InputParam(name="latents", type_hint=torch.Tensor, required=True),
+            InputParam(
+                name=LATENT_ANCHOR_INPUT,
+                default=None,
+                description=(
+                    "Video frames, (n, h, w, 3) uint8, whose per-channel latent mean and std the generated "
+                    "video latent is rescaled to before decode."
+                ),
+            ),
+            InputParam(name="height", type_hint=int, required=True),
+            InputParam(name="width", type_hint=int, required=True),
+        ]
+
+    @property
+    def intermediate_outputs(self):
+        return [OutputParam("latents", type_hint=torch.Tensor)]
+
+    @torch.no_grad()
+    def __call__(self, components, state):
+        block_state = self.get_block_state(state)
+        frames = getattr(block_state, LATENT_ANCHOR_INPUT)
+        if frames is None:
+            return components, state
+        mean, std = anchor_statistics(
+            components, frames, block_state.height, block_state.width
+        )
+        block_state.latents = match_channel_statistics(block_state.latents, mean, std)
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+__all__ = ["DwH3LatentAnchorStep", "anchor_statistics"]
diff --git a/dw/pipeline_processors/h3_rules.py b/dw/pipeline_processors/h3_rules.py
index 6d81216a..62e2f467 100644
--- a/dw/pipeline_processors/h3_rules.py
+++ b/dw/pipeline_processors/h3_rules.py
@@ -210,6 +210,9 @@ def _full_frame_problems(arguments, canvas):
 H3_FPS = 24
 
 
+# The latent statistics anchor's call argument (#1166; see ANCHOR_BLOCK)
+LATENT_ANCHOR_INPUT = "latent_anchor"
+
 # The arguments that condition a step on a reference, a keyframe or a guide: the
 # tile plan has no place to put one, and each tile would need its own crop
 TILE_CONFLICTS = (
@@ -220,6 +223,7 @@ def _full_frame_problems(arguments, canvas):
     "source_video",
     "mask_video",
     "mask_image",
+    LATENT_ANCHOR_INPUT,
 )
 
 
@@ -511,6 +515,49 @@ def guide_frame_problem(frame):
 # (dw/guides.py) reads the same names without importing chain.py
 CHAIN_CONTINUITY_MODES = ("last_frame", "last_segment", GUIDE_CONTINUITY)
 
+# The latent statistics anchor (#1166): `latent_anchor` is video frames whose
+# per-channel latent mean/std the generated video latent is rescaled to, after
+# `after_denoise` and before decode, by the ANCHOR_BLOCK step
+# (dw/pipeline_processors/h3_anchor.py). A chain's `anchor: "segment_1"` sets it
+# on every segment after the first to segment 1's decoded frames.
+ANCHOR_BLOCK = "dw_latent_anchor"
+ANCHOR_AFTER = "after_denoise"
+ANCHOR_NONE = "none"
+ANCHOR_SEGMENT_1 = "segment_1"
+CHAIN_ANCHOR_MODES = (ANCHOR_NONE, ANCHOR_SEGMENT_1)
+# The continuities whose segments are fresh t2va/fl2va generations the anchor
+# block runs in; `last_segment` carries a ref2va reference, which has no block
+ANCHOR_CONTINUITIES = ("last_frame", GUIDE_CONTINUITY)
+
+
+def chain_anchor_problem(chain):
+    """The reason a chain block's `anchor` is refused for its own settings, or
+    None: an unknown value (naming the values), or `segment_1` with a continuity
+    other than guide or last_frame. Unresolved references are left to the run;
+    whether the step is H3 is the caller's to ask (`not_h3`)."""
+    anchor = chain.get("anchor", ANCHOR_NONE)
+    if isinstance(anchor, str) and references.is_ref(references.UNRESOLVED, anchor):
+        return None
+    if anchor not in CHAIN_ANCHOR_MODES:
+        return (
+            f"unknown chain anchor {anchor!r} - expected one of "
+            f"{', '.join(CHAIN_ANCHOR_MODES)}"
+        )
+    if anchor == ANCHOR_NONE:
+        return None
+    continuity = chain.get("continuity", "last_frame")
+    if isinstance(continuity, str) and references.is_ref(
+        references.UNRESOLVED, continuity
+    ):
+        return None
+    if continuity not in ANCHOR_CONTINUITIES:
+        return (
+            f"anchor 'segment_1' needs continuity "
+            f"{' or '.join(repr(c) for c in ANCHOR_CONTINUITIES)} - its segments "
+            f"are MiniMax-H3 t2va/fl2va generations; got {continuity!r}"
+        )
+    return None
+
 
 def guide_chain_problems(chain):
     """[(key, message)] for a guide-continuity chain block's own settings: a
diff --git a/dw/pipeline_processors/pipeline.py b/dw/pipeline_processors/pipeline.py
index d0fa70a3..6ce8a069 100644
--- a/dw/pipeline_processors/pipeline.py
+++ b/dw/pipeline_processors/pipeline.py
@@ -32,12 +32,14 @@
     attention_slicing_requested,
     place_component,
 )
+from .h3_anchor import anchor_refusal, prepared_anchor
 from .h3_guides import guide_frames_array, guides_refusal, prepared_guide
 from .h3_hold import hold_audio_reference, holds_audio, refines
 from .h3_mask import binarized, edits_regions
 from .h3_rules import (
     GUIDES_INPUT,
     GUIDE_LIMIT,
+    LATENT_ANCHOR_INPUT,
     HELD_AUDIO_OUTPUT,
     HELD_AUDIO_RATE_OUTPUT,
     HOLD_AUDIO_INPUT,
@@ -662,6 +664,7 @@ def _call_pipeline(self, arguments, attn_backend):
         arguments = self._with_mask(arguments)
         arguments = self._with_held_audio(arguments)
         arguments = self._with_guides(arguments)
+        arguments = self._with_latent_anchor(arguments)
         # The load is over and the denoise loop is starting. Pipelines whose
         # signature has no step callback report nothing else at all, so this
         # is the only thing that distinguishes running from still loading
@@ -801,6 +804,26 @@ def _with_guides(self, arguments):
         arguments[GUIDES_INPUT] = prepared
         return arguments
 
+    def _with_latent_anchor(self, arguments):
+        """`latent_anchor` as the H3 anchor step takes it - uint8 frames cut to
+        a whole-latent length (dw/pipeline_processors/h3_anchor.py). None is no
+        anchor.
+
+        Raises:
+            ValueError: If this pipeline has no anchor step (not MiniMax-H3
+                t2va/fl2va), or the value is not frames of a video
+        """
+        value = arguments.get(LATENT_ANCHOR_INPUT)
+        if value is None:
+            return arguments
+        where = f"Step '{self.name}': {LATENT_ANCHOR_INPUT}"
+        refusal = anchor_refusal(self.pipeline)
+        if refusal:
+            raise ValueError(f"{where}: {refusal}")
+        arguments = dict(arguments)
+        arguments[LATENT_ANCHOR_INPUT] = prepared_anchor(where, value, self.base_dir)
+        return arguments
+
     def _with_mask(self, arguments):
         """A regional edit's `source_video` as uint8 frames and its mask as the
         bool frames the H3 source block maps to rows, under `REGENERATE_MASK`
diff --git a/dw/validation.py b/dw/validation.py
index 93abd77b..2dd51130 100644
--- a/dw/validation.py
+++ b/dw/validation.py
@@ -40,7 +40,7 @@
 from .content_types import content_type_errors
 from .dissolve_frame_errors import dissolve_frame_errors
 from .for_each import ForEachError, entry_field_warnings
-from .guides import guide_chain_errors, guides_errors
+from .guides import anchor_errors, guide_chain_errors, guides_errors
 from .hold_audio import hold_audio_errors, mask_errors, refine_strength_errors
 from .introspection import task_signature_errors
 from .kernel_availability import kernel_availability_errors
@@ -490,6 +490,12 @@ def _task_errors(context):
         "guide_chain",
         lambda c: guide_chain_errors(c.expanded, c.source_indices),
     ),
+    # A latent_anchor or chain anchor off H3 t2va/fl2va, an unknown chain
+    # anchor, or 'segment_1' under a continuity it cannot run under (#1166)
+    Check(
+        "latent_anchor",
+        lambda c: anchor_errors(c.expanded, c.source_indices),
+    ),
     Check("task_signatures", _task_errors),
     # A task argument that runs Hub code, anywhere in the step - the run
     # refuses it too (Task.run), but before it queues costs nothing
diff --git a/dw/workflow_schema.json b/dw/workflow_schema.json
index 1c4b6840..1d6c7e41 100644
--- a/dw/workflow_schema.json
+++ b/dw/workflow_schema.json
@@ -340,6 +340,23 @@
                     ],
                     "default": 22
                 },
+                "anchor": {
+                    "description": "Latent statistics anchor (MiniMax-H3 t2va and fl2va only, with continuity 'guide' or 'last_frame'). 'segment_1' passes segment 1's decoded frames to every later segment as its 'latent_anchor': each later segment's video latent is rescaled, per VAE latent channel, to segment 1's latent mean and std before decode, so colour and contrast stop drifting from segment to segment. Segment 1 itself is not corrected, and audio is never touched. 'none' (the default) anchors nothing. Accepts a 'variable:' reference.",
+                    "anyOf": [
+                        {
+                            "type": "string",
+                            "enum": [
+                                "none",
+                                "segment_1"
+                            ]
+                        },
+                        {
+                            "type": "string",
+                            "pattern": "^variable:"
+                        }
+                    ],
+                    "default": "none"
+                },
                 "segment_argument": {
                     "description": "The pipeline argument the carry-over lands in - e.g. 'image' for image-to-video pipelines, or 'references' for reference-conditioned modular pipelines, where the carry-over is appended as an image or video reference.",
                     "type": "string",

From fb46e9d5c99675d6b107356b8d0b71b9a65a629f Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 08:29:48 -0600
Subject: [PATCH 39/74] feat(h3): #1173 - latent anchor tests, docs and
 attribution

The schema takes chain.anchor as a string so validation's own check
refuses an unknown value naming the values.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md     |   2 +-
 docs/ATTRIBUTIONS.md     |  12 ++
 docs/WORKFLOW_GUIDE.md   |  28 ++++
 dw/workflow_schema.json  |  14 +-
 tests/test_h3_anchor.py  | 277 +++++++++++++++++++++++++++++++++++++++
 tests/test_h3_rules.py   |   2 +-
 tests/test_validation.py |   1 +
 7 files changed, 321 insertions(+), 15 deletions(-)
 create mode 100644 tests/test_h3_anchor.py

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 7ca5e001..ce867181 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout and regional edit | Eleven modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers` |
+| H3 audio hold, refine, tiled refine, guide layout, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and `not_h3`), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
diff --git a/docs/ATTRIBUTIONS.md b/docs/ATTRIBUTIONS.md
index 594bffe3..d9b31825 100644
--- a/docs/ATTRIBUTIONS.md
+++ b/docs/ATTRIBUTIONS.md
@@ -116,3 +116,15 @@ A ComfyUI node pack for MiniMax H3 prefix streaming, under the MIT licence. It
 inspired the texture-band measurement on `compare_outputs`
 (`texture_bands`, [dw/texture_bands.py](../dw/texture_bands.py)): RMS energy by
 wavelength band, to see whether a change in a chain adds or removes texture.
+
+### [1113800942/comfy-h3md-director](https://github.com/1113800942/comfy-h3md-director)
+
+H3MD Director, a ComfyUI node pack for directing long MiniMax H3 videos, under
+AGPL-3.0-or-later, so only the concept was taken. It inspired the latent
+statistics anchor: rescaling each later segment's video latent to the first
+segment's per-channel mean and standard deviation before decode
+(`latent_anchor`, `chain.anchor: "segment_1"`,
+[dw/pipeline_processors/h3_anchor.py](../dw/pipeline_processors/h3_anchor.py);
+[#1166](https://github.com/dkackman/diffusers-workflow/issues/1166)). The
+diagnosis it answers, colour and contrast drifting from segment to segment of
+an H3 chain, was described by Lumifel and by roycho87 (NerdNotes).
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index 5eb6ca52..40058929 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1832,6 +1832,30 @@ head: N segments of F frames give F + (N - 1)(F - P) frames - 3 x 124 at P = 22
 | `crossfade_ms` | ignored when `carry_audio` is true: the guide held the audio, so the seam is joined with only a declick |
 | `num_frames` | must exceed P, or the chain cannot progress |
 
+#### Anchoring a chain's colour: `latent_anchor` and `anchor: "segment_1"`
+
+A long H3 chain drifts in colour and contrast: each segment's latent statistics wander
+a little from the last, and the carry passes the drift on. `latent_anchor` (an H3
+`t2va`/`fl2va` argument) takes video frames - a path, an `asset:`/`output:` reference,
+or a step's frames - VAE-encodes them on the render's canvas the way a guide is
+encoded, and takes each latent channel's mean and standard deviation over every
+position. After denoising and before decode, the generated video latent is moved onto
+them, channel by channel: `(x - mean_x) / std_x * std_a + mean_a`. The audio is never
+touched. It is a full match, with no strength setting; without `latent_anchor` the
+step is unchanged, bit for bit.
+
+In a chain, `"anchor": "segment_1"` sets it for you: every segment after the first
+gets segment 1's frames as its `latent_anchor`, so the whole chain is held to the
+first segment's look. Segment 1 itself is not corrected.
+
+| Rule | Value |
+|---|---|
+| `anchor` | `none` (default) or `segment_1`; anything else is refused, naming the values |
+| `segment_1` continuity | `last_frame` or `guide`; refused with any other |
+| where | MiniMax-H3 `t2va` or `fl2va`; `latent_anchor` and `segment_1` are refused on `ref2va` and on a non-H3 step |
+| frames | cut to a whole-latent length (1, 5 or 17m + 5) |
+| `refine_tiles` | refused beside `latent_anchor`, as the other per-call H3 arguments are |
+
 `workflows/templates/minimax/chained-segments.json` takes `continuity` and
 `guide_frames` as variables. The `ref2va` chains (`chain-video-continuity` and the
 `match_audio` chains) keep `last_segment`.
@@ -2378,6 +2402,10 @@ joined into a single file:
   next as a held frame-0 guide (see *H3: holding a clip with `guides`*, *Chaining with a
   guide*).
 - `guide_frames` — with `guide`, how many frames the guide carries: 22 (default) or 39.
+- `anchor` — `none` (default) or `segment_1` (MiniMax-H3 `t2va`/`fl2va`, with
+  `last_frame` or `guide` continuity): every segment after the first is rescaled to
+  segment 1's per-channel latent statistics before decode, holding the chain's colour
+  and contrast (see *Anchoring a chain's colour*).
 - `carry_frames` — with `last_segment`, bound the carry to the last N frames of the
   segment (the audio is cut to the same span). Unset carries the whole segment.
 - `carry_audio` — with `last_segment` or `guide`, whether the carried reference includes its
diff --git a/dw/workflow_schema.json b/dw/workflow_schema.json
index 1d6c7e41..8afa3251 100644
--- a/dw/workflow_schema.json
+++ b/dw/workflow_schema.json
@@ -342,19 +342,7 @@
                 },
                 "anchor": {
                     "description": "Latent statistics anchor (MiniMax-H3 t2va and fl2va only, with continuity 'guide' or 'last_frame'). 'segment_1' passes segment 1's decoded frames to every later segment as its 'latent_anchor': each later segment's video latent is rescaled, per VAE latent channel, to segment 1's latent mean and std before decode, so colour and contrast stop drifting from segment to segment. Segment 1 itself is not corrected, and audio is never touched. 'none' (the default) anchors nothing. Accepts a 'variable:' reference.",
-                    "anyOf": [
-                        {
-                            "type": "string",
-                            "enum": [
-                                "none",
-                                "segment_1"
-                            ]
-                        },
-                        {
-                            "type": "string",
-                            "pattern": "^variable:"
-                        }
-                    ],
+                    "type": "string",
                     "default": "none"
                 },
                 "segment_argument": {
diff --git a/tests/test_h3_anchor.py b/tests/test_h3_anchor.py
new file mode 100644
index 00000000..52e12713
--- /dev/null
+++ b/tests/test_h3_anchor.py
@@ -0,0 +1,277 @@
+"""MiniMax-H3's latent statistics anchor (#1166, #1173): the per-channel
+rescale, the step dw inserts after `after_denoise`, the chain's
+`anchor: "segment_1"`, and the pre-run checks of both."""
+
+import numpy as np
+import pytest
+import torch
+
+from dw.guides import anchor_errors
+from dw.pipeline_processors.chain import ChainConfig, run_chain
+from dw.pipeline_processors.h3_anchor import (
+    channel_statistics,
+    insert_latent_anchor,
+    match_channel_statistics,
+    prepared_anchor,
+)
+from dw.pipeline_processors.h3_rules import (
+    ANCHOR_AFTER,
+    ANCHOR_BLOCK,
+    LATENT_ANCHOR_INPUT,
+    TILE_CONFLICTS,
+    chain_anchor_problem,
+)
+from tests.test_chain import FakePipeline, video_output
+
+
+def synthetic_latent(channels=4, dtype=torch.float32, seed=0):
+    generator = torch.Generator().manual_seed(seed)
+    scales = torch.arange(1, channels + 1, dtype=torch.float32).reshape(1, -1, 1, 1, 1)
+    values = torch.randn((1, channels, 5, 4, 6), generator=generator) * scales + 3.0
+    return values.to(dtype)
+
+
+class TestMatchChannelStatistics:
+    def test_each_channel_ends_at_the_anchors_statistics(self):
+        latents = synthetic_latent()
+        anchor_mean = torch.tensor([0.5, -1.0, 2.0, 0.0])
+        anchor_std = torch.tensor([0.25, 1.5, 0.75, 2.0])
+
+        matched = match_channel_statistics(latents, anchor_mean, anchor_std)
+        mean, std = channel_statistics(matched)
+
+        assert torch.allclose(mean.flatten(), anchor_mean, atol=1e-5)
+        assert torch.allclose(std.flatten(), anchor_std, atol=1e-5)
+
+    def test_matching_onto_an_encoded_anchors_own_statistics(self):
+        anchor = synthetic_latent(seed=1)
+        mean, std = channel_statistics(anchor)
+        matched = match_channel_statistics(synthetic_latent(), mean, std)
+        new_mean, new_std = channel_statistics(matched)
+        assert torch.allclose(new_mean, mean, atol=1e-5)
+        assert torch.allclose(new_std, std, atol=1e-5)
+
+    def test_keeps_the_latents_dtype_and_shape(self):
+        latents = synthetic_latent(dtype=torch.bfloat16)
+        matched = match_channel_statistics(latents, torch.zeros(4), torch.ones(4))
+        assert matched.dtype == torch.bfloat16
+        assert matched.shape == latents.shape
+
+    def test_a_flat_channel_lands_on_the_anchors_mean(self):
+        latents = synthetic_latent()
+        latents[:, 0] = 7.0
+        matched = match_channel_statistics(
+            latents, torch.full((4,), 2.0), torch.ones(4)
+        )
+        assert torch.isfinite(matched).all()
+        assert torch.allclose(matched[:, 0], torch.tensor(2.0))
+
+    def test_refuses_a_packed_latent(self):
+        with pytest.raises(ValueError, match="video latent"):
+            match_channel_statistics(torch.zeros(12, 3), torch.zeros(3), torch.ones(3))
+
+    def test_refuses_a_channel_mismatch(self):
+        with pytest.raises(ValueError, match="latent channels"):
+            match_channel_statistics(synthetic_latent(), torch.zeros(3), torch.ones(3))
+
+
+class TestPreparedAnchor:
+    def test_frames_are_cut_to_a_whole_latent_length(self):
+        frames = np.zeros((9, 4, 6, 3), dtype=np.uint8)
+        assert prepared_anchor("anchor", frames, None).shape[0] == 5
+
+    def test_refuses_what_is_not_frames(self):
+        with pytest.raises(ValueError, match="anchor"):
+            prepared_anchor("anchor", 42, None)
+
+
+class TestChainAnchor:
+    def test_segments_after_the_first_get_segment_ones_frames(self):
+        pipeline = FakePipeline(video_output)
+
+        run_chain(pipeline, {"segments": 3, "anchor": "segment_1"}, {"prompt": "p"})
+
+        assert LATENT_ANCHOR_INPUT not in pipeline.calls[0]
+        first = pipeline.calls[1][LATENT_ANCHOR_INPUT]
+        assert pipeline.calls[2][LATENT_ANCHOR_INPUT] is first
+        # Segment 1's colour (index 0), every one of its frames
+        assert len(first) == 4
+        assert first[1].getpixel((0, 0)) == (0, 100, 150)
+
+    def test_no_anchor_by_default(self):
+        pipeline = FakePipeline(video_output)
+        run_chain(pipeline, {"segments": 3}, {})
+        assert all(LATENT_ANCHOR_INPUT not in call for call in pipeline.calls)
+
+    def test_an_explicit_none(self):
+        pipeline = FakePipeline(video_output)
+        run_chain(pipeline, {"segments": 2, "anchor": "none"}, {})
+        assert all(LATENT_ANCHOR_INPUT not in call for call in pipeline.calls)
+
+    def test_config_refuses_an_unknown_anchor(self):
+        with pytest.raises(ValueError, match="none, segment_1"):
+            ChainConfig({"segments": 2, "anchor": "segment_2"}, {})
+
+    def test_config_refuses_another_continuity(self):
+        with pytest.raises(ValueError, match="last_frame' or 'guide"):
+            ChainConfig(
+                {"segments": 2, "anchor": "segment_1", "continuity": "last_segment"}, {}
+            )
+
+    @pytest.mark.parametrize("continuity", ["last_frame", "guide"])
+    def test_rule_accepts_the_anchor_continuities(self, continuity):
+        chain = {"anchor": "segment_1", "continuity": continuity}
+        assert chain_anchor_problem(chain) is None
+
+    def test_latent_anchor_conflicts_with_tiles(self):
+        assert LATENT_ANCHOR_INPUT in TILE_CONFLICTS
+
+
+def definition(workflow="fl2va", chain=None, pipeline_class=None, **arguments):
+    pipeline = {
+        "configuration": {"component_type": "ModularPipeline"},
+        "from_pretrained_arguments": {
+            "model_name": "MiniMaxAI/MiniMax-H3",
+            "workflow": workflow,
+        },
+        "arguments": arguments,
+    }
+    if pipeline_class:
+        pipeline["configuration"] = {"component_type": pipeline_class}
+        pipeline["from_pretrained_arguments"] = {"model_name": "some/model"}
+    if chain is not None:
+        pipeline["chain"] = chain
+    return {"steps": [{"name": "video", "pipeline": pipeline}]}
+
+
+class TestAnchorErrors:
+    @pytest.mark.parametrize("workflow", ["t2va", "fl2va"])
+    @pytest.mark.parametrize("continuity", ["last_frame", "guide"])
+    def test_a_segment_1_chain_on_h3(self, workflow, continuity):
+        chain = {"segments": 3, "anchor": "segment_1", "continuity": continuity}
+        assert anchor_errors(definition(workflow, chain)) == []
+
+    @pytest.mark.parametrize("workflow", ["t2va", "fl2va"])
+    def test_a_latent_anchor_argument_on_h3(self, workflow):
+        assert anchor_errors(definition(workflow, latent_anchor="clip.mp4")) == []
+
+    def test_no_anchor_is_clean(self):
+        assert anchor_errors(definition(chain={"segments": 2})) == []
+        assert anchor_errors(definition(chain={"segments": 2, "anchor": "none"})) == []
+
+    def test_a_variable_anchor_is_left_to_the_run(self):
+        chain = {"segments": 2, "anchor": "variable:anchor"}
+        assert anchor_errors(definition(chain=chain)) == []
+
+    def test_an_unknown_anchor_names_the_values(self):
+        chain = {"segments": 2, "anchor": "first"}
+        (error,) = anchor_errors(definition(chain=chain))
+        assert error["path"] == "steps[0].pipeline.chain.anchor"
+        assert "none, segment_1" in error["message"]
+
+    def test_segment_1_with_another_continuity(self):
+        chain = {"segments": 2, "anchor": "segment_1", "continuity": "last_segment"}
+        (error,) = anchor_errors(definition(chain=chain))
+        assert "last_frame' or 'guide" in error["message"]
+
+    def test_segment_1_off_h3(self):
+        chain = {"segments": 2, "anchor": "segment_1"}
+        (error,) = anchor_errors(definition(chain=chain, pipeline_class="WanPipeline"))
+        assert error["path"] == "steps[0].pipeline.chain.anchor"
+
+    def test_segment_1_on_ref2va(self):
+        chain = {"segments": 2, "anchor": "segment_1"}
+        (error,) = anchor_errors(definition("ref2va", chain=chain))
+        assert "not ref2va" in error["message"]
+
+    def test_latent_anchor_off_h3(self):
+        workflow = definition(pipeline_class="WanPipeline", latent_anchor="clip.mp4")
+        (error,) = anchor_errors(workflow)
+        assert error["path"] == "steps[0].pipeline.arguments.latent_anchor"
+
+    def test_latent_anchor_on_ref2va(self):
+        (error,) = anchor_errors(definition("ref2va", latent_anchor="clip.mp4"))
+        assert "not ref2va" in error["message"]
+
+
+minimax = pytest.importorskip("diffusers.modular_pipelines.minimax_h3")
+from diffusers.modular_pipelines.modular_pipeline import PipelineState  # noqa: E402
+from dw.pipeline_processors import h3_hold  # noqa: E402
+from dw.pipeline_processors.h3_anchor_steps import DwH3LatentAnchorStep  # noqa: E402
+from dw.pipeline_processors.h3_hold import insert_audio_hold  # noqa: E402
+
+
+def fresh(workflow=None):
+    blocks = minimax.MiniMaxH3Blocks()
+    if workflow:
+        blocks = blocks.get_workflow(workflow)
+    pipeline = blocks.init_pipeline()
+    insert_audio_hold(pipeline)
+    return pipeline
+
+
+def names(pipeline):
+    return [
+        (prefix, list(sequence.sub_blocks))
+        for prefix, sequence in h3_hold.core_denoise_sequences(pipeline)
+    ]
+
+
+class TestInsert:
+    @pytest.mark.parametrize("workflow", ["t2va", "fl2va"])
+    def test_follows_after_denoise(self, workflow):
+        pipeline = fresh(workflow)
+        assert insert_latent_anchor(pipeline) is True
+        ((prefix, order),) = names(pipeline)
+        after = order.index(prefix + ANCHOR_AFTER)
+        assert order[after + 1] == prefix + ANCHOR_BLOCK
+
+    def test_idempotent(self):
+        pipeline = fresh()
+        insert_latent_anchor(pipeline)
+        once = names(pipeline)
+        insert_latent_anchor(pipeline)
+        assert names(pipeline) == once
+
+    def test_ref2va_gets_no_anchor(self):
+        pipeline = fresh("ref2va")
+        before = names(pipeline)
+        assert insert_latent_anchor(pipeline) is False
+        assert names(pipeline) == before
+
+    def test_takes_latent_anchor_as_an_input(self):
+        inputs = {param.name for param in DwH3LatentAnchorStep().inputs}
+        assert LATENT_ANCHOR_INPUT in inputs
+
+
+class TestStep:
+    def state(self, latents, anchor=None):
+        state = PipelineState()
+        state.set("latents", latents)
+        state.set(LATENT_ANCHOR_INPUT, anchor)
+        state.set("height", 32)
+        state.set("width", 32)
+        return state
+
+    def test_without_an_anchor_the_latents_are_untouched(self):
+        latents = synthetic_latent()
+        state = self.state(latents)
+        _, state = DwH3LatentAnchorStep()(None, state)
+        assert state.get("latents") is latents
+
+    def test_with_an_anchor_the_latents_take_its_statistics(self, monkeypatch):
+        from dw.pipeline_processors import h3_anchor_steps
+
+        anchor_latent = synthetic_latent(seed=3)
+        monkeypatch.setattr(
+            h3_anchor_steps,
+            "anchor_statistics",
+            lambda components, frames, height, width: channel_statistics(anchor_latent),
+        )
+        frames = np.zeros((5, 32, 32, 3), dtype=np.uint8)
+        state = self.state(synthetic_latent(), frames)
+        _, state = DwH3LatentAnchorStep()(None, state)
+        mean, std = channel_statistics(state.get("latents"))
+        anchor_mean, anchor_std = channel_statistics(anchor_latent)
+        assert torch.allclose(mean, anchor_mean, atol=1e-5)
+        assert torch.allclose(std, anchor_std, atol=1e-5)
diff --git a/tests/test_h3_rules.py b/tests/test_h3_rules.py
index d5e547a1..58c1619f 100644
--- a/tests/test_h3_rules.py
+++ b/tests/test_h3_rules.py
@@ -50,7 +50,7 @@ def test_h3_rules_imports_neither_torch_nor_diffusers():
     assert _imported_heavy_modules() == ""
 
 
-@pytest.mark.parametrize("module", ["h3_hold", "h3_guides", "h3_mask"])
+@pytest.mark.parametrize("module", ["h3_hold", "h3_guides", "h3_mask", "h3_anchor"])
 def test_the_block_owners_import_no_diffusers(module):
     # Their blocks are h3_hold_steps and h3_guide_steps, which import diffusers
     # when they load: validation imports h3_guides, so those wait for a block
diff --git a/tests/test_validation.py b/tests/test_validation.py
index 83ddd45c..2ea5c943 100644
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -201,6 +201,7 @@ def test_validation_does_not_import_the_workflow_module():
     "select",
     "chain_prompts",
     "guide_chain",
+    "latent_anchor",
     "task_signatures",
     "task_remote_code",
     "component_types",

From 98bdc94eecd46de1420ca59015d8a44ca1c3a619 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 08:31:41 -0600
Subject: [PATCH 40/74] feat(h3): #1173 - ruff format

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/guides.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/dw/guides.py b/dw/guides.py
index 51df4c1b..aab563d1 100644
--- a/dw/guides.py
+++ b/dw/guides.py
@@ -346,7 +346,10 @@ def anchor_errors(workflow_definition, source_indices=None):
     ):
         base = ("steps", source, "pipeline")
         arguments = pipeline.get("arguments")
-        if isinstance(arguments, dict) and arguments.get(LATENT_ANCHOR_INPUT) is not None:
+        if (
+            isinstance(arguments, dict)
+            and arguments.get(LATENT_ANCHOR_INPUT) is not None
+        ):
             problem = _takes_anchor_problem(pipeline, LATENT_ANCHOR_INPUT)
             if problem:
                 errors.append(

From d1f41ecebb5e2d3b7b8d8280d105390ccbb91c62 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 08:48:22 -0600
Subject: [PATCH 41/74] fix(klein): #1168 - configuration.remove_blocks,
 dropped prompt_upsampler from Klein definitions

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 docs/WORKFLOW_GUIDE.md                        |  7 ++++
 docs/proposals/klein-skip-prompt-upsampler.md |  4 ++
 dw/pipeline_processors/components.py          | 37 ++++++++++++++++++
 dw/workflow_schema.json                       |  9 +++++
 plugins/dw/skills/flux3-klein/SKILL.md        |  5 +++
 tests/test_flux3_catalog.py                   | 25 ++++++++++++
 tests/test_pipeline_components.py             | 39 +++++++++++++++++++
 tests/test_step_cache.py                      | 10 ++---
 workflows/models/flux3-klein.json             |  1 +
 workflows/templates/flux3/image-edit.json     |  1 +
 workflows/templates/flux3/layout-boxes.json   |  1 +
 .../templates/flux3/multi-reference.json      |  1 +
 workflows/templates/flux3/text-to-image.json  |  1 +
 .../flux3/upsample-then-generate.json         |  1 +
 14 files changed, 137 insertions(+), 5 deletions(-)

diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index 40058929..66d6b59b 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1637,6 +1637,13 @@ and `load_components` pulls the weights:
   the resolution its image references are encoded at. A name the pipeline does not
   declare raises rather than passing quietly, since a dropped config reads as a setting
   that did nothing.
+- `remove_blocks` — names of top-level blocks to pop off a modular pipeline's block graph
+  once it is built (`blocks.sub_blocks.pop`). It is for a block that always runs and has no
+  input to turn it off. FLUX 3 Klein's `prompt_upsampler` is one: it rewrites every
+  caption and needs an 18.8 GB component, so every Klein definition declares
+  `"remove_blocks": ["prompt_upsampler"]` and the explicit upsample step is the only
+  upsampler. A name the pipeline has no block for raises at load, listing the blocks it
+  has (validate does not build the pipeline, so it cannot see this).
 - `components_manager` — attaches a `ComponentsManager`, which tracks the pipeline's
   components. With `enable_auto_cpu_offload` it keeps only the running components on the
   device and moves the rest to system memory, reserving `memory_reserve_margin`
diff --git a/docs/proposals/klein-skip-prompt-upsampler.md b/docs/proposals/klein-skip-prompt-upsampler.md
index 97e3d7f2..8fefca09 100644
--- a/docs/proposals/klein-skip-prompt-upsampler.md
+++ b/docs/proposals/klein-skip-prompt-upsampler.md
@@ -38,3 +38,7 @@ before; popping the block fixes that too.
 
 `layout-boxes` passes the caption through as written; `boxes` as an input for the
 in-pipeline upsampler is not used by any template, so popping loses nothing.
+
+## Outcome
+
+Option 1 shipped (#1168): `configuration.remove_blocks`. Validate cannot check a name against the block graph (it does not build the pipeline), so an unknown name is refused when the pipeline loads, before any weights are pulled for the step to use.
diff --git a/dw/pipeline_processors/components.py b/dw/pipeline_processors/components.py
index c4537c83..ebba6b28 100644
--- a/dw/pipeline_processors/components.py
+++ b/dw/pipeline_processors/components.py
@@ -601,6 +601,40 @@ def get_block_configs(configuration, component):
     return dict(configs)
 
 
+def remove_pipeline_blocks(configuration, component):
+    """Pop the top-level blocks a workflow's `remove_blocks` names off a modular pipeline.
+
+    A block that always runs - Klein's `prompt_upsampler` is an AutoPipelineBlocks whose
+    default branch fires on every call - has no input that turns it off. Diffusers'
+    documented skip is `blocks.sub_blocks.pop(name)`, which this declares. Which block a
+    model needs gone is the workflow's knowledge, not the engine's.
+
+    Raises:
+        ValueError: If the pipeline has no block graph, or has no block by a name
+    """
+    names = configuration.get("remove_blocks", None)
+    if not names:
+        return []
+
+    sub_blocks = getattr(getattr(component, "blocks", None), "sub_blocks", None)
+    if sub_blocks is None or not has_method(sub_blocks, "pop"):
+        raise ValueError(
+            f"'remove_blocks' is only supported on modular pipelines, "
+            f"{type(component).__name__} has no block graph"
+        )
+    unknown = [name for name in names if name not in sub_blocks]
+    if unknown:
+        raise ValueError(
+            f"{type(component).__name__} has no block named "
+            f"{', '.join(sorted(set(unknown)))} - its blocks are "
+            f"{', '.join(sub_blocks) or 'none'}"
+        )
+    for name in names:
+        sub_blocks.pop(name, None)
+    logger.info(f"Removed pipeline blocks: {', '.join(names)}")
+    return list(names)
+
+
 def load_component(
     component_name,
     configuration,
@@ -715,6 +749,9 @@ def from_pretrained():
                 # is the whole component as quantized (dw/quantized_cache.py)
                 quantized_cache.load_components(component, load_components_arguments)
 
+            # Blocks the workflow declares gone (Klein's prompt_upsampler)
+            remove_pipeline_blocks(configuration, component)
+
             # MiniMax-H3 takes `hold_audio` on every core-denoise workflow; a
             # no-op on any other pipeline (dw/pipeline_processors/h3_hold.py)
             insert_audio_hold(component)
diff --git a/dw/workflow_schema.json b/dw/workflow_schema.json
index 8afa3251..fe85f2fc 100644
--- a/dw/workflow_schema.json
+++ b/dw/workflow_schema.json
@@ -676,6 +676,15 @@
                         }
                     }
                 },
+                "remove_blocks": {
+                    "description": "Names of top-level blocks to pop off a modular pipeline's block graph once it is built (blocks.sub_blocks.pop). For a block that always runs and has no input that turns it off: FLUX 3 Klein's 'prompt_upsampler' rewrites every caption and needs an 18.8 GB component, so its workflows remove it and use an explicit upsample step instead (#1168). A name the pipeline has no block for is an error.",
+                    "type": "array",
+                    "items": {
+                        "type": "string",
+                        "minLength": 1
+                    },
+                    "uniqueItems": true
+                },
                 "configs": {
                     "description": "Values a modular pipeline's blocks declare and read while they run - not components, and not call arguments. Keys are whatever the pipeline itself declares, so what may be set here depends on the model: MiniMax-H3 declares 'canvas_short_edge', 'canvas_max_pixels' and 'reference_image_short_edge'. A name the pipeline does not declare is an error rather than a setting that quietly did nothing.",
                     "type": "object",
diff --git a/plugins/dw/skills/flux3-klein/SKILL.md b/plugins/dw/skills/flux3-klein/SKILL.md
index 736988b6..cc78419e 100644
--- a/plugins/dw/skills/flux3-klein/SKILL.md
+++ b/plugins/dw/skills/flux3-klein/SKILL.md
@@ -83,6 +83,11 @@ argument, or `output:` for an earlier run's file.
 - The upsampler (about 18 GB) and Klein (about 19 GB) do not fit a 24 GB
   card together. Keep `release_models` on the upsample step and load Klein
   after it.
+- The in-pipeline upsampler is removed: every Klein definition sets
+  `remove_blocks: ["prompt_upsampler"]`, so the explicit upsample step is the only
+  upsampler and Klein receives the caption as written, never rewritten twice. A Klein
+  workflow you author needs the same setting, plus `load_components.names` listing only
+  the four components the run uses.
 
 ## Prompts
 
diff --git a/tests/test_flux3_catalog.py b/tests/test_flux3_catalog.py
index 36ffe9ca..b1194641 100644
--- a/tests/test_flux3_catalog.py
+++ b/tests/test_flux3_catalog.py
@@ -201,3 +201,28 @@ def test_every_klein_step_skips_the_repo_s_prompt_upsampler(path):
             "transformer",
             "vae",
         ], path
+
+
+KLEIN_DEFINITIONS = [
+    "workflows/templates/flux3/text-to-image.json",
+    "workflows/templates/flux3/image-edit.json",
+    "workflows/templates/flux3/multi-reference.json",
+    "workflows/templates/flux3/layout-boxes.json",
+    "workflows/templates/flux3/upsample-then-generate.json",
+    "workflows/models/flux3-klein.json",
+]
+
+
+@pytest.mark.parametrize("path", KLEIN_DEFINITIONS)
+def test_every_klein_definition_removes_the_in_pipeline_upsampler(path):
+    """#1168: the block always runs and needs the 18.8 GB upsampler loaded"""
+    definition = load(os.path.join(ROOT, path))
+    klein = [
+        s["pipeline"]["configuration"]
+        for s in definition["steps"]
+        if "pipeline" in s
+        and s["pipeline"]["configuration"].get("component_type") == "ModularPipeline"
+    ]
+    assert klein
+    for configuration in klein:
+        assert configuration["remove_blocks"] == ["prompt_upsampler"]
diff --git a/tests/test_pipeline_components.py b/tests/test_pipeline_components.py
index c4302e73..a6e6120c 100644
--- a/tests/test_pipeline_components.py
+++ b/tests/test_pipeline_components.py
@@ -13,6 +13,7 @@
     enable_cache_on_transformer,
     get_block_configs,
     load_component,
+    remove_pipeline_blocks,
 )
 from dw.pipeline_processors.pipeline import (
     Pipeline,
@@ -1091,3 +1092,41 @@ def test_an_output_with_no_videos_is_untouched(self):
         output = {"latents": [1.0]}
         assert attach_video_frame_rate(self._pipeline(fps=24), output) is output
         assert output == {"latents": [1.0]}
+
+
+class TestRemoveBlocks:
+    """`remove_blocks` pops named top-level blocks off a built modular pipeline (#1168)"""
+
+    def pipeline(self):
+        from collections import OrderedDict
+        from types import SimpleNamespace
+
+        blocks = SimpleNamespace(
+            sub_blocks=OrderedDict(prompt_upsampler="u", text_encoder="t", denoise="d")
+        )
+        return SimpleNamespace(blocks=blocks)
+
+    def test_the_named_block_is_popped(self):
+        pipeline = self.pipeline()
+        removed = remove_pipeline_blocks(
+            {"remove_blocks": ["prompt_upsampler"]}, pipeline
+        )
+        assert removed == ["prompt_upsampler"]
+        assert list(pipeline.blocks.sub_blocks) == ["text_encoder", "denoise"]
+
+    def test_an_unknown_name_is_refused_and_nothing_is_popped(self):
+        pipeline = self.pipeline()
+        with pytest.raises(ValueError, match="no block named prompt_upsamplr"):
+            remove_pipeline_blocks(
+                {"remove_blocks": ["prompt_upsampler", "prompt_upsamplr"]}, pipeline
+            )
+        assert "prompt_upsampler" in pipeline.blocks.sub_blocks
+
+    def test_a_pipeline_without_the_setting_is_unchanged(self):
+        pipeline = self.pipeline()
+        assert remove_pipeline_blocks({}, pipeline) == []
+        assert len(pipeline.blocks.sub_blocks) == 3
+
+    def test_a_pipeline_with_no_block_graph_is_an_error(self):
+        with pytest.raises(ValueError, match="only supported on modular pipelines"):
+            remove_pipeline_blocks({"remove_blocks": ["x"]}, StandardLike())
diff --git a/tests/test_step_cache.py b/tests/test_step_cache.py
index 1a3b9150..33424a48 100644
--- a/tests/test_step_cache.py
+++ b/tests/test_step_cache.py
@@ -668,19 +668,19 @@ def test_normalized_downstream_false_when_nothing_references_it():
     "expand-prompt.json": {},
     "face-drift-check.json": {},
     "flux3/image-edit.json": {
-        "edit": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
+        "edit": "af1014c287ace2f22907a6a91d05107d5874f22f6aeb066c58c639c0243d6a66"
     },
     "flux3/layout-boxes.json": {
-        "layout_to_image": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
+        "layout_to_image": "af1014c287ace2f22907a6a91d05107d5874f22f6aeb066c58c639c0243d6a66"
     },
     "flux3/multi-reference.json": {
-        "edit": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
+        "edit": "af1014c287ace2f22907a6a91d05107d5874f22f6aeb066c58c639c0243d6a66"
     },
     "flux3/text-to-image.json": {
-        "text_to_image": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
+        "text_to_image": "af1014c287ace2f22907a6a91d05107d5874f22f6aeb066c58c639c0243d6a66"
     },
     "flux3/upsample-then-generate.json": {
-        "text_to_image": "8ba947272298d95529e632f381db181ab573ca6c08b25c1e293e80fd70d1147e"
+        "text_to_image": "af1014c287ace2f22907a6a91d05107d5874f22f6aeb066c58c639c0243d6a66"
     },
     "generate-speech.json": {},
     "image-edit.json": {
diff --git a/workflows/models/flux3-klein.json b/workflows/models/flux3-klein.json
index 07bca7af..0ddd763a 100644
--- a/workflows/models/flux3-klein.json
+++ b/workflows/models/flux3-klein.json
@@ -21,6 +21,7 @@
             "pipeline": {
                 "configuration": {
                     "component_type": "ModularPipeline",
+                    "remove_blocks": ["prompt_upsampler"],
                     "load_components": {
                         "names": [
                             "text_encoder",
diff --git a/workflows/templates/flux3/image-edit.json b/workflows/templates/flux3/image-edit.json
index 26409281..c9b7dc18 100644
--- a/workflows/templates/flux3/image-edit.json
+++ b/workflows/templates/flux3/image-edit.json
@@ -36,6 +36,7 @@
             "pipeline": {
                 "configuration": {
                     "component_type": "ModularPipeline",
+                    "remove_blocks": ["prompt_upsampler"],
                     "load_components": {
                         "names": [
                             "text_encoder",
diff --git a/workflows/templates/flux3/layout-boxes.json b/workflows/templates/flux3/layout-boxes.json
index 982ae9ed..c2baa227 100644
--- a/workflows/templates/flux3/layout-boxes.json
+++ b/workflows/templates/flux3/layout-boxes.json
@@ -31,6 +31,7 @@
             "pipeline": {
                 "configuration": {
                     "component_type": "ModularPipeline",
+                    "remove_blocks": ["prompt_upsampler"],
                     "load_components": {
                         "names": [
                             "text_encoder",
diff --git a/workflows/templates/flux3/multi-reference.json b/workflows/templates/flux3/multi-reference.json
index 358cfb13..1ab43640 100644
--- a/workflows/templates/flux3/multi-reference.json
+++ b/workflows/templates/flux3/multi-reference.json
@@ -39,6 +39,7 @@
             "pipeline": {
                 "configuration": {
                     "component_type": "ModularPipeline",
+                    "remove_blocks": ["prompt_upsampler"],
                     "load_components": {
                         "names": [
                             "text_encoder",
diff --git a/workflows/templates/flux3/text-to-image.json b/workflows/templates/flux3/text-to-image.json
index 5049ff5f..30c278d1 100644
--- a/workflows/templates/flux3/text-to-image.json
+++ b/workflows/templates/flux3/text-to-image.json
@@ -30,6 +30,7 @@
             "pipeline": {
                 "configuration": {
                     "component_type": "ModularPipeline",
+                    "remove_blocks": ["prompt_upsampler"],
                     "load_components": {
                         "names": [
                             "text_encoder",
diff --git a/workflows/templates/flux3/upsample-then-generate.json b/workflows/templates/flux3/upsample-then-generate.json
index aecb3768..6500a8e4 100644
--- a/workflows/templates/flux3/upsample-then-generate.json
+++ b/workflows/templates/flux3/upsample-then-generate.json
@@ -45,6 +45,7 @@
             "pipeline": {
                 "configuration": {
                     "component_type": "ModularPipeline",
+                    "remove_blocks": ["prompt_upsampler"],
                     "load_components": {
                         "names": [
                             "text_encoder",

From b936337e19a80c5a469ae5818e4e3d2c700da2b4 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 08:56:26 -0600
Subject: [PATCH 42/74] feat(h3): #1173 - latent_anchor asks the guides step
 rule; refuse references

The bounce: _takes_anchor_problem copied the guides rule without its
references refusal. latent_anchor and the chain segment_1 now ask
_takes_guides_problem for their own argument name; _with_latent_anchor
refuses references at run time; a chain anchor refuses
segment_argument 'references' and step references.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md               |   2 +-
 docs/WORKFLOW_GUIDE.md             |   3 +-
 dw/guides.py                       |  43 ++++-------
 dw/pipeline_processors/chain.py    |  14 ++--
 dw/pipeline_processors/h3_rules.py |  12 +++-
 dw/pipeline_processors/pipeline.py |   9 ++-
 tests/test_h3_anchor.py            | 112 ++++++++++++++++++++++++++++-
 7 files changed, 157 insertions(+), 38 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index ce867181..72c505c9 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and `not_h3`), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values` |
+| H3 audio hold, refine, tiled refine, guide layout, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index 66d6b59b..e367086f 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1859,7 +1859,8 @@ first segment's look. Segment 1 itself is not corrected.
 |---|---|
 | `anchor` | `none` (default) or `segment_1`; anything else is refused, naming the values |
 | `segment_1` continuity | `last_frame` or `guide`; refused with any other |
-| where | MiniMax-H3 `t2va` or `fl2va`; `latent_anchor` and `segment_1` are refused on `ref2va` and on a non-H3 step |
+| where | MiniMax-H3 `t2va` or `fl2va`, where `guides` run; `latent_anchor` and `segment_1` are refused on `ref2va`, on a step that passes `references` (that call runs ref2va), and on a non-H3 step |
+| `segment_1` carry | refused with `segment_argument: "references"` - the later segments would run ref2va |
 | frames | cut to a whole-latent length (1, 5 or 17m + 5) |
 | `refine_tiles` | refused beside `latent_anchor`, as the other per-call H3 arguments are |
 
diff --git a/dw/guides.py b/dw/guides.py
index aab563d1..5aa12f0c 100644
--- a/dw/guides.py
+++ b/dw/guides.py
@@ -26,10 +26,11 @@
 `carry_frames` it would not read (`guide_chain_problems`, shared with the run).
 
 The latent statistics anchor (#1166) is refused here too, by `anchor_errors`:
-a `latent_anchor` argument, or a chain's `anchor: "segment_1"`, off an H3
-t2va/fl2va step, and a chain `anchor` that is unknown or paired with a
-continuity other than guide or last_frame (`chain_anchor_problem`, shared with
-the run).
+a `latent_anchor` argument, or a chain's `anchor: "segment_1"`, on a step the
+guides step rule refuses (`_takes_guides_problem`: off H3, ref2va, or passing
+`references`), and a chain `anchor` that is unknown, paired with a continuity
+other than guide or last_frame, or carried into `references`
+(`chain_anchor_problem`, shared with the run).
 """
 
 from . import references
@@ -105,12 +106,14 @@ def _clip_frames(video, base_dir, probe, with_audio=False):
     return None, None
 
 
-def _takes_guides_problem(pipeline, arguments):
-    """Why this step cannot take guides, or None: it is not MiniMax-H3
-    (`h3_rules.not_h3`), or it loads ref2va or passes `references`. The one
-    step rule for both a `guides` argument and a guide chain. A pipeline or
+def _takes_guides_problem(pipeline, arguments, argument=GUIDES_INPUT):
+    """Why this step cannot take `argument`, or None: it is not MiniMax-H3
+    (`h3_rules.not_h3`), or it loads ref2va or passes `references` (a call
+    with references routes to ref2va). The one step rule for a `guides`
+    argument, a guide chain, a `latent_anchor` argument and a chain's
+    `anchor: "segment_1"` - every H3 t2va/fl2va-only input. A pipeline or
     workflow that is not literal is left to the run."""
-    h3_problem = not_h3(pipeline, GUIDES_INPUT)
+    h3_problem = not_h3(pipeline, argument)
     if h3_problem:
         return h3_problem
     from_pretrained = pipeline.get(FROM_PRETRAINED_KEY)
@@ -121,7 +124,7 @@ def _takes_guides_problem(pipeline, arguments):
         isinstance(arguments, dict) and arguments.get("references") is not None
     )
     if workflow in REFERENCE_WORKFLOWS or passes_references:
-        return "guides cannot be combined with ref2va / references - use t2va or fl2va"
+        return f"{argument} cannot be combined with ref2va / references - use t2va or fl2va"
     return None
 
 
@@ -319,22 +322,6 @@ def guide_lengths(pipeline, base_dir=None, probe=None):
     return lengths
 
 
-def _takes_anchor_problem(pipeline, argument):
-    """Why this step cannot take the latent anchor, or None: it is not
-    MiniMax-H3 (`h3_rules.not_h3`), or it loads ref2va, which has no anchor
-    step. A pipeline or workflow that is not literal is left to the run."""
-    h3_problem = not_h3(pipeline, argument)
-    if h3_problem:
-        return h3_problem
-    from_pretrained = pipeline.get(FROM_PRETRAINED_KEY)
-    workflow = (
-        from_pretrained.get(WORKFLOW_KEY) if isinstance(from_pretrained, dict) else None
-    )
-    if workflow in REFERENCE_WORKFLOWS:
-        return f"{argument} runs on MiniMax-H3 t2va or fl2va only, not ref2va"
-    return None
-
-
 def anchor_errors(workflow_definition, source_indices=None):
     """Every latent statistics anchor refused before the run, as
     [{path, message}]: a `latent_anchor` argument on a step that cannot take
@@ -350,7 +337,7 @@ def anchor_errors(workflow_definition, source_indices=None):
             isinstance(arguments, dict)
             and arguments.get(LATENT_ANCHOR_INPUT) is not None
         ):
-            problem = _takes_anchor_problem(pipeline, LATENT_ANCHOR_INPUT)
+            problem = _takes_guides_problem(pipeline, arguments, LATENT_ANCHOR_INPUT)
             if problem:
                 errors.append(
                     {
@@ -369,7 +356,7 @@ def anchor_errors(workflow_definition, source_indices=None):
         anchor = chain.get("anchor", ANCHOR_NONE)
         if anchor == ANCHOR_NONE or references.is_ref(references.UNRESOLVED, anchor):
             continue
-        problem = _takes_anchor_problem(pipeline, "chain anchor 'segment_1'")
+        problem = _takes_guides_problem(pipeline, arguments, "chain anchor 'segment_1'")
         if problem:
             errors.append({"path": path, "message": f"{problem}{where}"})
     return errors
diff --git a/dw/pipeline_processors/chain.py b/dw/pipeline_processors/chain.py
index cfe897da..01898eb0 100644
--- a/dw/pipeline_processors/chain.py
+++ b/dw/pipeline_processors/chain.py
@@ -434,7 +434,7 @@ def run_chain(pipeline, chain_definition, arguments):
     if config.continuity == GUIDE_CONTINUITY:
         _check_guide_chain(pipeline, arguments, chain_definition, config)
     if config.anchor == ANCHOR_SEGMENT_1:
-        _check_anchor_chain(pipeline)
+        _check_anchor_chain(pipeline, arguments)
 
     # With save_segments, each completed segment is written to disk and its
     # frames freed, bounding memory to one segment - a crash leaves the
@@ -620,9 +620,15 @@ def _record_seam(shot, segment, config, applied):
     )
 
 
-def _check_anchor_chain(pipeline):
-    """Refuse an `anchor: "segment_1"` chain whose loaded pipeline has no
-    latent anchor step - not MiniMax-H3 t2va/fl2va - before segment 1 renders."""
+def _check_anchor_chain(pipeline, arguments):
+    """Refuse an `anchor: "segment_1"` chain whose segments could not run the
+    latent anchor step - a loaded pipeline that is not MiniMax-H3 t2va/fl2va,
+    or a step that passes references (ref2va) - before segment 1 renders."""
+    if arguments.get("references") is not None:
+        raise ValueError(
+            "Chain anchor 'segment_1' cannot be combined with references - its "
+            "segments would run ref2va, which has no anchor step"
+        )
     loaded = getattr(pipeline, "pipeline", None)
     if loaded is not None:
         refusal = anchor_refusal(loaded)
diff --git a/dw/pipeline_processors/h3_rules.py b/dw/pipeline_processors/h3_rules.py
index 62e2f467..89052967 100644
--- a/dw/pipeline_processors/h3_rules.py
+++ b/dw/pipeline_processors/h3_rules.py
@@ -533,7 +533,8 @@ def guide_frame_problem(frame):
 def chain_anchor_problem(chain):
     """The reason a chain block's `anchor` is refused for its own settings, or
     None: an unknown value (naming the values), or `segment_1` with a continuity
-    other than guide or last_frame. Unresolved references are left to the run;
+    other than guide or last_frame, or carrying into `references`. Unresolved
+    references are left to the run;
     whether the step is H3 is the caller's to ask (`not_h3`)."""
     anchor = chain.get("anchor", ANCHOR_NONE)
     if isinstance(anchor, str) and references.is_ref(references.UNRESOLVED, anchor):
@@ -556,6 +557,15 @@ def chain_anchor_problem(chain):
             f"{' or '.join(repr(c) for c in ANCHOR_CONTINUITIES)} - its segments "
             f"are MiniMax-H3 t2va/fl2va generations; got {continuity!r}"
         )
+    # The carry lands in `segment_argument` on every later segment, and a call
+    # that passes references runs ref2va, which has no anchor step - the
+    # anchor would be dropped without a word
+    if chain.get("segment_argument") == "references":
+        return (
+            "anchor 'segment_1' cannot carry into 'references' - a segment that "
+            "passes references runs ref2va, which has no anchor step; use "
+            "segment_argument 'image' (t2va/fl2va)"
+        )
     return None
 
 
diff --git a/dw/pipeline_processors/pipeline.py b/dw/pipeline_processors/pipeline.py
index 6ce8a069..e06467c1 100644
--- a/dw/pipeline_processors/pipeline.py
+++ b/dw/pipeline_processors/pipeline.py
@@ -811,7 +811,9 @@ def _with_latent_anchor(self, arguments):
 
         Raises:
             ValueError: If this pipeline has no anchor step (not MiniMax-H3
-                t2va/fl2va), or the value is not frames of a video
+                t2va/fl2va), the step also passes `references` (the call would
+                run ref2va, which has no anchor step), or the value is not
+                frames of a video
         """
         value = arguments.get(LATENT_ANCHOR_INPUT)
         if value is None:
@@ -820,6 +822,11 @@ def _with_latent_anchor(self, arguments):
         refusal = anchor_refusal(self.pipeline)
         if refusal:
             raise ValueError(f"{where}: {refusal}")
+        if arguments.get("references") is not None:
+            raise ValueError(
+                f"{where} cannot be combined with references - the call would run "
+                f"ref2va, which has no anchor step; use latent_anchor on t2va or fl2va"
+            )
         arguments = dict(arguments)
         arguments[LATENT_ANCHOR_INPUT] = prepared_anchor(where, value, self.base_dir)
         return arguments
diff --git a/tests/test_h3_anchor.py b/tests/test_h3_anchor.py
index 52e12713..fbbbf191 100644
--- a/tests/test_h3_anchor.py
+++ b/tests/test_h3_anchor.py
@@ -118,6 +118,27 @@ def test_config_refuses_another_continuity(self):
                 {"segments": 2, "anchor": "segment_1", "continuity": "last_segment"}, {}
             )
 
+    def test_config_refuses_a_carry_into_references(self):
+        with pytest.raises(ValueError, match="'references'"):
+            ChainConfig(
+                {
+                    "segments": 2,
+                    "anchor": "segment_1",
+                    "segment_argument": "references",
+                },
+                {},
+            )
+
+    def test_run_refuses_references_before_segment_1(self):
+        pipeline = FakePipeline(video_output)
+        with pytest.raises(ValueError, match="references"):
+            run_chain(
+                pipeline,
+                {"segments": 2, "anchor": "segment_1"},
+                {"prompt": "p", "references": ["a.png"]},
+            )
+        assert pipeline.calls == []
+
     @pytest.mark.parametrize("continuity", ["last_frame", "guide"])
     def test_rule_accepts_the_anchor_continuities(self, continuity):
         chain = {"anchor": "segment_1", "continuity": continuity}
@@ -182,7 +203,7 @@ def test_segment_1_off_h3(self):
     def test_segment_1_on_ref2va(self):
         chain = {"segments": 2, "anchor": "segment_1"}
         (error,) = anchor_errors(definition("ref2va", chain=chain))
-        assert "not ref2va" in error["message"]
+        assert "ref2va / references" in error["message"]
 
     def test_latent_anchor_off_h3(self):
         workflow = definition(pipeline_class="WanPipeline", latent_anchor="clip.mp4")
@@ -191,7 +212,50 @@ def test_latent_anchor_off_h3(self):
 
     def test_latent_anchor_on_ref2va(self):
         (error,) = anchor_errors(definition("ref2va", latent_anchor="clip.mp4"))
-        assert "not ref2va" in error["message"]
+        assert "ref2va / references" in error["message"]
+
+    @pytest.mark.parametrize("workflow", [None, "t2va", "fl2va"])
+    def test_latent_anchor_with_references(self, workflow):
+        # A call that passes references runs ref2va, which has no anchor step
+        step = definition(workflow, latent_anchor="clip.mp4", references=["a.png"])
+        (error,) = anchor_errors(step)
+        assert error["path"] == "steps[0].pipeline.arguments.latent_anchor"
+        assert "references" in error["message"]
+
+    def test_segment_1_with_references(self):
+        chain = {"segments": 2, "anchor": "segment_1"}
+        (error,) = anchor_errors(definition(None, chain, references=["a.png"]))
+        assert error["path"] == "steps[0].pipeline.chain.anchor"
+        assert "references" in error["message"]
+
+    def test_segment_1_carrying_into_references(self):
+        chain = {
+            "segments": 2,
+            "anchor": "segment_1",
+            "continuity": "last_frame",
+            "segment_argument": "references",
+        }
+        (error,) = anchor_errors(definition(chain=chain))
+        assert error["path"] == "steps[0].pipeline.chain.anchor"
+        assert "'references'" in error["message"]
+
+    @pytest.mark.parametrize("workflow", ["ref2va", "fl2va"])
+    def test_the_step_rule_is_the_guides_rule(self, workflow):
+        # One owner: the anchor's refusal is the `guides` check's own reason,
+        # asked for the anchor's argument name
+        from dw.guides import _takes_guides_problem
+
+        arguments = {"latent_anchor": "clip.mp4"}
+        if workflow == "fl2va":
+            arguments["references"] = ["a.png"]
+        step = definition(workflow, **arguments)
+        pipeline = step["steps"][0]["pipeline"]
+        (error,) = anchor_errors(step)
+
+        expected = _takes_guides_problem(pipeline, arguments, LATENT_ANCHOR_INPUT)
+        assert expected and error["message"].startswith(expected)
+        guides_reason = _takes_guides_problem(pipeline, arguments)
+        assert expected == guides_reason.replace("guides", LATENT_ANCHOR_INPUT, 1)
 
 
 minimax = pytest.importorskip("diffusers.modular_pipelines.minimax_h3")
@@ -275,3 +339,47 @@ def test_with_an_anchor_the_latents_take_its_statistics(self, monkeypatch):
         anchor_mean, anchor_std = channel_statistics(anchor_latent)
         assert torch.allclose(mean, anchor_mean, atol=1e-5)
         assert torch.allclose(std, anchor_std, atol=1e-5)
+
+
+@pytest.fixture(scope="module")
+def anchored():
+    pipeline = fresh("fl2va")
+    assert insert_latent_anchor(pipeline)
+    return pipeline
+
+
+class TestWithLatentAnchor:
+    """The run-time refusal, as `Pipeline._with_guides` refuses guides."""
+
+    def step(self, pipeline):
+        from types import SimpleNamespace
+
+        return SimpleNamespace(name="video", pipeline=pipeline, base_dir=None)
+
+    def frames(self):
+        return np.zeros((5, 4, 6, 3), dtype=np.uint8)
+
+    def test_none_passes_through(self, anchored):
+        from dw.pipeline_processors.pipeline import Pipeline
+
+        arguments = {"prompt": "x"}
+        assert Pipeline._with_latent_anchor(self.step(anchored), arguments) is arguments
+
+    def test_frames_are_prepared(self, anchored):
+        from dw.pipeline_processors.pipeline import Pipeline
+
+        result = Pipeline._with_latent_anchor(
+            self.step(anchored), {LATENT_ANCHOR_INPUT: self.frames()}
+        )
+        assert result[LATENT_ANCHOR_INPUT].shape == (5, 4, 6, 3)
+
+    def test_references_are_refused(self, anchored):
+        # With references the call runs ref2va, which has no anchor step: the
+        # anchor would otherwise be dropped without a word
+        from dw.pipeline_processors.pipeline import Pipeline
+
+        with pytest.raises(ValueError, match="references"):
+            Pipeline._with_latent_anchor(
+                self.step(anchored),
+                {LATENT_ANCHOR_INPUT: self.frames(), "references": [object()]},
+            )

From c73d0fcb00359ff187d65ed4c5f5cbbee0240b6b Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 09:20:49 -0600
Subject: [PATCH 43/74] fix(klein): #1168 - remove_blocks pops _blocks, not the
 deepcopy the blocks property returns

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 dw/pipeline_processors/components.py |  4 +++-
 tests/test_pipeline_components.py    | 24 ++++++++++++++++++++----
 2 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/dw/pipeline_processors/components.py b/dw/pipeline_processors/components.py
index ebba6b28..2c8e6f0c 100644
--- a/dw/pipeline_processors/components.py
+++ b/dw/pipeline_processors/components.py
@@ -616,7 +616,9 @@ def remove_pipeline_blocks(configuration, component):
     if not names:
         return []
 
-    sub_blocks = getattr(getattr(component, "blocks", None), "sub_blocks", None)
+    # `_blocks`, not the public `blocks`: that property hands back a deepcopy, so a pop
+    # on it succeeds and the pipeline still runs the block (#1168)
+    sub_blocks = getattr(getattr(component, "_blocks", None), "sub_blocks", None)
     if sub_blocks is None or not has_method(sub_blocks, "pop"):
         raise ValueError(
             f"'remove_blocks' is only supported on modular pipelines, "
diff --git a/tests/test_pipeline_components.py b/tests/test_pipeline_components.py
index a6e6120c..8a2f1b21 100644
--- a/tests/test_pipeline_components.py
+++ b/tests/test_pipeline_components.py
@@ -1101,10 +1101,25 @@ def pipeline(self):
         from collections import OrderedDict
         from types import SimpleNamespace
 
+        import copy
+
         blocks = SimpleNamespace(
             sub_blocks=OrderedDict(prompt_upsampler="u", text_encoder="t", denoise="d")
         )
-        return SimpleNamespace(blocks=blocks)
+
+        class Pipeline:
+            def __init__(self):
+                self._blocks = blocks
+
+            # like diffusers: the public property returns a deepcopy
+            @property
+            def blocks(self):
+                return copy.deepcopy(self._blocks)
+
+            def __call__(self):
+                return list(self._blocks.sub_blocks)
+
+        return Pipeline()
 
     def test_the_named_block_is_popped(self):
         pipeline = self.pipeline()
@@ -1112,7 +1127,8 @@ def test_the_named_block_is_popped(self):
             {"remove_blocks": ["prompt_upsampler"]}, pipeline
         )
         assert removed == ["prompt_upsampler"]
-        assert list(pipeline.blocks.sub_blocks) == ["text_encoder", "denoise"]
+        # what the pipeline actually executes, not a copy of it
+        assert pipeline() == ["text_encoder", "denoise"]
 
     def test_an_unknown_name_is_refused_and_nothing_is_popped(self):
         pipeline = self.pipeline()
@@ -1120,12 +1136,12 @@ def test_an_unknown_name_is_refused_and_nothing_is_popped(self):
             remove_pipeline_blocks(
                 {"remove_blocks": ["prompt_upsampler", "prompt_upsamplr"]}, pipeline
             )
-        assert "prompt_upsampler" in pipeline.blocks.sub_blocks
+        assert "prompt_upsampler" in pipeline()
 
     def test_a_pipeline_without_the_setting_is_unchanged(self):
         pipeline = self.pipeline()
         assert remove_pipeline_blocks({}, pipeline) == []
-        assert len(pipeline.blocks.sub_blocks) == 3
+        assert len(pipeline()) == 3
 
     def test_a_pipeline_with_no_block_graph_is_an_error(self):
         with pytest.raises(ValueError, match="only supported on modular pipelines"):

From f5bd946ec08ddf0aabe940395832978ac6f76a2d Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 09:24:11 -0600
Subject: [PATCH 44/74] fix(assess): #1179 - seam_hole not in rules_applied
 when skipped on every seam

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 dw/tasks/assess.py   | 3 +++
 tests/test_assess.py | 2 ++
 2 files changed, 5 insertions(+)

diff --git a/dw/tasks/assess.py b/dw/tasks/assess.py
index 545b4628..208dbc51 100644
--- a/dw/tasks/assess.py
+++ b/dw/tasks/assess.py
@@ -966,6 +966,9 @@ def seams_answer(media, records, source):
                 "seams": sorted(blended),
             }
         )
+        if len(blended) == len(seams):
+            # Measured nowhere: not "applied" (#394 contract, #1179)
+            answer["rules_applied"].remove("seam_hole")
     return answer
 
 
diff --git a/tests/test_assess.py b/tests/test_assess.py
index 9c99342e..61289464 100644
--- a/tests/test_assess.py
+++ b/tests/test_assess.py
@@ -388,6 +388,8 @@ def test_a_chain_crossfade_on_the_shot_is_no_hole_finding(self):
         assert [f for f in answer["findings"] if f["rule"] == "seam_hole"] == []
         skipped = [r for r in answer["rules_skipped"] if r["rule"] == "seam_hole"]
         assert skipped and skipped[0]["seams"] == [1]
+        # Skipped on every seam: measured nowhere, so not "applied" (#1179)
+        assert "seam_hole" not in answer["rules_applied"]
 
     def test_an_unrequested_hole_still_fires(self):
         # Same material and the same dip, but the fade is only in the

From e9e8efad550948c524eec40e673f1c6eea6a6ab8 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 09:29:52 -0600
Subject: [PATCH 45/74] feat(h3): #1173 - chain.anchor of any type refused
 naming the values; off-H3 refusal names only t2va/fl2va

The schema typed chain.anchor as a string, so an integer was refused as a
JSON type error without the values. The type is left to chain_anchor_problem,
which names them for every value. not_h3 takes the workflows its message
names, and the guides/anchor step rule names t2va and fl2va only.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/guides.py                       |  4 +++-
 dw/pipeline_processors/h3_rules.py | 11 ++++++++---
 dw/workflow_schema.json            |  3 +--
 tests/test_h3_anchor.py            | 28 ++++++++++++++++++++++++++++
 4 files changed, 40 insertions(+), 6 deletions(-)

diff --git a/dw/guides.py b/dw/guides.py
index 5aa12f0c..5307dc89 100644
--- a/dw/guides.py
+++ b/dw/guides.py
@@ -36,6 +36,7 @@
 from . import references
 from .adapter_compatibility import (
     FROM_PRETRAINED_KEY,
+    KEYFRAME_WORKFLOWS,
     REFERENCE_WORKFLOWS,
     WORKFLOW_KEY,
 )
@@ -113,7 +114,8 @@ def _takes_guides_problem(pipeline, arguments, argument=GUIDES_INPUT):
     argument, a guide chain, a `latent_anchor` argument and a chain's
     `anchor: "segment_1"` - every H3 t2va/fl2va-only input. A pipeline or
     workflow that is not literal is left to the run."""
-    h3_problem = not_h3(pipeline, argument)
+    # Named as taken by t2va/fl2va only: ref2va refuses it below
+    h3_problem = not_h3(pipeline, argument, taken_by=KEYFRAME_WORKFLOWS)
     if h3_problem:
         return h3_problem
     from_pretrained = pipeline.get(FROM_PRETRAINED_KEY)
diff --git a/dw/pipeline_processors/h3_rules.py b/dw/pipeline_processors/h3_rules.py
index 89052967..92ead7af 100644
--- a/dw/pipeline_processors/h3_rules.py
+++ b/dw/pipeline_processors/h3_rules.py
@@ -412,9 +412,14 @@ def tile_plan(width, height, tile_width, tile_height, overlap):
     ]
 
 
-def not_h3(pipeline, argument=HOLD_AUDIO_INPUT):
+def not_h3(pipeline, argument=HOLD_AUDIO_INPUT, taken_by=H3_WORKFLOWS):
     """Why this step's pipeline cannot take `argument` - one of the H3 block
-    arguments - or None when it can or cannot be told before the load."""
+    arguments - or None when it can or cannot be told before the load.
+    `taken_by` is the H3 workflows the message names as taking it: an
+    argument ref2va refuses (guides, latent_anchor) passes t2va/fl2va only,
+    so the refusal does not send the author to a workflow that refuses it
+    too. It only words the message - an H3 workflow outside it is left to
+    the caller's own rule, which says why."""
     configuration = pipeline.get("configuration")
     component_type = (
         configuration.get("component_type") if isinstance(configuration, dict) else None
@@ -425,7 +430,7 @@ def not_h3(pipeline, argument=HOLD_AUDIO_INPUT):
     )
     holds = (
         f"{argument} is a MiniMax-H3 argument, taken by its "
-        f"{', '.join(sorted(H3_WORKFLOWS))} workflows"
+        f"{', '.join(sorted(taken_by))} workflows"
     )
     if (
         isinstance(component_type, str)
diff --git a/dw/workflow_schema.json b/dw/workflow_schema.json
index fe85f2fc..a1fc0776 100644
--- a/dw/workflow_schema.json
+++ b/dw/workflow_schema.json
@@ -341,8 +341,7 @@
                     "default": 22
                 },
                 "anchor": {
-                    "description": "Latent statistics anchor (MiniMax-H3 t2va and fl2va only, with continuity 'guide' or 'last_frame'). 'segment_1' passes segment 1's decoded frames to every later segment as its 'latent_anchor': each later segment's video latent is rescaled, per VAE latent channel, to segment 1's latent mean and std before decode, so colour and contrast stop drifting from segment to segment. Segment 1 itself is not corrected, and audio is never touched. 'none' (the default) anchors nothing. Accepts a 'variable:' reference.",
-                    "type": "string",
+                    "description": "Latent statistics anchor (MiniMax-H3 t2va and fl2va only, with continuity 'guide' or 'last_frame'). 'segment_1' passes segment 1's decoded frames to every later segment as its 'latent_anchor': each later segment's video latent is rescaled, per VAE latent channel, to segment 1's latent mean and std before decode, so colour and contrast stop drifting from segment to segment. Segment 1 itself is not corrected, and audio is never touched. 'none' (the default) anchors nothing; any other value, of any type, is refused naming the two. Accepts a 'variable:' reference.",
                     "default": "none"
                 },
                 "segment_argument": {
diff --git a/tests/test_h3_anchor.py b/tests/test_h3_anchor.py
index fbbbf191..82b663b4 100644
--- a/tests/test_h3_anchor.py
+++ b/tests/test_h3_anchor.py
@@ -190,6 +190,34 @@ def test_an_unknown_anchor_names_the_values(self):
         assert error["path"] == "steps[0].pipeline.chain.anchor"
         assert "none, segment_1" in error["message"]
 
+    @pytest.mark.parametrize("anchor", [1, "", True, None, ["segment_1"]])
+    def test_any_other_value_names_the_values(self, anchor):
+        # The schema leaves the anchor's type to this rule, so a non-string is
+        # refused with the values named rather than as a JSON type error (#1173)
+        from dw.schema import load_schema, validate_data_all
+
+        step = definition(chain={"segments": 2, "anchor": anchor})
+        assert validate_data_all({"id": "a", **step}, load_schema("workflow")) == []
+        (error,) = anchor_errors(step)
+        assert error["path"] == "steps[0].pipeline.chain.anchor"
+        assert "expected one of none, segment_1" in error["message"]
+
+    @pytest.mark.parametrize("anchor", [1, ""])
+    def test_config_refuses_a_non_string_naming_the_values(self, anchor):
+        with pytest.raises(ValueError, match="none, segment_1"):
+            ChainConfig({"segments": 2, "anchor": anchor}, {})
+
+    @pytest.mark.parametrize(
+        "chain, arguments",
+        [({"segments": 2, "anchor": "segment_1"}, {}), (None, {"latent_anchor": "c"})],
+    )
+    def test_off_h3_names_only_the_workflows_that_take_it(self, chain, arguments):
+        # ref2va refuses the anchor, so the refusal must not send the author there
+        step = definition(chain=chain, pipeline_class="WanPipeline", **arguments)
+        (error,) = anchor_errors(step)
+        assert "taken by its fl2va, t2va workflows" in error["message"]
+        assert "ref2va" not in error["message"]
+
     def test_segment_1_with_another_continuity(self):
         chain = {"segments": 2, "anchor": "segment_1", "continuity": "last_segment"}
         (error,) = anchor_errors(definition(chain=chain))

From 571bc7defc1414109413ad45847bad6083f82d0d Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 10:03:10 -0600
Subject: [PATCH 46/74] feat(locations): #1173 - latent_anchor meets the media
 location policy at validate

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/locations.py         | 11 +++++++++-
 tests/test_h3_anchor.py | 15 ++++++++++++++
 tests/test_locations.py | 46 +++++++++++++++++++++++++++++++++++++++++
 3 files changed, 71 insertions(+), 1 deletion(-)

diff --git a/dw/locations.py b/dw/locations.py
index aa0cf2b5..5e79ff87 100644
--- a/dw/locations.py
+++ b/dw/locations.py
@@ -64,6 +64,13 @@
 MEDIA_KEY_SUFFIXES = ("_image", "_video", "_audio")
 MEDIA_KEY_NAMES = ("image", "video", "audio", "location", "from_file")
 
+# Pipeline arguments that read a file but are not named like their media, so
+# the key match above would miss them: MiniMax-H3's `latent_anchor` (#1166,
+# `h3_rules.LATENT_ANCHOR_INPUT`, pinned equal by tests/test_h3_anchor.py)
+# takes a video the way a guide's `video` does, and is refused here the same
+# way rather than only by the loader at run time (SE-F061)
+PIPELINE_MEDIA_ARGUMENTS = ("latent_anchor",)
+
 # Task arguments that name a file to read but not by the conventions above -
 # a generic name the key match would miss, so each command declares them on
 # its registration (`register_command(media_arguments=...)`,
@@ -578,7 +585,9 @@ def validate_model_name(name, base_dir=None):
 
 def _is_media_key(key):
     return isinstance(key, str) and (
-        key in MEDIA_KEY_NAMES or key.endswith(MEDIA_KEY_SUFFIXES)
+        key in MEDIA_KEY_NAMES
+        or key in PIPELINE_MEDIA_ARGUMENTS
+        or key.endswith(MEDIA_KEY_SUFFIXES)
     )
 
 
diff --git a/tests/test_h3_anchor.py b/tests/test_h3_anchor.py
index 82b663b4..b07ead85 100644
--- a/tests/test_h3_anchor.py
+++ b/tests/test_h3_anchor.py
@@ -84,6 +84,21 @@ def test_refuses_what_is_not_frames(self):
         with pytest.raises(ValueError, match="anchor"):
             prepared_anchor("anchor", 42, None)
 
+    def test_the_location_policy_checks_it_as_media(self):
+        """validate's location pass names the argument itself, since its key
+        is not named like a video (SE-F061)."""
+        from dw.locations import PIPELINE_MEDIA_ARGUMENTS
+
+        assert LATENT_ANCHOR_INPUT in PIPELINE_MEDIA_ARGUMENTS
+
+    def test_an_out_of_root_path_is_refused_at_run_time(self, monkeypatch, tmp_path):
+        from dw.security import SecurityError
+        from dw.trust import TRUST_WORKFLOWS_ENV_VAR
+
+        monkeypatch.setenv(TRUST_WORKFLOWS_ENV_VAR, "0")
+        with pytest.raises(SecurityError):
+            prepared_anchor("anchor", "/usr/share/videos/x.mp4", str(tmp_path))
+
 
 class TestChainAnchor:
     def test_segments_after_the_first_get_segment_ones_frames(self):
diff --git a/tests/test_locations.py b/tests/test_locations.py
index 6e93957d..0f4291c7 100644
--- a/tests/test_locations.py
+++ b/tests/test_locations.py
@@ -480,6 +480,52 @@ def test_a_file_url_is_an_error_with_its_path(self, untrusted, workflow_dir, key
         ]
         assert "'file' URL" in errors[0]["message"]
 
+    @pytest.mark.parametrize(
+        "anchor",
+        [
+            "/usr/share/videos/x.mp4",
+            "file:///usr/share/videos/x.mp4",
+            "http://127.0.0.1:8765/api/server",
+            "../../outside.mp4",
+            ["/usr/share/videos/x.mp4"],
+            {"location": "/usr/share/videos/x.mp4"},
+        ],
+    )
+    def test_a_latent_anchor_meets_the_media_policy(
+        self, untrusted, workflow_dir, anchor
+    ):
+        """`latent_anchor` reads a video but is not named like one, so the
+        key match alone let every location through to the run (SE-F061)."""
+        definition = {
+            "steps": [
+                {
+                    "name": "shot",
+                    "pipeline": {"arguments": {"latent_anchor": anchor}},
+                }
+            ]
+        }
+        errors = location_errors(definition, base_dir=workflow_dir)
+        assert len(errors) == 1
+        assert errors[0]["path"].startswith("steps[0].pipeline.arguments.latent_anchor")
+
+    def test_a_latent_anchor_inside_a_root_is_allowed(self, untrusted, workflow_dir):
+        clip = os.path.join(workflow_dir, "anchor.mp4")
+        open(clip, "wb").close()
+        definition = {
+            "steps": [
+                {
+                    "name": "shot",
+                    "pipeline": {
+                        "arguments": {
+                            "latent_anchor": clip,
+                            "image": "previous_result:keyframe",
+                        }
+                    },
+                }
+            ]
+        }
+        assert location_errors(definition, base_dir=workflow_dir) == []
+
     def test_an_upper_case_https_url_is_checked_as_a_url(self, untrusted, workflow_dir):
         """Not refused for its scheme, and still sent through the host policy."""
         definition = {

From 3dbf8a04eea67b45fcc1ee66d0c2bb3c813c68eb Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 10:18:18 -0600
Subject: [PATCH 47/74] feat(h3): #1174 - guide_noise: cosine-tapered
 paired-encode noise on H3 video guides

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 dw/guides.py                             | 72 ++++++++++++++++++++++-
 dw/pipeline_processors/h3_guide_steps.py | 75 ++++++++++++++++++++----
 dw/pipeline_processors/h3_rules.py       | 37 ++++++++++++
 dw/pipeline_processors/pipeline.py       | 35 +++++++++++
 dw/validation.py                         | 19 +++++-
 tests/test_validation.py                 |  2 +
 6 files changed, 227 insertions(+), 13 deletions(-)

diff --git a/dw/guides.py b/dw/guides.py
index 5307dc89..a88e9c26 100644
--- a/dw/guides.py
+++ b/dw/guides.py
@@ -31,6 +31,11 @@
 `references`), and a chain `anchor` that is unknown, paired with a continuity
 other than guide or last_frame, or carried into `references`
 (`chain_anchor_problem`, shared with the run).
+
+Fixed guide noise (#1166) is refused by `guide_noise_errors`: a `guide_noise`
+that is not a number in [0, 1) (`guide_noise_problem`, shared with the run), or
+one on a step the guides step rule refuses. `guide_noise_warnings` says when it
+would do nothing: a step with no `guides` and no guide chain.
 """
 
 from . import references
@@ -51,12 +56,14 @@
     GUIDE_CHAIN_RULE,
     GUIDE_CONTINUITY,
     GUIDE_LIMIT,
+    GUIDE_NOISE_INPUT,
     LATENT_ANCHOR_INPUT,
     RENDER_GRID,
     chain_anchor_problem,
     guide_chain_problems,
     guide_end_problem,
     guide_frame_problem,
+    guide_noise_problem,
     not_h3,
     snap_guide_length,
 )
@@ -364,4 +371,67 @@ def anchor_errors(workflow_definition, source_indices=None):
     return errors
 
 
-__all__ = ["anchor_errors", "guide_chain_errors", "guide_lengths", "guides_errors"]
+def _guide_noise_steps(workflow_definition, source_indices):
+    """(pipeline, arguments, path, where) for every step passing a non-null
+    `guide_noise`."""
+    for _, _, pipeline, source, where in references.iter_steps(
+        workflow_definition.get("steps"), source_indices, "pipeline"
+    ):
+        arguments = pipeline.get("arguments")
+        if isinstance(arguments, dict) and arguments.get(GUIDE_NOISE_INPUT) is not None:
+            path = render_path(
+                ("steps", source, "pipeline", "arguments", GUIDE_NOISE_INPUT)
+            )
+            yield pipeline, arguments, path, where
+
+
+def guide_noise_errors(workflow_definition, source_indices=None):
+    """Every `guide_noise` refused before the run, as [{path, message}]: on a
+    step that cannot take guides (off H3, ref2va, references), or not a
+    number in [0, 1)."""
+    errors = []
+    for pipeline, arguments, path, where in _guide_noise_steps(
+        workflow_definition, source_indices
+    ):
+        problem = _takes_guides_problem(
+            pipeline, arguments, GUIDE_NOISE_INPUT
+        ) or guide_noise_problem(arguments[GUIDE_NOISE_INPUT])
+        if problem:
+            errors.append({"path": path, "message": f"{problem}{where}"})
+    return errors
+
+
+def guide_noise_warnings(workflow_definition, source_indices=None):
+    """["path: message"] for a nonzero `guide_noise` on a step with no
+    `guides` and no guide chain: it noises guides, and there are none."""
+    warnings = []
+    for pipeline, arguments, path, where in _guide_noise_steps(
+        workflow_definition, source_indices
+    ):
+        if arguments[GUIDE_NOISE_INPUT] == 0:
+            continue
+        guides = arguments.get(GUIDES_INPUT)
+        chain = pipeline.get("chain")
+        continuity = chain.get("continuity") if isinstance(chain, dict) else None
+        if guides or continuity == GUIDE_CONTINUITY:
+            continue
+        if isinstance(continuity, str) and references.is_ref(
+            references.UNRESOLVED, continuity
+        ):
+            continue
+        warnings.append(
+            f"{path}: guide_noise does nothing here - it noises the step's "
+            f"guides, and this step has no 'guides' and no chain with "
+            f"continuity 'guide'{where}"
+        )
+    return warnings
+
+
+__all__ = [
+    "anchor_errors",
+    "guide_chain_errors",
+    "guide_lengths",
+    "guide_noise_errors",
+    "guide_noise_warnings",
+    "guides_errors",
+]
diff --git a/dw/pipeline_processors/h3_guide_steps.py b/dw/pipeline_processors/h3_guide_steps.py
index ababf080..45039363 100644
--- a/dw/pipeline_processors/h3_guide_steps.py
+++ b/dw/pipeline_processors/h3_guide_steps.py
@@ -50,9 +50,11 @@
     GUIDE_AUDIO_ROWS,
     GUIDE_FRAMES_PER_CHUNK,
     GUIDE_LATENTS_PER_CHUNK,
+    GUIDE_NOISE_INPUT,
     GUIDES_INPUT,
     guide_end_problem,
     guide_frame_problem,
+    guide_noise_taper,
 )
 
 logger = logging.getLogger("dw")
@@ -249,6 +251,20 @@ def fit_guide_frames(frames, height, width):
     )
 
 
+def noised_guide_pixels(pixels, strength, seed):
+    """`pixels`, `(n, h, w, 3)` uint8, as float pixels on the same 0-255 scale
+    with Gaussian noise added - `guide_noise` (#1166). Each frame's noise std is
+    its `guide_noise_taper` amplitude as a fraction of full scale: `strength`
+    on the first frame down to none on the last, the frame next to the join.
+    The noise is drawn on the CPU under `seed`, so a seeded run repeats."""
+    amplitudes = torch.tensor(
+        guide_noise_taper(pixels.shape[0], strength), dtype=torch.float32
+    ).view(-1, 1, 1, 1)
+    generator = torch.Generator().manual_seed(seed)
+    noise = torch.randn(pixels.shape, generator=generator, dtype=torch.float32)
+    return (pixels.to(torch.float32) + noise * amplitudes * 255.0).clamp(0.0, 255.0)
+
+
 class DwH3GuideLayoutStep(ModularPipelineBlocks):
     model_name = "minimax-h3"
 
@@ -292,6 +308,17 @@ def inputs(self):
                     "carries 'audio': (channels, samples) and 'sample_rate' holds that soundtrack over its span."
                 ),
             ),
+            InputParam(
+                name=GUIDE_NOISE_INPUT,
+                type_hint=float,
+                default=None,
+                description=(
+                    "Fixed guide noise in [0, 1): pixel noise on every video guide, tapered from this std at "
+                    "the guide's first frame to none at its last, put into the guide's latent by a paired "
+                    "encode (the same encode seed for the noisy and clean frames). The rows stay held. "
+                    "0 or unset is the guide as it is; audio is never noised."
+                ),
+            ),
             InputParam(name="condition_latents", type_hint=list, default=None),
         ]
 
@@ -319,7 +346,12 @@ def __call__(self, components, state):
             laid["latent_height"], laid["latent_width"], patch_h, patch_w
         )
         latents, positions = self._encode_guides(
-            components, guides, laid, frame_grid, num_text
+            components,
+            guides,
+            laid,
+            frame_grid,
+            num_text,
+            state.get(GUIDE_NOISE_INPUT) or 0.0,
         )
         layout = splice_guide_rows(
             tuple(laid[name].cpu() for name in _PACKED)
@@ -340,9 +372,16 @@ def __call__(self, components, state):
         state.set("condition_latents", list(laid["condition_latents"] or []) + latents)
         return components, state
 
-    def _encode_guides(self, components, guides, laid, frame_grid, num_text):
+    def _encode_guides(
+        self, components, guides, laid, frame_grid, num_text, guide_noise=0.0
+    ):
         """([latents], [positions]): each guide VAE-encoded on the canvas, and
-        its rows' rotary positions, timed from the target frame it lands on."""
+        its rows' rotary positions, timed from the target frame it lands on.
+
+        With `guide_noise`, each guide is also encoded with tapered pixel noise
+        (`noised_guide_pixels`) under the same encode seed, and the difference
+        E(noisy) - E(clean) is added to the clean latent - PrefixStream's paired
+        encode, so 0 leaves today's latent exactly."""
         device = components._execution_device
         target_time = before_denoise._temporal_position_grid(
             laid["num_latent_frames"], float(num_text)
@@ -357,14 +396,15 @@ def _encode_guides(self, components, guides, laid, frame_grid, num_text):
                 raise ValueError(f"guides[{index}]: {problem}")
             pixels = fit_guide_frames(frames, laid["height"], laid["width"])
             pixels = torch.from_numpy(np.ascontiguousarray(pixels))
-            # Through the module, so the encode is the one installed when it runs
-            encoded = encoders.encode_vae_condition(
-                components.vae,
-                pixels.to(device).permute(3, 0, 1, 2)[None],
-                components.pixel_mean,
-                components.pixel_std,
-                components.keyframe_encode_seed,
-            )
+            encoded = self._encode_pixels(components, pixels, device)
+            if guide_noise:
+                noisy = noised_guide_pixels(
+                    pixels, guide_noise, components.keyframe_encode_seed + index
+                )
+                encoded = encoded + (
+                    self._encode_pixels(components, noisy, device) - encoded
+                )
+                logger.info(f"Guide {index}: guide_noise {guide_noise} on its frames")
             latents.append(encoded)
             positions.append(
                 guide_position_ids(
@@ -380,6 +420,19 @@ def _encode_guides(self, components, guides, laid, frame_grid, num_text):
             )
         return latents, positions
 
+    @staticmethod
+    def _encode_pixels(components, pixels, device):
+        """One guide's `(n, h, w, 3)` pixels VAE-encoded the way a keyframe
+        is, under the keyframe encode seed."""
+        # Through the module, so the encode is the one installed when it runs
+        return encoders.encode_vae_condition(
+            components.vae,
+            pixels.to(device).permute(3, 0, 1, 2)[None],
+            components.pixel_mean,
+            components.pixel_std,
+            components.keyframe_encode_seed,
+        )
+
     def _hold_guide_audio(
         self, components, state, guides, layout, laid, width_grid, num_text
     ):
diff --git a/dw/pipeline_processors/h3_rules.py b/dw/pipeline_processors/h3_rules.py
index 92ead7af..fe0fd456 100644
--- a/dw/pipeline_processors/h3_rules.py
+++ b/dw/pipeline_processors/h3_rules.py
@@ -457,6 +457,43 @@ def not_h3(pipeline, argument=HOLD_AUDIO_INPUT, taken_by=H3_WORKFLOWS):
 GUIDE_AUDIO_BLOCK = "dw_guide_audio"
 GUIDE_AUDIO_ROWS = "dw_guide_audio_latents"
 
+# Fixed guide noise (#1166, after PrefixStream): pixel noise on every video
+# guide, tapered by `guide_noise_taper` to clean at the guide's last frame, put
+# into its latent by a paired encode (h3_guide_steps.DwH3GuideLayoutStep). The
+# rows stay held condition rows; audio is never noised.
+GUIDE_NOISE_INPUT = "guide_noise"
+
+
+def guide_noise_problem(value):
+    """Why `value` cannot be a `guide_noise`, naming the range, or None. An
+    unresolved reference is left to the run."""
+    if isinstance(value, str) and references.is_ref(references.UNRESOLVED, value):
+        return None
+    if isinstance(value, bool) or not isinstance(value, (int, float)):
+        return (
+            f"guide_noise is a number in [0, 1) - about 0.4 - and {value!r} is "
+            f"not a number"
+        )
+    if not 0 <= value < 1:  # NaN fails it too
+        return (
+            f"guide_noise is a number in [0, 1) - about 0.4; 0 adds none - and "
+            f"{value} is outside it"
+        )
+    return None
+
+
+def guide_noise_taper(num_frames, strength):
+    """The noise amplitude on each of a guide's `num_frames` frames: a cosine
+    taper from `strength` at the first frame to 0 at the last, the frame next
+    to the join. A one-frame guide is all join, so it is clean."""
+    if num_frames <= 1:
+        return [0.0] * num_frames
+    return [
+        strength * 0.5 * (1.0 + math.cos(math.pi * index / (num_frames - 1)))
+        for index in range(num_frames)
+    ]
+
+
 # Pixel frames per VAE chunk and latent frames per chunk - a clip encodes to whole
 # latents at 1, 5 or 17m + 5 frames, and lines up with the target's latent grid
 # only at a chunk boundary, frame 17j (latent 5j). Copies of the H3 video VAE's
diff --git a/dw/pipeline_processors/pipeline.py b/dw/pipeline_processors/pipeline.py
index e06467c1..30324447 100644
--- a/dw/pipeline_processors/pipeline.py
+++ b/dw/pipeline_processors/pipeline.py
@@ -39,6 +39,7 @@
 from .h3_rules import (
     GUIDES_INPUT,
     GUIDE_LIMIT,
+    GUIDE_NOISE_INPUT,
     LATENT_ANCHOR_INPUT,
     HELD_AUDIO_OUTPUT,
     HELD_AUDIO_RATE_OUTPUT,
@@ -51,6 +52,7 @@
     REGENERATE_MASK,
     SOURCE_VIDEO_INPUT,
     edits_region,
+    guide_noise_problem,
     mask_problems,
     media_problems,
     refine_problems,
@@ -664,6 +666,7 @@ def _call_pipeline(self, arguments, attn_backend):
         arguments = self._with_mask(arguments)
         arguments = self._with_held_audio(arguments)
         arguments = self._with_guides(arguments)
+        arguments = self._with_guide_noise(arguments)
         arguments = self._with_latent_anchor(arguments)
         # The load is over and the denoise loop is starting. Pipelines whose
         # signature has no step callback report nothing else at all, so this
@@ -804,6 +807,38 @@ def _with_guides(self, arguments):
         arguments[GUIDES_INPUT] = prepared
         return arguments
 
+    def _with_guide_noise(self, arguments):
+        """`guide_noise` checked before the call: a number in [0, 1) on a
+        pipeline with the guide layout (dw/pipeline_processors/h3_guide_steps.py).
+        None or 0 is dropped, so the call is today's.
+
+        Raises:
+            ValueError: If the value is not a number in [0, 1), or this
+                pipeline cannot take guides
+        """
+        if GUIDE_NOISE_INPUT not in arguments:
+            return arguments
+        value = arguments[GUIDE_NOISE_INPUT]
+        problem = None if value is None else guide_noise_problem(value)
+        if value is None or (value == 0 and problem is None):
+            arguments = dict(arguments)
+            del arguments[GUIDE_NOISE_INPUT]
+            return arguments
+        where = f"Step '{self.name}'"
+        if problem:
+            raise ValueError(f"{where}: {problem}")
+        refusal = guides_refusal(self.pipeline)
+        if refusal:
+            raise ValueError(f"{where}: {GUIDE_NOISE_INPUT} noises guides - {refusal}")
+        if arguments.get("references") is not None:
+            raise ValueError(
+                f"{where}: {GUIDE_NOISE_INPUT} cannot be combined with references - "
+                f"ref2va lays out its own conditioning; use it on t2va or fl2va"
+            )
+        arguments = dict(arguments)
+        arguments[GUIDE_NOISE_INPUT] = float(value)
+        return arguments
+
     def _with_latent_anchor(self, arguments):
         """`latent_anchor` as the H3 anchor step takes it - uint8 frames cut to
         a whole-latent length (dw/pipeline_processors/h3_anchor.py). None is no
diff --git a/dw/validation.py b/dw/validation.py
index 2dd51130..ebeaa8f2 100644
--- a/dw/validation.py
+++ b/dw/validation.py
@@ -40,7 +40,13 @@
 from .content_types import content_type_errors
 from .dissolve_frame_errors import dissolve_frame_errors
 from .for_each import ForEachError, entry_field_warnings
-from .guides import anchor_errors, guide_chain_errors, guides_errors
+from .guides import (
+    anchor_errors,
+    guide_chain_errors,
+    guide_noise_errors,
+    guide_noise_warnings,
+    guides_errors,
+)
 from .hold_audio import hold_audio_errors, mask_errors, refine_strength_errors
 from .introspection import task_signature_errors
 from .kernel_availability import kernel_availability_errors
@@ -496,6 +502,11 @@ def _task_errors(context):
         "latent_anchor",
         lambda c: anchor_errors(c.expanded, c.source_indices),
     ),
+    # A guide_noise off H3 t2va/fl2va, or not a number in [0, 1) (#1166)
+    Check(
+        "guide_noise",
+        lambda c: guide_noise_errors(c.expanded, c.source_indices),
+    ),
     Check("task_signatures", _task_errors),
     # A task argument that runs Hub code, anywhere in the step - the run
     # refuses it too (Task.run), but before it queues costs nothing
@@ -726,6 +737,12 @@ def _null_variable_argument_warnings(context):
     # cache, a CUDA-only attention backend off CUDA - each of which cost a
     # render to find out (#868)
     Check("acceleration_warnings", acceleration_warnings),
+    # A guide_noise on a step with no guides and no guide chain - it would
+    # noise nothing (#1166)
+    Check(
+        "guide_noise_warnings",
+        lambda c: guide_noise_warnings(c.expanded, c.source_indices),
+    ),
 ]
 
 
diff --git a/tests/test_validation.py b/tests/test_validation.py
index 2ea5c943..a223c15a 100644
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -202,6 +202,7 @@ def test_validation_does_not_import_the_workflow_module():
     "chain_prompts",
     "guide_chain",
     "latent_anchor",
+    "guide_noise",
     "task_signatures",
     "task_remote_code",
     "component_types",
@@ -315,6 +316,7 @@ def test_a_childs_pathless_finding_is_placed_at_the_sub_workflow_step(
     "shot_span_warnings",
     "inherited_vram_warnings",
     "acceleration_warnings",
+    "guide_noise_warnings",
 ]
 
 

From 9363e97c2679775afc4a7adfee58aedff68e94e5 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 10:21:21 -0600
Subject: [PATCH 48/74] feat(h3): #1174 - guide_noise tests and docs
 (WORKFLOW_GUIDE, ARCHITECTURE, ATTRIBUTIONS)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md         |   2 +-
 docs/ATTRIBUTIONS.md         |   5 +
 docs/WORKFLOW_GUIDE.md       |  25 ++
 tests/test_h3_guide_noise.py | 456 +++++++++++++++++++++++++++++++++++
 4 files changed, 487 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_h3_guide_noise.py

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 72c505c9..ca836fb3 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values` |
+| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` adds E(noisy) - E(clean) to each video guide's clean encode when `guide_noise` is set, on `h3_rules.guide_noise_taper`, and encodes once without it; `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
diff --git a/docs/ATTRIBUTIONS.md b/docs/ATTRIBUTIONS.md
index d9b31825..d00ef812 100644
--- a/docs/ATTRIBUTIONS.md
+++ b/docs/ATTRIBUTIONS.md
@@ -116,6 +116,11 @@ A ComfyUI node pack for MiniMax H3 prefix streaming, under the MIT licence. It
 inspired the texture-band measurement on `compare_outputs`
 (`texture_bands`, [dw/texture_bands.py](../dw/texture_bands.py)): RMS energy by
 wavelength band, to see whether a change in a chain adds or removes texture.
+It also inspired `guide_noise`
+([dw/pipeline_processors/h3_guide_steps.py](../dw/pipeline_processors/h3_guide_steps.py);
+[#1166](https://github.com/dkackman/diffusers-workflow/issues/1166)), from its prefix
+noise at `775be90`: a cosine-tapered noise on each guide, folded in as the difference of
+a paired VAE encode, so the guide's hold loosens toward its start.
 
 ### [1113800942/comfy-h3md-director](https://github.com/1113800942/comfy-h3md-director)
 
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index e367086f..177e1e0f 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1818,6 +1818,30 @@ server with `cudnn_deterministic: true` (`ACCELERATION.md`, *TF32 and cuDNN*); w
 default `false`, two runs match frame for frame by eye but their audio levels differ by a
 few dB.
 
+#### Loosening a guide's hold: `guide_noise`
+
+A guide is held almost exactly, so the take freezes under it and leaves the guide's end
+with a visible change of pace. `guide_noise` (an H3 `t2va`/`fl2va` argument, a number in
+[0, 1), about 0.4 to start) loosens every video guide on the step: each guide frame gets
+Gaussian noise of standard deviation `amplitude x 255` on its 0-255 pixels, where the
+amplitude falls on a cosine taper from `guide_noise` at the guide's first frame to 0 at
+its last, so the frames nearest the new material are held clean. The guide is encoded
+twice with the same encode seed, clean and noisy, and E(noisy) - E(clean) is added to the
+clean latent; the noise draw is seeded per guide (`keyframe_encode_seed` + the guide's
+index), so a seeded run repeats. The rows are still held, never denoised, and the row
+plan, the refine schedule and guide audio (`"audio": true`) are unchanged.
+`guide_noise: 0` (or absent) skips the second encode, and the latent is bit-identical to
+a step without it.
+
+It is a step argument, so in a `continuity: "guide"` chain it rides to every segment and
+loosens the carried guide too.
+
+| Rule | Value |
+|---|---|
+| value | a number in [0, 1); anything else, or a value outside the range, is refused naming the range. Accepts a `variable:` reference |
+| where | MiniMax-H3 `t2va` or `fl2va`, where `guides` run; refused on `ref2va`, on a step that passes `references`, and on a non-H3 step |
+| no guides | a warning, not a refusal: a step with no `guides` and no `continuity: "guide"` chain has nothing to noise |
+
 #### Chaining with a guide: `continuity: "guide"`
 
 A chain on an H3 `t2va` or `fl2va` step can carry its seam with a guide instead of a
@@ -1838,6 +1862,7 @@ head: N segments of F frames give F + (N - 1)(F - P) frames - 3 x 124 at P = 22
 | `trim_frames` | ignored (the trim is P), with a note in the job log |
 | `crossfade_ms` | ignored when `carry_audio` is true: the guide held the audio, so the seam is joined with only a declick |
 | `num_frames` | must exceed P, or the chain cannot progress |
+| `guide_noise` | rides on the step's arguments to every segment, so segments 2+ noise the carried guide (above) |
 
 #### Anchoring a chain's colour: `latent_anchor` and `anchor: "segment_1"`
 
diff --git a/tests/test_h3_guide_noise.py b/tests/test_h3_guide_noise.py
new file mode 100644
index 00000000..28270ba9
--- /dev/null
+++ b/tests/test_h3_guide_noise.py
@@ -0,0 +1,456 @@
+"""MiniMax-H3 fixed guide noise (#1166, #1174): the cosine taper, the noised
+pixels, the paired encode in the guide layout step, the pre-run checks and the
+call-time refusal of `guide_noise`."""
+
+import math
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import torch
+
+from dw import validation
+from dw.guides import guide_noise_errors, guide_noise_warnings
+from dw.pipeline_processors.h3_guides import insert_guides
+from dw.pipeline_processors.h3_hold import insert_audio_hold
+from dw.pipeline_processors.h3_rules import (
+    GUIDE_AUDIO_ROWS,
+    GUIDE_NOISE_INPUT,
+    guide_noise_problem,
+    guide_noise_taper,
+)
+from dw.pipeline_processors.pipeline import Pipeline
+
+minimax = pytest.importorskip("diffusers.modular_pipelines.minimax_h3")
+from diffusers.modular_pipelines.modular_pipeline import PipelineState  # noqa: E402
+from dw.pipeline_processors import h3_hold_steps  # noqa: E402
+from dw.pipeline_processors.h3_guide_steps import (  # noqa: E402
+    DwH3GuideLayoutStep,
+    noised_guide_pixels,
+)
+
+ENCODER = "diffusers.modular_pipelines.minimax_h3.encoders.encode_vae_condition"
+TEXT = 10
+SIDE = 64
+FRAMES = 124
+AUDIO_C = 4
+
+
+class TestTaper:
+    def test_cosine_values(self):
+        values = guide_noise_taper(5, 0.4)
+        expected = [0.4 * 0.5 * (1 + math.cos(math.pi * i / 4)) for i in range(5)]
+        assert values == pytest.approx(expected)
+
+    def test_endpoints_are_exact(self):
+        values = guide_noise_taper(9, 0.4)
+        assert values[0] == 0.4
+        assert values[-1] == 0.0
+
+    def test_monotonically_falling(self):
+        values = guide_noise_taper(12, 0.7)
+        assert all(a > b for a, b in zip(values, values[1:]))
+
+    def test_one_frame_is_clean(self):
+        assert guide_noise_taper(1, 0.4) == [0.0]
+
+    def test_no_frames(self):
+        assert guide_noise_taper(0, 0.4) == []
+
+
+class TestNoisedPixels:
+    def pixels(self, n=5):
+        return torch.full((n, 8, 8, 3), 128, dtype=torch.uint8)
+
+    def test_last_frame_is_unchanged_exactly(self):
+        pixels = self.pixels()
+        out = noised_guide_pixels(pixels, 0.4, 3)
+        assert torch.equal(out[-1], pixels[-1].to(torch.float32))
+
+    def test_first_frame_changes(self):
+        pixels = self.pixels()
+        out = noised_guide_pixels(pixels, 0.4, 3)
+        assert not torch.equal(out[0], pixels[0].to(torch.float32))
+
+    def test_noise_falls_along_the_guide(self):
+        pixels = self.pixels(9)
+        out = noised_guide_pixels(pixels, 0.2, 1)
+        spread = (out - 128.0).abs().mean(dim=(1, 2, 3))
+        assert spread[0] > spread[4] > spread[-1] == 0
+
+    def test_clamped_to_pixel_range(self):
+        for fill in (0, 255):
+            pixels = torch.full((5, 8, 8, 3), fill, dtype=torch.uint8)
+            out = noised_guide_pixels(pixels, 0.9, 0)
+            assert out.min() >= 0.0 and out.max() <= 255.0
+            assert out.dtype == torch.float32
+
+    def test_same_seed_repeats(self):
+        pixels = self.pixels()
+        assert torch.equal(
+            noised_guide_pixels(pixels, 0.4, 7), noised_guide_pixels(pixels, 0.4, 7)
+        )
+
+    def test_another_seed_differs(self):
+        pixels = self.pixels()
+        assert not torch.equal(
+            noised_guide_pixels(pixels, 0.4, 7), noised_guide_pixels(pixels, 0.4, 8)
+        )
+
+
+# The layout step, with the VAE stubbed
+
+
+def layout_components():
+    return SimpleNamespace(
+        _execution_device=torch.device("cpu"),
+        canvas_multiple=32,
+        config=SimpleNamespace(canvas_short_edge=768, canvas_max_pixels=768 * 1344),
+        vae_frames_per_chunk=17,
+        vae_latents_per_chunk=5,
+        fps=24,
+        min_duration=5,
+        max_duration=15,
+        vae_spatial_compression_ratio=16,
+        patch_size=(1, 2, 2),
+        audio_channels=2,
+        audio_tag=7,
+        video_tag=9,
+        pixel_mean=0,
+        pixel_std=1,
+        keyframe_encode_seed=100,
+        vae=None,
+        audio_sampling_rate=16000,
+        audio_latent_channels=AUDIO_C,
+    )
+
+
+def layout_state(guides, guide_noise=None):
+    state = PipelineState()
+    state.set("text_token_tags", torch.ones(TEXT, dtype=torch.long))
+    state.set("height", SIDE)
+    state.set("width", SIDE)
+    state.set("num_frames", FRAMES)
+    state.set("keyframe_anchors", ())
+    state.set("guides", guides)
+    if guide_noise is not None:
+        state.set(GUIDE_NOISE_INPUT, guide_noise)
+    return state
+
+
+def run_step(guides, guide_noise=None):
+    return DwH3GuideLayoutStep()(
+        layout_components(), layout_state(guides, guide_noise)
+    )[1]
+
+
+@pytest.fixture
+def encodes(monkeypatch):
+    """Records every (pixels, seed) the stubbed VAE encode is given. Its latent
+    depends on the pixels, so a noisy encode differs from the clean one."""
+    calls = []
+
+    def encode(vae, pixels, mean, std, seed):
+        calls.append((pixels.clone(), seed))
+        frames = pixels.shape[2]
+        latents = 1 if frames == 1 else (frames - 5) // 17 * 5 + 2
+        shape = (1, 4, latents, SIDE // 16, SIDE // 16)
+        value = pixels.to(torch.float32).mean(dim=(1, 3, 4)).mean() / 255.0
+        ramp = torch.linspace(0, 1, latents).view(1, 1, -1, 1, 1)
+        return torch.full(shape, 1.0) * value + ramp
+
+    monkeypatch.setattr(ENCODER, encode)
+    return calls
+
+
+def video_guide(frame=17, n=22, fill=100):
+    return {"video": np.full((n, 32, 32, 3), fill, np.uint8), "frame": frame}
+
+
+class TestPairedEncode:
+    @pytest.mark.parametrize("noise", [None, 0, 0.0])
+    def test_zero_is_todays_latent_with_one_encode_per_guide(self, encodes, noise):
+        guides = [video_guide(0), video_guide(17)]
+        state = run_step(guides, noise)
+        assert len(encodes) == 2
+        baseline = run_step(guides)
+        for ours, theirs in zip(
+            state.get("condition_latents"), baseline.get("condition_latents")
+        ):
+            assert torch.equal(ours, theirs)
+
+    def test_zero_is_the_plain_encode(self, encodes):
+        state = run_step([video_guide()], 0)
+        ((pixels, seed),) = encodes
+        assert seed == 100
+        assert pixels.dtype == torch.uint8
+        expected = encodes_value(pixels)
+        assert torch.equal(state.get("condition_latents")[0], expected)
+
+    def test_noise_encodes_twice_per_guide_with_one_seed(self, encodes):
+        run_step([video_guide(0), video_guide(17)], 0.4)
+        assert len(encodes) == 4
+        # Clean then noisy, per guide, under the same encode seed
+        for clean, noisy in (encodes[0:2], encodes[2:4]):
+            assert clean[1] == noisy[1] == 100
+            assert clean[0].dtype == torch.uint8
+            assert noisy[0].dtype == torch.float32
+            assert clean[0].shape == noisy[0].shape
+
+    def test_result_is_clean_plus_the_encode_difference(self, encodes):
+        state = run_step([video_guide()], 0.4)
+        (clean_pixels, _), (noisy_pixels, _) = encodes
+        clean = encodes_value(clean_pixels)
+        noisy = encodes_value(noisy_pixels)
+        assert not torch.equal(clean, noisy)
+        result = state.get("condition_latents")[0]
+        assert torch.equal(result, clean + (noisy - clean))
+
+    def test_the_noisy_frames_are_the_tapered_noise(self, encodes):
+        run_step([video_guide()], 0.4)
+        (clean_pixels, _), (noisy_pixels, _) = encodes
+        # (1, c, n, h, w): the last frame is the join, left clean
+        assert torch.equal(noisy_pixels[:, :, -1], clean_pixels[:, :, -1].float())
+        assert not torch.equal(noisy_pixels[:, :, 0], clean_pixels[:, :, 0].float())
+
+    def test_another_guide_gets_another_noise_seed(self, encodes):
+        run_step([video_guide(0, fill=100), video_guide(17, fill=100)], 0.4)
+        first = encodes[1][0]
+        second = encodes[3][0]
+        assert not torch.equal(first, second)
+
+    def test_a_run_repeats(self, encodes):
+        run_step([video_guide()], 0.4)
+        run_step([video_guide()], 0.4)
+        assert torch.equal(encodes[1][0], encodes[3][0])
+
+
+def encodes_value(pixels):
+    frames = pixels.shape[2]
+    latents = 1 if frames == 1 else (frames - 5) // 17 * 5 + 2
+    shape = (1, 4, latents, SIDE // 16, SIDE // 16)
+    value = pixels.to(torch.float32).mean(dim=(1, 3, 4)).mean() / 255.0
+    ramp = torch.linspace(0, 1, latents).view(1, 1, -1, 1, 1)
+    return torch.full(shape, 1.0) * value + ramp
+
+
+LAYOUT_KEYS = [
+    "num_frames",
+    "num_latent_frames",
+    "latent_height",
+    "latent_width",
+    "num_audio_latents",
+    "position_ids",
+    "token_tags",
+    "video_indices",
+    "audio_indices",
+    "text_indices",
+    "num_condition_video_rows",
+    "num_condition_audio_rows",
+]
+
+
+def same(a, b):
+    if isinstance(a, torch.Tensor):
+        return torch.equal(a, b)
+    return a == b
+
+
+class TestRowsUnchanged:
+    def test_layout_is_the_same_with_noise(self, encodes):
+        guides = [video_guide(0), video_guide(17, n=39)]
+        plain = run_step(guides)
+        noised = run_step(guides, 0.5)
+        for key in LAYOUT_KEYS:
+            assert same(plain.get(key), noised.get(key)), key
+        assert len(noised.get("condition_latents")) == 2
+        for a, b in zip(
+            plain.get("condition_latents"), noised.get("condition_latents")
+        ):
+            assert a.shape == b.shape
+
+    def test_guide_audio_rows_are_the_same_with_noise(self, encodes, monkeypatch):
+        spans = []
+
+        def encode_span(encoder, components, waveform, num_latents):
+            spans.append((tuple(waveform.shape), num_latents))
+            return torch.full((2, num_latents, AUDIO_C), 5.0)
+
+        monkeypatch.setattr(h3_hold_steps, "encode_audio_span", encode_span)
+        guide = {
+            "video": np.zeros((39, 32, 32, 3), np.uint8),
+            "frame": 0,
+            "audio": torch.ones(2, 16000 * 39 // 24),
+            "sample_rate": 16000,
+        }
+        plain = run_step([guide])
+        noised = run_step([dict(guide)], 0.6)
+        assert spans[0] == spans[1]
+        assert torch.equal(plain.get(GUIDE_AUDIO_ROWS), noised.get(GUIDE_AUDIO_ROWS))
+        assert plain.get("num_condition_audio_rows") == noised.get(
+            "num_condition_audio_rows"
+        )
+        assert torch.equal(plain.get("position_ids"), noised.get("position_ids"))
+
+
+# Validation
+
+
+def definition(workflow="t2va", chain=None, pipeline_class=None, **arguments):
+    pipeline = {
+        "configuration": {"component_type": "ModularPipeline"},
+        "from_pretrained_arguments": {
+            "model_name": "MiniMaxAI/MiniMax-H3",
+            "workflow": workflow,
+        },
+        "arguments": arguments,
+    }
+    if pipeline_class:
+        pipeline["configuration"] = {"component_type": pipeline_class}
+        pipeline["from_pretrained_arguments"] = {"model_name": "some/model"}
+    if chain is not None:
+        pipeline["chain"] = chain
+    return {"steps": [{"name": "video", "pipeline": pipeline}]}
+
+
+GUIDE = [{"video": "clip.mp4", "frame": 0}]
+
+
+class TestGuideNoiseErrors:
+    @pytest.mark.parametrize("value", [1, 1.5, -0.1, "x", True, False, float("nan")])
+    def test_refused_naming_the_range(self, value):
+        (error,) = guide_noise_errors(definition(guide_noise=value, guides=GUIDE))
+        assert error["path"] == "steps[0].pipeline.arguments.guide_noise"
+        assert "[0, 1)" in error["message"]
+
+    @pytest.mark.parametrize("value", [0, 0.0, 0.4, 0.999])
+    @pytest.mark.parametrize("workflow", ["t2va", "fl2va"])
+    def test_in_range_is_clean(self, value, workflow):
+        step = definition(workflow, guide_noise=value, guides=GUIDE)
+        assert guide_noise_errors(step) == []
+
+    def test_unset_and_null_are_clean(self):
+        assert guide_noise_errors(definition()) == []
+        assert guide_noise_errors(definition(guide_noise=None)) == []
+
+    def test_unresolved_variable_is_not_refused(self):
+        step = definition(guide_noise="variable:noise", guides=GUIDE)
+        assert guide_noise_errors(step) == []
+        assert guide_noise_problem("variable:noise") is None
+
+    def test_off_h3_is_refused(self):
+        step = definition(pipeline_class="WanPipeline", guide_noise=0.4)
+        (error,) = guide_noise_errors(step)
+        assert error["path"] == "steps[0].pipeline.arguments.guide_noise"
+
+    def test_ref2va_is_refused(self):
+        (error,) = guide_noise_errors(definition("ref2va", guide_noise=0.4))
+        assert "ref2va" in error["message"]
+
+    def test_references_are_refused(self):
+        step = definition("fl2va", guide_noise=0.4, references=["a.png"])
+        (error,) = guide_noise_errors(step)
+        assert "references" in error["message"]
+
+    def test_registered_as_a_check(self):
+        assert "guide_noise" in [check.name for check in validation.ERROR_CHECKS]
+        assert "guide_noise_warnings" in [
+            check.name for check in validation.WARNING_CHECKS
+        ]
+
+
+class TestGuideNoiseWarnings:
+    @pytest.mark.parametrize("workflow", ["t2va", "fl2va"])
+    def test_no_guides_and_no_chain_warns(self, workflow):
+        (warning,) = guide_noise_warnings(definition(workflow, guide_noise=0.4))
+        assert warning.startswith("steps[0].pipeline.arguments.guide_noise:")
+        assert "does nothing" in warning
+
+    def test_empty_guides_warn(self):
+        step = definition(guide_noise=0.4, guides=[])
+        assert len(guide_noise_warnings(step)) == 1
+
+    def test_a_chain_without_guide_continuity_warns(self):
+        step = definition(guide_noise=0.4, chain={"segments": 2})
+        assert len(guide_noise_warnings(step)) == 1
+
+    def test_guides_do_not_warn(self):
+        assert guide_noise_warnings(definition(guide_noise=0.4, guides=GUIDE)) == []
+
+    def test_guide_continuity_does_not_warn(self):
+        chain = {"segments": 3, "continuity": "guide"}
+        assert guide_noise_warnings(definition(guide_noise=0.4, chain=chain)) == []
+
+    def test_unresolved_continuity_does_not_warn(self):
+        chain = {"segments": 3, "continuity": "variable:continuity"}
+        assert guide_noise_warnings(definition(guide_noise=0.4, chain=chain)) == []
+
+    def test_zero_does_not_warn(self):
+        assert guide_noise_warnings(definition(guide_noise=0)) == []
+
+    def test_unset_does_not_warn(self):
+        assert guide_noise_warnings(definition()) == []
+
+    def test_unresolved_variable_value_is_not_an_error_and_may_warn(self):
+        step = definition(guide_noise="variable:noise")
+        assert guide_noise_errors(step) == []
+
+
+# Pipeline._with_guide_noise
+
+
+def fresh(workflow):
+    pipeline = minimax.MiniMaxH3Blocks().get_workflow(workflow).init_pipeline()
+    insert_audio_hold(pipeline)
+    return pipeline
+
+
+@pytest.fixture(scope="module")
+def guided():
+    pipeline = fresh("t2va")
+    assert insert_guides(pipeline)
+    return pipeline
+
+
+def step(pipeline):
+    return SimpleNamespace(name="video", pipeline=pipeline, base_dir=None)
+
+
+class TestWithGuideNoise:
+    def test_absent_passes_through(self, guided):
+        arguments = {"prompt": "x"}
+        assert Pipeline._with_guide_noise(step(guided), arguments) is arguments
+
+    @pytest.mark.parametrize("value", [None, 0, 0.0])
+    def test_zero_and_none_drop_the_key(self, guided, value):
+        arguments = {"prompt": "x", GUIDE_NOISE_INPUT: value}
+        result = Pipeline._with_guide_noise(step(guided), arguments)
+        assert GUIDE_NOISE_INPUT not in result
+        assert result["prompt"] == "x"
+        assert GUIDE_NOISE_INPUT in arguments  # the caller's dict is left be
+
+    def test_a_value_is_kept_as_a_float(self, guided):
+        result = Pipeline._with_guide_noise(step(guided), {GUIDE_NOISE_INPUT: 0.3})
+        assert result[GUIDE_NOISE_INPUT] == 0.3
+        assert isinstance(result[GUIDE_NOISE_INPUT], float)
+
+    def test_an_int_is_made_a_float(self, guided):
+        # 0 drops; there is no other int in [0, 1)
+        result = Pipeline._with_guide_noise(step(guided), {GUIDE_NOISE_INPUT: 0})
+        assert GUIDE_NOISE_INPUT not in result
+
+    @pytest.mark.parametrize("value", [1, 1.5, -0.1, "x", True, float("nan")])
+    def test_out_of_range_raises(self, guided, value):
+        with pytest.raises(ValueError, match=r"\[0, 1\)"):
+            Pipeline._with_guide_noise(step(guided), {GUIDE_NOISE_INPUT: value})
+
+    def test_references_raise(self, guided):
+        with pytest.raises(ValueError, match="references"):
+            Pipeline._with_guide_noise(
+                step(guided), {GUIDE_NOISE_INPUT: 0.3, "references": [object()]}
+            )
+
+    def test_a_pipeline_that_cannot_take_guides_raises(self):
+        other = SimpleNamespace(name="other")
+        with pytest.raises(ValueError, match="guide_noise"):
+            Pipeline._with_guide_noise(step(other), {GUIDE_NOISE_INPUT: 0.3})

From f63fe50b2f6a410a262269112eb55852c22db05f Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 13:38:17 -0600
Subject: [PATCH 49/74] feat(workspace): enhance workspace handling with
 dynamic refetching and creation support

---
 ui/src/lib/workspace.svelte.test.ts | 30 +++++++++++++++++++++++++++++
 ui/src/lib/workspace.svelte.ts      | 22 ++++++++++++++++++++-
 2 files changed, 51 insertions(+), 1 deletion(-)

diff --git a/ui/src/lib/workspace.svelte.test.ts b/ui/src/lib/workspace.svelte.test.ts
index 527a1f31..ad3759cb 100644
--- a/ui/src/lib/workspace.svelte.test.ts
+++ b/ui/src/lib/workspace.svelte.test.ts
@@ -1,5 +1,7 @@
 import { afterEach, beforeEach, expect, it, vi } from 'vitest'
 
+// Workspaces the server gained after the page loaded - a test pushes to it
+const created = vi.hoisted((): string[] => [])
 const listWorkspaces = vi.hoisted(() =>
   vi.fn(() =>
     Promise.resolve({
@@ -22,6 +24,14 @@ const listWorkspaces = vi.hoisted(() =>
           outputs: '/ws/studio/outputs',
           prompts: null,
         },
+        ...created.map((name) => ({
+          name,
+          default: false,
+          workflows: `/ws/${name}/workflows`,
+          assets: null,
+          outputs: `/ws/${name}/outputs`,
+          prompts: null,
+        })),
       ],
     }),
   ),
@@ -33,6 +43,7 @@ vi.mock('./toast', () => ({ notify: { error: errorToast, success: vi.fn() } }))
 beforeEach(() => {
   listWorkspaces.mockClear()
   errorToast.mockClear()
+  created.length = 0
   localStorage.clear()
   location.hash = ''
 })
@@ -135,10 +146,29 @@ it('a missing workspace entered after the listing landed still redirects', async
   expect(errorToast).not.toHaveBeenCalled()
   location.hash = '#/ws/typo/gallery'
   window.dispatchEvent(new HashChangeEvent('hashchange'))
+  // Refetched once before it is called missing - see the next test
+  await loadWorkspaces()
   expect(location.hash).toBe('#/ws/default/overview')
   expect(errorToast).toHaveBeenCalledWith(expect.stringContaining('typo'))
 })
 
+it('a workspace created after the listing landed is refetched, not called missing', async () => {
+  location.hash = '#/ws/studio/gallery'
+  await import('./router.svelte')
+  const { loadWorkspaces, workspace } = await import('./workspace.svelte')
+  await loadWorkspaces()
+  // Created elsewhere (an agent's create_workspace) after this page loaded -
+  // the running chip's job link then corrects the URL into it
+  created.push('fresh')
+  location.hash = '#/ws/fresh/jobs/abc'
+  window.dispatchEvent(new HashChangeEvent('hashchange'))
+  await loadWorkspaces()
+  expect(workspace.names).toContain('fresh')
+  expect(location.hash).toBe('#/ws/fresh/jobs/abc')
+  expect(workspace.current).toBe('fresh')
+  expect(errorToast).not.toHaveBeenCalled()
+})
+
 it('a failed listing does not read as every other workspace being gone', async () => {
   listWorkspaces.mockRejectedValueOnce(new Error('down'))
   location.hash = '#/ws/studio/gallery'
diff --git a/ui/src/lib/workspace.svelte.ts b/ui/src/lib/workspace.svelte.ts
index f21aa1a2..817477ed 100644
--- a/ui/src/lib/workspace.svelte.ts
+++ b/ui/src/lib/workspace.svelte.ts
@@ -36,6 +36,13 @@ let lastView: RouteView | null = null
 // the default alone so the sidebar has something to show, and that fallback
 // must not be read as proof that every other workspace is gone.
 let listed = false
+// Whether `names` was fetched for the check now reading it. The listing is
+// cached for the page's life, and a workspace can be created elsewhere after
+// it landed (an agent's create_workspace, another tab) - the running chip's
+// job link then corrects the URL into one the cache has never heard of. So
+// a name the cache lacks is refetched once before it is called missing.
+let fresh = false
+let refetching = false
 
 export function applyRouteWorkspace(view: RouteView | null): void {
   lastView = view
@@ -59,6 +66,14 @@ export function applyRouteWorkspace(view: RouteView | null): void {
 export function checkWorkspaceExists(view: RouteView | null): void {
   if (!listed || !workspace.names) return
   if (workspace.names.includes(workspace.current)) return
+  if (!fresh) {
+    // The refetch's own landing re-runs this check against `lastView`
+    if (refetching) return
+    refetching = true
+    invalidateWorkspaces()
+    void loadWorkspaces().finally(() => (refetching = false))
+    return
+  }
   const missing = workspace.current
   workspace.current = DEFAULT_WORKSPACE
   rememberWorkspace(DEFAULT_WORKSPACE)
@@ -94,7 +109,12 @@ export function loadWorkspaces(): Promise<void> {
         // A route naming a workspace that has gone away (deleted elsewhere,
         // or a stale bookmark) is caught here for the route the app opened
         // on; `applyRouteWorkspace` catches every one after
-        checkWorkspaceExists(lastView)
+        fresh = true
+        try {
+          checkWorkspaceExists(lastView)
+        } finally {
+          fresh = false
+        }
       } catch {
         listed = false
         workspace.names = [DEFAULT_WORKSPACE]

From 5e0ffc0fedbd76a4d06934592f09815f9d4c7a3e Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 15:26:17 -0600
Subject: [PATCH 50/74] docs(proposals): #1166 - design record for H3
 guide-chain drift counters

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../complete/h3-guide-chain-drift-complete.md | 182 ++++++++++++++++++
 docs/proposals/todo.md                        |  11 ++
 2 files changed, 193 insertions(+)
 create mode 100644 docs/proposals/complete/h3-guide-chain-drift-complete.md

diff --git a/docs/proposals/complete/h3-guide-chain-drift-complete.md b/docs/proposals/complete/h3-guide-chain-drift-complete.md
new file mode 100644
index 00000000..eafe6104
--- /dev/null
+++ b/docs/proposals/complete/h3-guide-chain-drift-complete.md
@@ -0,0 +1,182 @@
+# H3 guide-chain drift counters: latent anchor and guide noise (#1166)
+
+Written by model `claude-opus-5-5` via provider `anthropic` (close-out session,
+2026-11-02). Don filed the idea on 2026-11-02 from a review of
+[roycho87/NerdNotes](https://github.com/roycho87/NerdNotes). Plan v2 is the
+plan comment on #1166, which Don approved as written with all defaults. It was
+built as three stages: #1173, #1174 and #1175. The plan comment is still the
+full design; this file records what shipped, what the probe found and what is
+still open.
+
+## The problem
+
+On an H3 chain with `continuity: "guide"`, colour and texture drift build up
+segment by segment. The held guide rows reach the model almost clean at every
+step, so each segment copies its carried frames, drift included. H3MD Director
+finds the drift is born as a slow shift in the per-channel latent mean and
+spread. The goal was two off-by-default counters, measured on a 10-segment chain
+on a static scene and on a moving-camera scene.
+
+## Verdict: build smaller (cheap arms first, then probe)
+
+The plan ranked five arms by cost and built the two that don't touch the denoise
+loop, the row plan or the refine schedule:
+
+- **Arm 5, latent statistics anchor** (H3MD concept): `chain.anchor` plus the
+  H3 argument `latent_anchor`. Built as stage 1.
+- **Arm 2, fixed guide noise** (PrefixStream concept, its paired encode):
+  `guide_noise`. Built as stage 2.
+- **Arm 3, the in-loop per-step ramp** (NerdNotes/Lumifel): cut. *What would
+  bring it back:* arm 2 null and residual texture drift after arm 5. The probe
+  found arm 2 not null, so it stays cut. The plan's "Arm 3, for the record"
+  keeps the mechanism (v1's per-row re-noise with H3MD's ramp shape) for a
+  revival.
+- **Arm 4, keystone and capstone** (NerdNotes): cut, replaced by arm 5. The
+  keystone alone "did little"; the capstone needed a new late-gated guide
+  grammar. *What would bring it back:* arm 5 holds the static scene but fails
+  the arc. **That trigger fired** (see the probe), so a sequence-level anchor is
+  a live candidate if Don wants to pursue the arc.
+
+Non-goals that stayed out: audio latents, `ref2va`, any default change, MCP or
+REST surface, and #1033's seam colour-match.
+
+## Plan history
+
+- **v1.** The in-loop per-step ramp (arm 3) as the main build. Don sent it back:
+  Q1 brought the anchors back into view, Q2 asked for a re-plan around all five
+  arms.
+- **v2 (approved, 2026-11-02).** Arms 5 and 2 first, then a two-scene,
+  two-round probe.
+  - Q1 [scope]: yes. Arm 3 only on its trigger.
+  - Q2 [scope]: yes. Arm 5 replaces arm 4.
+  - Q3 [spend]: no keystone arm in round 1.
+  - Q4 [spend]: both scenes, about 2¼ h for round 1 plus about 45 min for round 2.
+  - Q5 [scope]: syntax as written. If the arc flattens under arm 5, the
+    stage 3 reading names `latent_anchor_strength` as the follow-up.
+  - Q6 [taste]: **still open** (see "Deferred and open").
+
+## What was built
+
+### Stage 1 (#1173): `chain.anchor` + `latent_anchor`
+
+Final merge `5921be2c` on `develop`, deployed to lem.
+
+- **`latent_anchor`**, an H3 `t2va`/`fl2va` call argument taking video frames.
+  `dw/pipeline_processors/h3_anchor.py` holds the per-channel mean/std match and
+  the frame prep; `h3_anchor_steps.py` holds `DwH3LatentAnchorStep`, which
+  VAE-encodes the anchor with `encode_vae_condition` and rescales the denoised
+  latents to its per-channel statistics. Without the argument the latents pass
+  through as the same object.
+- **Pre-decode step:** `insert_latent_anchor` puts `dw_latent_anchor` right
+  after `after_denoise` in every core denoise sequence of `t2va` and `fl2va`.
+  `ref2va` gets no step, and `latent_anchor` with `references` is refused at
+  validate and at run time.
+- **`chain.anchor`:** `"none"` (default) or `"segment_1"`. With `"segment_1"`
+  the chain keeps segment 1's decoded frames and passes them as
+  `latent_anchor` to every later segment. One rule,
+  `h3_rules.chain_anchor_problem`, is shared by validate and `ChainConfig`; any
+  other value, of any type, is refused naming both allowed values. The schema
+  no longer types the key, so the rule is its only owner.
+- **Step rule:** `dw/guides.py` `_takes_guides_problem` is the one step rule for
+  `guides`, `latent_anchor` and `chain.anchor: "segment_1"`.
+- **Location policy:** `latent_anchor` is a media argument
+  (`dw/locations.py` `PIPELINE_MEDIA_ARGUMENTS`), so it meets the same location
+  policy as a guide's `video` at validate and submission. The run-time loader
+  already enforced it.
+- Docs: `WORKFLOW_GUIDE.md` H3 section and guide-chain table, the
+  `ARCHITECTURE.md` H3 row, `ATTRIBUTIONS.md` (H3MD Director).
+
+**Bounces: 3.**
+1. Architecture review: a copied step rule (`_takes_anchor_problem`) was a
+   second owner. It was deleted in favour of `_takes_guides_problem`.
+2. C-F671 1c / C-F676: a non-string `chain.anchor` was refused by the schema
+   with a bare type error that didn't name the values. Fixed by dropping the
+   schema type.
+3. SE-F061: a location-policy gap on `latent_anchor`, filed and handled as a
+   private advisory. Fixed by `PIPELINE_MEDIA_ARGUMENTS`.
+
+Cases verified: C-F671 to C-F676, SE-F061.
+
+### Stage 2 (#1174): `guide_noise`
+
+Merged as `bdbe4723` on `develop` (commits 3dbf8a04, 9363e97c), deployed to lem.
+
+- **`guide_noise`**, an H3 `t2va`/`fl2va` float in `[0, 1)`. The range rule is
+  `h3_rules.guide_noise_problem`, called by validate (`guides.guide_noise_errors`)
+  and the run-time refusal (`Pipeline._with_guide_noise`).
+- **Cosine taper** per guide frame (`h3_rules.guide_noise_taper`): `guide_noise`
+  at the first frame, exactly 0 at the last, the one next to the join. A
+  one-frame guide stays clean.
+- **Paired encode** in `DwH3GuideLayoutStep._encode_guides`: the clean and the
+  noisy pixels are encoded with the same seed, and `E(noisy) − E(clean)` is added
+  to the clean latent, so 0 is exactly today's latent. Guide rows are held as
+  before, the row plan and refine schedule are unchanged, and guide audio rows
+  are untouched.
+- A warning when nothing is noised (no `guides` and no guide chain).
+- Docs: `WORKFLOW_GUIDE.md`, `ARCHITECTURE.md`, `ATTRIBUTIONS.md` (PrefixStream,
+  MIT; Lumifel and NerdNotes for the diagnosis).
+
+**Bounces: 0.** Cases verified: C-F677 to C-F680.
+
+### Stage 3 (#1175): drift probe on lem
+
+No code. Workspace `probe-1166`, workflow `drift-chain` (a copy of
+`templates/minimax/chained-segments`: fl2va, turbo-4step-keyframe at 5 steps,
+int4, 960×544, seed 1166, `continuity: "guide"`, `guide_frames` 22, 10
+segments, 1042 frames). Measured at f62 / f481 / f991 (segments 1 / 5 / 10)
+with `compare_outputs(texture_bands=true)` on a subject-free crop, and seams
+with `analyze_seams`. Round 2 (B+C) ran because B and C each beat A on some
+measure. The two comparison comments on #1166 hold the full tables, job ids and
+frames; the arc table in the second comment replaces the first's.
+
+| Arm | Static scene, seg10 fine Y × A / look mean | Arc scene, seg10 / look mean / jump mean |
+|---|---|---|
+| A control | 1.00 / 1.67 | blown out toward white / 0.98 / 1.13 |
+| B `guide_noise` 0.4 | 1.47 (rougher) / 1.11 | most coherent by eye / 1.07 / 1.09 |
+| C `chain.anchor: "segment_1"` | 0.40 / 1.27 | crawling sky texture / 3.62 / 2.20 |
+| B+C | 0.29 (cleanest) / 0.69 | worst: full-frame posterized texture / 4.64 / 1.88 |
+
+The lead's reading (not the verdict):
+- **The anchor is a static-scene tool at full strength.** It is the best arm on
+  the static scene, and with `guide_noise` it is better still. On the arc it
+  pulls the lighting back to segment 1 at seams (lightness steps of −15 to −27)
+  and lays a crawling texture over the sky. The camera keeps moving in every
+  arm, so it doesn't freeze the shot.
+- **`guide_noise` is not null.** It is the only arm that doesn't hurt the arc,
+  but it roughens static texture.
+- Arm 4's trigger fired; arm 3's did not.
+
+**Bounces: 1.** C-F681: the first arc arms B, C and B+C didn't share arm A's
+segment 1 (about 22 dB). Neither arm changes segment 1 (diagnostics
+`33b3c0ddd350`, `326709c7ad8d`, `f6a218511cb4`); that batch went wrong on the
+long-lived cuda:0 worker, and the cause wasn't found. The three arms were re-run
+with `rerun_job` and matched A exactly. Case verified: C-F681.
+
+## Cost and bounces
+
+| Stage | Bounces | Cost |
+|---|---|---|
+| #1173 | 3 (architecture, C-F671 1c, SE-F061) | Not recorded on the stage |
+| #1174 | 0 | Not recorded on the stage |
+| #1175 | 1 (C-F681, arc segment 1) | Not recorded on the stage. Lem time ran over the plan's 3 h by the three arc re-runs and their diagnostics |
+
+## Deferred and open
+
+- **Q6, Don's taste verdict** on the comparison comments: does B or C hold the
+  look better than A without softening the seam or flattening the arc's real
+  changes? "Yes" brings a template variable and an H3 skill rule as a follow-up
+  plan. "No" leaves both arms opt-in and unrecommended. Until he rules, no
+  template or skill sets either key.
+- **`latent_anchor_strength`** (Q5's named follow-up): the arc shows the full
+  match fighting the scene's real lighting change. Not filed yet; it rests on
+  Q6.
+- **Arm 4 (sequence-level anchor)**: its return trigger fired. It comes back
+  only if Don wants the arc solved after Q6.
+- **Arm 3 (in-loop ramp)**: stays cut; its trigger did not fire.
+- **Unresolved from the build:** what happens when a step's own `latent_anchor`
+  and `chain.anchor: "segment_1"` are both set (the tester's spec note). No case
+  asserts it.
+- **One seed, two scenes.** Read the direction, not `proven`.
+- Related: #1033 (seam colour-match, parked), #1150 (latent-tail continuity,
+  parked), #1151 (anti-burn LoRA, awaiting its own taste verdict), #1170
+  (`chain.seam_source`, sequenced after stage 1 since both edit `ChainConfig`).
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index 0cd97d2c..61419342 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -37,6 +37,17 @@ remaining deferred fix is recorded in
 
 ## Shipped since the ranking
 
+- **H3 guide-chain drift counters** (#1166, stages #1173-#1175), shipped
+  2026-11-02. Two off-by-default H3 options: `latent_anchor` with
+  `chain.anchor: "segment_1"`, a pre-decode per-channel latent statistics
+  match to segment 1, and `guide_noise`, a cosine-tapered paired-encode noise
+  on video guides. A 10-segment, two-scene probe on lem found the anchor
+  (with or without guide noise) best on a static scene and worst on a
+  moving-camera arc, and guide noise alone the only arm that didn't hurt the
+  arc. No template or skill sets either until Don's taste verdict (Q6);
+  `latent_anchor_strength` is the named follow-up. The in-loop ramp and the
+  keystone/capstone were cut: `complete/h3-guide-chain-drift-complete.md`.
+
 - **Texture-band drift measurement on `compare_outputs`** (#1152, stage
   #1160), shipped 2026-11-02. `compare_outputs(texture_bands=true)` reports
   RMS energy in the fine 2-8, mid 8-32 and coarse 32-128 px bands (luma, R−G,

From 502dca2cfaa5fca5a6c260746e3dfb9621b490b8 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 15:35:59 -0600
Subject: [PATCH 51/74] feat(flow): implement support for chained segments in
 workflow processing

---
 ui/src/lib/editor/FlowLegend.svelte |   9 +++
 ui/src/lib/editor/FlowView.svelte   | 114 +++++++++++++++++++++++++---
 ui/src/lib/editor/FlowView.test.ts  |  95 +++++++++++++++++++++++
 ui/src/lib/editor/flowLayout.ts     |  27 ++++++-
 ui/src/lib/flow.test.ts             |  78 +++++++++++++++++++
 ui/src/lib/flow.ts                  |  91 ++++++++++++++++++++++
 ui/src/lib/pages/JobPage.svelte     |   3 +
 ui/src/lib/runstate.test.ts         |  43 +++++++++++
 ui/src/lib/runstate.ts              |  27 +++++++
 9 files changed, 476 insertions(+), 11 deletions(-)

diff --git a/ui/src/lib/editor/FlowLegend.svelte b/ui/src/lib/editor/FlowLegend.svelte
index 42d266ce..c1bc1780 100644
--- a/ui/src/lib/editor/FlowLegend.svelte
+++ b/ui/src/lib/editor/FlowLegend.svelte
@@ -5,6 +5,7 @@
     edges = false,
     fanIn = false,
     forEach = false,
+    chain = false,
     done = false,
     active = false,
     failed = false,
@@ -14,6 +15,7 @@
     edges?: boolean
     fanIn?: boolean
     forEach?: boolean
+    chain?: boolean
     done?: boolean
     active?: boolean
     failed?: boolean
@@ -44,6 +46,13 @@
       <span class="swatch chip"></span><code>for_each</code> entries
     </li>
   {/if}
+  {#if chain}
+    <li
+      title="a chained step: the pipeline runs once per segment, inset, each seam's carry between them, and the segments are stitched into one video"
+    >
+      <span class="swatch chip"></span><code>chain</code> segments
+    </li>
+  {/if}
   {#if done}
     <li title="this run has finished the step; its time is on the right">
       <span class="swatch done"></span>finished
diff --git a/ui/src/lib/editor/FlowView.svelte b/ui/src/lib/editor/FlowView.svelte
index f6087270..7b74a931 100644
--- a/ui/src/lib/editor/FlowView.svelte
+++ b/ui/src/lib/editor/FlowView.svelte
@@ -5,6 +5,7 @@
   import {
     BOX_H,
     BOX_W,
+    CHAIN_SUMMARY_Y,
     CHIP_CHARS,
     CHIP_CHARS_TIMED,
     CHIP_H,
@@ -13,9 +14,13 @@
     DETAIL_CHARS,
     NAME_CHARS,
     NAME_CHARS_WITH_TAG,
+    SEAM_CHARS,
+    SEGMENT_STEP,
+    SEGMENT_TOP,
     fit,
     heightOf,
     layoutGraph,
+    segmentChips,
   } from './flowLayout'
 
   let {
@@ -31,6 +36,7 @@
     doneMembers = [],
     nodeTimes = {},
     memberTimes = {},
+    activeSegments = {},
   }: {
     workflow: Record<string, any>
     onselect?: (stepName: string) => void
@@ -57,6 +63,9 @@
     nodeTimes?: Record<string, number>
     /** Seconds each finished for_each member took, engine names. */
     memberTimes?: Record<string, number>
+    /** The segment, 1-based, each chained step is on or last ran - its
+     * generating phases (runstate.ts chainSegments). */
+    activeSegments?: Record<string, number>
   } = $props()
 
   const showsRun = $derived(
@@ -87,17 +96,40 @@
           : '',
   )
 
+  // A segment's state follows its step's: every segment of a finished step
+  // is done, and on a running or failed one, those before the segment it
+  // is on are done and that one carries the step's state
+  const segmentStateOf = $derived((node: string, segment: number) => {
+    const state = stateOf(node)
+    if (state === 'done') return 'done'
+    if (state !== 'active' && state !== 'failed') return ''
+    const on = activeSegments[node]
+    if (on === undefined) return ''
+    return segment < on ? 'done' : segment === on ? state : ''
+  })
+
   const graph = $derived(dataFlowGraph(workflow))
   const hasForEach = $derived(graph.nodes.some((node) => node.forEach))
+  const hasChain = $derived(
+    graph.nodes.some((node) => node.chain && !node.forEach),
+  )
   const hasPending = $derived(
     graph.nodes.some((node) => stateOf(node.name) === 'pending'),
   )
-  // One plain step has no flow to draw: it is shown as a line, not a canvas
+  // One plain step has no flow to draw: it is shown as a line, not a canvas.
+  // A chained step is not plain - its segments are the flow
   const lone = $derived(
-    graph.nodes.length === 1 && !graph.nodes[0].forEach
+    graph.nodes.length === 1 && !graph.nodes[0].forEach && !graph.nodes[0].chain
       ? graph.nodes[0]
       : undefined,
   )
+
+  function chainLabel(node: FlowNode): string {
+    if (!node.chain || node.forEach) return ''
+    if (node.chain.fromAudio) return 'chain · length from audio'
+    const n = node.chain.segments
+    return n === null ? 'chain' : `chain · ${n} segment${n === 1 ? '' : 's'}`
+  }
   const canSelect = (name: string) => !!onselect && (selectable?.(name) ?? true)
 
   /** The parts of a node's labels that did not fit, in full, for its tooltip. */
@@ -181,6 +213,7 @@
       edges={graph.edges.length > 0}
       fanIn={graph.fanIn.size > 0}
       forEach={hasForEach}
+      chain={hasChain}
       done={doneSteps.length > 0}
       active={activeStep !== undefined}
       failed={failedStep !== undefined}
@@ -251,7 +284,7 @@
               clip-path={boxH > BOX_H
                 ? `url(#flow-nodebox-${boxH})`
                 : 'url(#flow-nodebox)'}
-              aria-label={`step ${node.name}, ${kindLabel(node.kind)}${stateOf(node.name) ? ', ' + stateOf(node.name) : ''}${node.isEntryPoint ? ', entry point' : ''}${fanIn ? ', fan-in: ' + fanIn.label : ''}${node.forEach ? (node.members?.length ? `, for_each with ${node.members.length} entries` : ', for_each') : ''}`}
+              aria-label={`step ${node.name}, ${kindLabel(node.kind)}${stateOf(node.name) ? ', ' + stateOf(node.name) : ''}${node.isEntryPoint ? ', entry point' : ''}${fanIn ? ', fan-in: ' + fanIn.label : ''}${node.forEach ? (node.members?.length ? `, for_each with ${node.members.length} entries` : ', for_each') : ''}${node.chain && !node.forEach ? (node.chain.segments ? `, chain of ${node.chain.segments} segments` : ', chain') : ''}`}
               {...nodeAttributes(node.name)}
             >
               {#if overflowTitle(node)}
@@ -330,6 +363,61 @@
                 <text x="11" y={CHIP_TOP + 12} class="membersunknown"
                   >for_each</text
                 >
+              {:else if node.chain}
+                <!-- The segments the one step runs, in order, each seam's
+                     carry written between them; the run's segment from its
+                     generating phases -->
+                <text x="11" y={CHAIN_SUMMARY_Y} class="chainsummary"
+                  >{chainLabel(node)}</text
+                >
+                {#each Array.from({ length: segmentChips(node) }, (_, i) => i) as i (i)}
+                  {@const y = SEGMENT_TOP + i * SEGMENT_STEP}
+                  {@const state = segmentStateOf(node.name, i + 1)}
+                  <g
+                    class="segment"
+                    class:done={state === 'done'}
+                    class:active={state === 'active'}
+                    class:failed={state === 'failed'}
+                    aria-label={`segment ${i + 1}${state ? ', ' + state : ''}`}
+                  >
+                    <rect
+                      x="10"
+                      {y}
+                      width={BOX_W - 20}
+                      height={CHIP_H}
+                      rx="4"
+                      class="chip"
+                    />
+                    <text x="15" y={y + 11} class="chiplabel"
+                      >segment {i + 1}</text
+                    >
+                    {#if i === 0 && node.chain.anchor}
+                      <g>
+                        <title
+                          >segment 1's latents anchor every later segment's
+                          (chain anchor: segment_1)</title
+                        >
+                        <text
+                          x={BOX_W - 15}
+                          y={y + 11}
+                          class="anchortag"
+                          text-anchor="end">anchor</text
+                        >
+                      </g>
+                    {/if}
+                  </g>
+                  {#if i < segmentChips(node) - 1}
+                    <text x="15" y={y + CHIP_H + 10} class="seamlabel"
+                      >{#if `↓ ${node.chain.seam}`.length > SEAM_CHARS}<title
+                          >{node.chain.seam}</title
+                        >{/if}{fit(
+                        `↓ ${node.chain.seam}`,
+                        SEAM_CHARS,
+                        'head',
+                      )}</text
+                    >
+                  {/if}
+                {/each}
               {/if}
             </g>
             {#if fanIn}
@@ -517,26 +605,31 @@
   /* The entries a for_each step runs, inset beneath its header. Machine
      state on their edges, as on the nodes: done green, running the
      safelight amber. */
-  .member .chip {
+  .member .chip,
+  .segment .chip {
     fill: var(--panel-2);
     stroke: var(--line);
     stroke-width: 1;
   }
-  .member.done .chip {
+  .member.done .chip,
+  .segment.done .chip {
     stroke: var(--good);
     stroke-width: 1.5;
   }
-  .member.failed .chip {
+  .member.failed .chip,
+  .segment.failed .chip {
     stroke: var(--bad);
     stroke-width: 1.5;
   }
-  .member.active .chip {
+  .member.active .chip,
+  .segment.active .chip {
     stroke: var(--live);
     stroke-width: 1.5;
     animation: dw-pulse 1.6s ease-in-out infinite;
   }
   @media (prefers-reduced-motion: reduce) {
-    .member.active .chip {
+    .member.active .chip,
+    .segment.active .chip {
       animation: none;
     }
   }
@@ -545,7 +638,10 @@
     fill: var(--ink);
     font-family: var(--font-mono);
   }
-  .membersunknown {
+  .membersunknown,
+  .chainsummary,
+  .seamlabel,
+  .anchortag {
     font-size: 9px;
     fill: var(--muted);
     font-family: var(--font-mono);
diff --git a/ui/src/lib/editor/FlowView.test.ts b/ui/src/lib/editor/FlowView.test.ts
index 721eb026..d45cc8e9 100644
--- a/ui/src/lib/editor/FlowView.test.ts
+++ b/ui/src/lib/editor/FlowView.test.ts
@@ -354,3 +354,98 @@ it('shows a lone plain step as one line, not a canvas', () => {
   expect(line.textContent).toContain('gen')
   expect(line.textContent).toContain('Fake')
 })
+
+describe('chained segments', () => {
+  // The chained-segments template as a realized run carries it: one step
+  const chainWorkflow = {
+    variables: { segments: 3, anchor: 'segment_1' },
+    steps: [
+      {
+        name: 'clip',
+        pipeline: {
+          configuration: { component_type: 'ModularPipeline' },
+          chain: {
+            segments: 'variable:segments',
+            anchor: 'variable:anchor',
+            trim_frames: 2,
+            crossfade_ms: 80,
+          },
+        },
+      },
+    ],
+  }
+  const segmentsOf = (container: HTMLElement) => [
+    ...container.querySelectorAll('g.segment'),
+  ]
+
+  it('draws a lone chained step as a box of its segments, not one line', () => {
+    const { container } = render(FlowView, { workflow: chainWorkflow })
+    expect(container.querySelector('.loneline')).toBeNull()
+    const clip = nodeFor(container, 'clip')
+    expect(clip.getAttribute('aria-label')).toContain('chain of 3 segments')
+    expect(clip.querySelector('.chainsummary')?.textContent).toBe(
+      'chain · 3 segments',
+    )
+    const chips = segmentsOf(container)
+    expect(
+      chips.map((c) => c.querySelector('.chiplabel')?.textContent),
+    ).toEqual(['segment 1', 'segment 2', 'segment 3'])
+    // The anchor every later segment is pulled toward is segment 1
+    expect(chips[0].querySelector('.anchortag')?.textContent).toBe('anchor')
+    expect(chips[1].querySelector('.anchortag')).toBeNull()
+    // One seam between each pair, saying what crosses it
+    expect(
+      [...clip.querySelectorAll('.seamlabel')].map((s) => s.textContent),
+    ).toEqual(['↓ last frame · trim 2 · 80ms', '↓ last frame · trim 2 · 80ms'])
+  })
+
+  it('colors the segments behind the one a run is on, and that one live', () => {
+    const { container } = render(FlowView, {
+      workflow: chainWorkflow,
+      activeStep: 'clip',
+      activeSegments: { clip: 2 },
+    })
+    const states = segmentsOf(container).map((c) =>
+      ['done', 'active', 'failed'].find((s) => c.classList.contains(s)),
+    )
+    expect(states).toEqual(['done', 'active', undefined])
+    expect(segmentsOf(container)[1].getAttribute('aria-label')).toBe(
+      'segment 2, active',
+    )
+  })
+
+  it('marks the segment a failed run stopped in, and every one of a finished step', () => {
+    const failed = render(FlowView, {
+      workflow: chainWorkflow,
+      failedStep: 'clip',
+      activeSegments: { clip: 3 },
+    }).container
+    expect(
+      segmentsOf(failed).map((c) => c.classList.contains('failed')),
+    ).toEqual([false, false, true])
+    const done = render(FlowView, {
+      workflow: chainWorkflow,
+      doneSteps: ['clip'],
+    }).container
+    expect(segmentsOf(done).every((c) => c.classList.contains('done'))).toBe(
+      true,
+    )
+  })
+
+  it('says where the length comes from when the count is not in the definition', () => {
+    const fromAudio = {
+      steps: [
+        {
+          name: 'clip',
+          pipeline: { chain: { match_audio: true } },
+        },
+      ],
+    }
+    const { container } = render(FlowView, { workflow: fromAudio })
+    const clip = nodeFor(container, 'clip')
+    expect(clip.querySelector('.chainsummary')?.textContent).toBe(
+      'chain · length from audio',
+    )
+    expect(segmentsOf(container)).toHaveLength(0)
+  })
+})
diff --git a/ui/src/lib/editor/flowLayout.ts b/ui/src/lib/editor/flowLayout.ts
index 0b01a870..0f1ad7cc 100644
--- a/ui/src/lib/editor/flowLayout.ts
+++ b/ui/src/lib/editor/flowLayout.ts
@@ -31,6 +31,15 @@ export const CHIP_STEP = 19
 export const CHIP_CHARS = 26
 export const CHIP_CHARS_TIMED = 17 // a finished chip's run time takes the right end
 export const CHIP_TOP = BOX_H - 2
+// Segment chips: a chained step's box holds a summary line, then one
+// chip per segment with each seam's carry written in the gap between.
+// A long chain keeps the summary alone rather than a box taller than the
+// page.
+export const CHAIN_SUMMARY_Y = CHIP_TOP + 10
+export const SEGMENT_TOP = CHIP_TOP + 16
+export const SEGMENT_STEP = CHIP_H + 14
+export const SEAM_CHARS = 28
+export const MAX_SEGMENT_CHIPS = 12
 // The gap ROW_H left between fixed-height boxes, kept for the
 // height-aware stacking below
 const ROW_GAP = ROW_H - BOX_H
@@ -53,13 +62,27 @@ export function fit(text: string, max: number, keep: 'head' | 'tail'): string {
     : '…' + text.slice(text.length - max + 1)
 }
 
+/** The segments a chained step's box draws as chips - none for a count
+ * the definition does not give, or one too long to draw. A for_each step
+ * draws its members instead: the chain reruns inside each. */
+export function segmentChips(node: FlowNode): number {
+  const count = node.forEach ? null : node.chain?.segments
+  return count && count <= MAX_SEGMENT_CHIPS ? count : 0
+}
+
 /** The box's height: the standard header, plus a chip row per entry for
  * a list-driven step - or one empty slot when the step carries
- * for_each but its list cannot be read from the definition. */
+ * for_each but its list cannot be read from the definition. A chained
+ * step holds its summary line and segment chips. */
 export function heightOf(node: FlowNode): number {
   const count = node.members?.length ?? 0
   if (count) return CHIP_TOP + (count - 1) * CHIP_STEP + CHIP_H + 6
-  return node.forEach ? BOX_H + 26 : BOX_H
+  if (node.forEach) return BOX_H + 26
+  if (!node.chain) return BOX_H
+  const segments = segmentChips(node)
+  return segments
+    ? SEGMENT_TOP + (segments - 1) * SEGMENT_STEP + CHIP_H + 6
+    : SEGMENT_TOP
 }
 
 /** One line per producer -> consumer pair, carrying every argument it
diff --git a/ui/src/lib/flow.test.ts b/ui/src/lib/flow.test.ts
index 362e6013..56ad27e5 100644
--- a/ui/src/lib/flow.test.ts
+++ b/ui/src/lib/flow.test.ts
@@ -439,3 +439,81 @@ describe('the shared reference cases', () => {
     if (flagged.length === 0) expect(messages).toEqual([])
   })
 })
+
+describe('chained segments', () => {
+  const chainStep = (chain: Record<string, unknown>) => ({
+    name: 'clip',
+    pipeline: { configuration: { component_type: 'ModularPipeline' }, chain },
+  })
+
+  it("reads a declared segment count and the seam's last-frame bookkeeping", () => {
+    // The chained-segments template's shape: the count and continuity are
+    // variables, which a realized workflow always carries
+    const wf = {
+      variables: { segments: 3, continuity: 'last_frame', anchor: 'segment_1' },
+      steps: [
+        chainStep({
+          segments: 'variable:segments',
+          continuity: 'variable:continuity',
+          anchor: 'variable:anchor',
+          trim_frames: 2,
+          crossfade_ms: 80,
+        }),
+      ],
+    }
+    expect(dataFlowGraph(wf).nodes[0].chain).toEqual({
+      segments: 3,
+      fromAudio: false,
+      seam: 'last frame · trim 2 · 80ms',
+      anchor: true,
+    })
+  })
+
+  it('falls back to the engine defaults the block leaves out', () => {
+    const wf = { steps: [chainStep({ segments: 2 })] }
+    expect(dataFlowGraph(wf).nodes[0].chain).toEqual({
+      segments: 2,
+      fromAudio: false,
+      seam: 'last frame · trim 1 · 75ms',
+      anchor: false,
+    })
+  })
+
+  it('names what a guide or a video reference carries across the seam', () => {
+    const seam = (chain: Record<string, unknown>) =>
+      dataFlowGraph({ steps: [chainStep({ segments: 2, ...chain })] }).nodes[0]
+        .chain!.seam
+    expect(seam({ continuity: 'guide' })).toBe('guide · 22 frames')
+    expect(seam({ continuity: 'guide', guide_frames: 39 })).toBe(
+      'guide · 39 frames',
+    )
+    expect(seam({ continuity: 'last_segment' })).toBe('video reference')
+    expect(
+      seam({
+        continuity: 'last_segment',
+        carry_frames: 24,
+        carry_audio: false,
+      }),
+    ).toBe('video reference · 24 frames · no audio')
+    expect(seam({ continuity: 'variable:unset' })).toBe('carry')
+  })
+
+  it('leaves the count unknown when the length comes from audio or an unset variable', () => {
+    const fromAudio = dataFlowGraph({
+      steps: [chainStep({ match_audio: true })],
+    }).nodes[0].chain!
+    expect(fromAudio.segments).toBeNull()
+    expect(fromAudio.fromAudio).toBe(true)
+    const unset = dataFlowGraph({
+      steps: [chainStep({ segments: 'variable:missing' })],
+    }).nodes[0].chain!
+    expect(unset.segments).toBeNull()
+    expect(unset.fromAudio).toBe(false)
+  })
+
+  it('is null on a step with no chain', () => {
+    expect(dataFlowGraph({ steps: [step('plain', {})] }).nodes[0].chain).toBe(
+      null,
+    )
+  })
+})
diff --git a/ui/src/lib/flow.ts b/ui/src/lib/flow.ts
index 67804d8c..41e7b898 100644
--- a/ui/src/lib/flow.ts
+++ b/ui/src/lib/flow.ts
@@ -230,6 +230,26 @@ export interface FlowNode {
    * names the run's members carry after the `@`. Null when there is no
    * for_each or the list cannot be read statically. */
   members: string[] | null
+  /** What the step's pipeline `chain` block runs, or null without one. */
+  chain: ChainSummary | null
+}
+
+/** A chained pipeline step, read off its `chain` block: the pipeline runs
+ * once per segment inside the one step, carrying continuity across each
+ * seam, and the segments are stitched into one video
+ * (dw/pipeline_processors/chain.py). */
+export interface ChainSummary {
+  /** The segment count, when the definition gives it - a literal or a
+   * declared variable, which a realized workflow always holds. Null under
+   * `match_audio`, or for a variable left unset. */
+  segments: number | null
+  /** True when the audio's length decides the count at run time. */
+  fromAudio: boolean
+  /** What each seam carries into the next segment, in a few words. */
+  seam: string
+  /** True under `anchor: "segment_1"`: segment 1's latents anchor every
+   * later segment's (#1166). */
+  anchor: boolean
 }
 
 export interface FlowEdge {
@@ -302,6 +322,76 @@ function forEachMembers(
   )
 }
 
+// The chain block's engine defaults (dw/workflow_schema.json $defs/chain)
+const CHAIN_DEFAULTS: Record<string, unknown> = {
+  continuity: 'last_frame',
+  trim_frames: 1,
+  crossfade_ms: 75,
+  guide_frames: 22,
+  carry_audio: true,
+  anchor: 'none',
+}
+
+/** The step's `chain` block summarized for the graph, or null. A value
+ * may be a `variable:`, read from the declared variables - undefined when
+ * it names none, or one left null. */
+function chainSummary(
+  workflow: Record<string, any>,
+  step: Record<string, any>,
+): ChainSummary | null {
+  const chain = step.pipeline?.chain
+  if (chain === null || typeof chain !== 'object') return null
+  const read = (key: string): unknown => {
+    const value = key in chain ? chain[key] : CHAIN_DEFAULTS[key]
+    const name = referenceName(value, VARIABLE)
+    if (name === null) return value
+    return workflow.variables?.[name] ?? undefined
+  }
+  const segments = read('segments')
+  const parts: Array<string | null> = []
+  switch (read('continuity')) {
+    case 'last_frame': {
+      const trim = read('trim_frames')
+      const fade = read('crossfade_ms')
+      parts.push(
+        'last frame',
+        typeof trim === 'number' ? `trim ${trim}` : null,
+        typeof fade === 'number' ? `${fade}ms` : null,
+      )
+      break
+    }
+    case 'guide': {
+      const frames = read('guide_frames')
+      parts.push(
+        'guide',
+        typeof frames === 'number' ? `${frames} frames` : null,
+      )
+      break
+    }
+    case 'last_segment': {
+      const frames = read('carry_frames')
+      parts.push(
+        'video reference',
+        typeof frames === 'number' ? `${frames} frames` : null,
+      )
+      break
+    }
+    default:
+      parts.push('carry')
+  }
+  if (read('continuity') !== 'last_frame' && read('carry_audio') === false)
+    parts.push('no audio')
+  return {
+    segments:
+      typeof segments === 'number' && segments >= 1 && !chain.match_audio
+        ? segments
+        : null,
+    fromAudio: chain.match_audio === true,
+    seam: parts.filter(Boolean).join(' · '),
+    anchor: read('anchor') === 'segment_1',
+  }
+}
+
 /** The read-only data-flow view's graph: one node per step, one edge per
  * `previous_result:<step>` reference (labeled with the attribute that
  * carries it), entry points flagged, and fan-in points - steps combining
@@ -321,6 +411,7 @@ export function dataFlowGraph(workflow: Record<string, any>): DataFlowGraph {
       producedCount: producedCount(step),
       forEach: FOR_EACH_KEY in step,
       members: forEachMembers(workflow, step),
+      chain: chainSummary(workflow, step),
     }
   })
   const edges: FlowEdge[] = []
diff --git a/ui/src/lib/pages/JobPage.svelte b/ui/src/lib/pages/JobPage.svelte
index 12b87986..a3955721 100644
--- a/ui/src/lib/pages/JobPage.svelte
+++ b/ui/src/lib/pages/JobPage.svelte
@@ -6,6 +6,7 @@
   import { groupResultFiles, unsavedSteps } from '../results'
   import {
     activeMember,
+    chainSegments,
     finishedMembers,
     finishedNodes,
     flowNodeName,
@@ -175,6 +176,7 @@
   // One grain finer than the group: which entries of a for_each step have
   // finished and which is running, in the engine's own `group@entry` names
   const finishedMemberSteps = $derived(finishedMembers(events as JobEvent[]))
+  const segments = $derived(chainSegments(events as JobEvent[]))
   const activeMemberStep = $derived(
     running ? activeMember(events as JobEvent[]) : undefined,
   )
@@ -300,6 +302,7 @@
         selectable={hasResult}
         selectHint="click a step to see its results"
         doneMembers={finishedMemberSteps}
+        activeSegments={segments}
         {nodeTimes}
         {memberTimes}
       />
diff --git a/ui/src/lib/runstate.test.ts b/ui/src/lib/runstate.test.ts
index 3acb7ece..3faebc19 100644
--- a/ui/src/lib/runstate.test.ts
+++ b/ui/src/lib/runstate.test.ts
@@ -3,6 +3,7 @@ import {
   nodeDurations,
   stepDurations,
   activeMember,
+  chainSegments,
   finishedMembers,
   finishedNodes,
   flowNodeName,
@@ -148,3 +149,45 @@ describe('step and node durations', () => {
     expect(stepDurations([bare, ev('step_end', 'x', 3)])).toEqual({})
   })
 })
+
+describe('chainSegments', () => {
+  const start = (step: string, extra = {}) => ({
+    seq: 0,
+    event: 'step_start',
+    step,
+    ...extra,
+  })
+  const segment = (i: number, n: number) => ({
+    seq: 0,
+    event: 'phase',
+    phase: 'generating',
+    detail: `segment ${i}/${n}`,
+  })
+
+  it('names the segment each top-level step is on, from its generating phases', () => {
+    const events = [
+      start('clip'),
+      { seq: 0, event: 'phase', phase: 'loading', detail: 'MiniMax-H3' },
+      segment(1, 3),
+      segment(2, 3),
+    ] as JobEvent[]
+    expect(chainSegments(events)).toEqual({ clip: 2 })
+  })
+
+  it("leaves a sub-workflow's inner chain to no node", () => {
+    const events = [
+      start('compose'),
+      start('inner', { parent_step: 'compose' }),
+      segment(1, 2),
+    ] as JobEvent[]
+    expect(chainSegments(events)).toEqual({})
+  })
+
+  it("ignores a plain step's generating phase, which names the step", () => {
+    const events = [
+      start('gen'),
+      { seq: 0, event: 'phase', phase: 'generating', detail: 'gen' },
+    ] as JobEvent[]
+    expect(chainSegments(events)).toEqual({})
+  })
+})
diff --git a/ui/src/lib/runstate.ts b/ui/src/lib/runstate.ts
index ff952727..a26b62db 100644
--- a/ui/src/lib/runstate.ts
+++ b/ui/src/lib/runstate.ts
@@ -136,3 +136,30 @@ export function nodeDurations(events: JobEvent[]): Record<string, number> {
     [...nodes].map(([node, [start, end]]) => [node, end - start]),
   )
 }
+
+// The detail a chained step's generating phase carries for each segment
+// (Pipeline._run_once, dw/pipeline_processors/pipeline.py); an unchained
+// step's names the step instead
+const SEGMENT_DETAIL = /^segment (\d+)\/\d+$/
+
+/** The segment each chained top-level step is on, 1-based, keyed by the
+ * engine's step name: the latest `segment i/N` generating phase since that
+ * step started. A phase event names no step, so it belongs to the last
+ * step_start before it - and one inside a sub-workflow belongs to no chip
+ * this graph draws, for the reason `finishedNodes` gives. Under fan-out
+ * the chain reruns per iteration, and the latest segment is the one on. */
+export function chainSegments(events: JobEvent[]): Record<string, number> {
+  const segments: Record<string, number> = {}
+  let current: string | null = null
+  for (const event of events) {
+    if (event.event === 'step_start') {
+      current = event.parent_step ? null : (event.step as string)
+      continue
+    }
+    if (current === null || event.event !== 'phase') continue
+    if (event.phase !== 'generating') continue
+    const match = SEGMENT_DETAIL.exec(String(event.detail ?? ''))
+    if (match) segments[current] = Number(match[1])
+  }
+  return segments
+}

From 85e4c39f58fb16e3c2a383672dd592b29fdaad17 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 16:16:15 -0600
Subject: [PATCH 52/74] feat(h3): #1181 - log the latent anchor when applied,
 nothing when unset

The block narrator skips a dw block whose input is absent (idle_without),
so an unanchored H3 call neither names nor runs dw_latent_anchor - the
step was a pass-through there, so the output is unchanged. A call that
sets latent_anchor logs one line naming its source: the chain's segment
1, the reference given, or frames from the arguments.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/WORKFLOW_GUIDE.md                    |  4 +-
 dw/pipeline_processors/chain.py           |  5 ++
 dw/pipeline_processors/h3_anchor.py       |  7 ++-
 dw/pipeline_processors/h3_anchor_steps.py |  3 +
 dw/pipeline_processors/pipeline.py        | 20 +++++++
 dw/pipeline_processors/progress.py        | 11 ++++
 tests/test_h3_anchor.py                   | 62 +++++++++++++++++++
 tests/test_modular_progress.py            | 73 +++++++++++++++++++++++
 8 files changed, 182 insertions(+), 3 deletions(-)

diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index 177e1e0f..d73f15e8 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1874,7 +1874,9 @@ encoded, and takes each latent channel's mean and standard deviation over every
 position. After denoising and before decode, the generated video latent is moved onto
 them, channel by channel: `(x - mean_x) / std_x * std_a + mean_a`. The audio is never
 touched. It is a full match, with no strength setting; without `latent_anchor` the
-step is unchanged, bit for bit.
+step is unchanged, bit for bit, and its job log says nothing about an anchor. A call
+that applies one logs a line naming its source, such as
+`segment 2/3: latent anchor applied - latent statistics from segment 1`.
 
 In a chain, `"anchor": "segment_1"` sets it for you: every segment after the first
 gets segment 1's frames as its `latent_anchor`, so the whole chain is held to the
diff --git a/dw/pipeline_processors/chain.py b/dw/pipeline_processors/chain.py
index 01898eb0..0cd4741c 100644
--- a/dw/pipeline_processors/chain.py
+++ b/dw/pipeline_processors/chain.py
@@ -469,6 +469,10 @@ def run_chain(pipeline, chain_definition, arguments):
             # The denoise counter restarts for every segment - without this the
             # bar rewinds to zero with nothing saying why
             pipeline.segment_label = f"segment {segment.index + 1}/{len(config.plan)}"
+            # Names the anchor in the job log when this segment carries one
+            pipeline.latent_anchor_source = (
+                "segment 1" if segment.index > 0 and anchor_frames is not None else None
+            )
             output = pipeline._run_once(segment_arguments)
             artifact = _single_artifact(output)
 
@@ -536,6 +540,7 @@ def run_chain(pipeline, chain_definition, arguments):
         # The label belongs to the run that set it - on a raise or a cancel
         # as much as on the way out
         pipeline.segment_label = None
+        pipeline.latent_anchor_source = None
 
     if spill is not None:
         frames = _spilled_frames(config, spill)
diff --git a/dw/pipeline_processors/h3_anchor.py b/dw/pipeline_processors/h3_anchor.py
index c740da23..6d68bb6f 100644
--- a/dw/pipeline_processors/h3_anchor.py
+++ b/dw/pipeline_processors/h3_anchor.py
@@ -18,8 +18,11 @@
 
 The block is inserted at load into every t2va/fl2va core-denoise sequence
 (`insert_latent_anchor`) and passes the latent through untouched when no
-`latent_anchor` is given, so a run without one is bit-identical to stock. ref2va
-gets no block, and `anchor_refusal` names why.
+`latent_anchor` is given, so a run without one is bit-identical to stock. The
+block narrator (progress.reported_blocks) skips it then - neither named in the job
+log nor run - and a call that sets one logs a line naming the anchor's source
+(Pipeline._with_latent_anchor, #1181). ref2va gets no block, and `anchor_refusal`
+names why.
 
 The concept follows H3MD Director's segment colour anchoring (concept only, no
 code; docs/ATTRIBUTIONS.md). The block module imports diffusers when it loads, so
diff --git a/dw/pipeline_processors/h3_anchor_steps.py b/dw/pipeline_processors/h3_anchor_steps.py
index 77fa5eea..f14bf679 100644
--- a/dw/pipeline_processors/h3_anchor_steps.py
+++ b/dw/pipeline_processors/h3_anchor_steps.py
@@ -47,6 +47,9 @@ def anchor_statistics(components, frames, height, width):
 
 class DwH3LatentAnchorStep(ModularPipelineBlocks):
     model_name = "minimax-h3"
+    # Without this input the step is a pass-through, so the block narrator
+    # skips it - no anchor phase in a log that never asked for one (#1181)
+    idle_without = LATENT_ANCHOR_INPUT
 
     @property
     def description(self):
diff --git a/dw/pipeline_processors/pipeline.py b/dw/pipeline_processors/pipeline.py
index 30324447..c4ae8061 100644
--- a/dw/pipeline_processors/pipeline.py
+++ b/dw/pipeline_processors/pipeline.py
@@ -185,6 +185,16 @@ def apply_runtime_settings(pipeline_definition, model):
     apply_sparse_settings(pipeline_definition.get("configuration", {}), model)
 
 
+def latent_anchor_source(chain_source, value):
+    """What a `latent_anchor` names, for the job log (#1181): the chain
+    segment that supplied it, the reference given, or frames passed in."""
+    if chain_source:
+        return chain_source
+    if isinstance(value, str):
+        return f"'{value}'"
+    return "the frames given in the step's arguments"
+
+
 class Pipeline:
     """
     Manages pipeline initialization, configuration, and execution.
@@ -230,6 +240,9 @@ def __init__(
         # What a chained run calls the segment it is on, so progress can say
         # which one the denoise counter belongs to - it restarts per segment
         self.segment_label = None
+        # Where a chain's injected `latent_anchor` frames came from ("segment
+        # 1"), so the job log can name the anchor's source (#1181)
+        self.latent_anchor_source = None
         logger.debug(f"Initialized pipeline with device: {self.device}")
 
     @property
@@ -864,6 +877,13 @@ def _with_latent_anchor(self, arguments):
             )
         arguments = dict(arguments)
         arguments[LATENT_ANCHOR_INPUT] = prepared_anchor(where, value, self.base_dir)
+        # The one line that says the anchor applies to this call - without it
+        # the job log shows only a block name (#1181)
+        label = getattr(self, "segment_label", None) or self.name
+        source = latent_anchor_source(
+            getattr(self, "latent_anchor_source", None), value
+        )
+        emit_log(f"{label}: latent anchor applied - latent statistics from {source}")
         return arguments
 
     def _with_mask(self, arguments):
diff --git a/dw/pipeline_processors/progress.py b/dw/pipeline_processors/progress.py
index 7ce12caf..b52b636f 100644
--- a/dw/pipeline_processors/progress.py
+++ b/dw/pipeline_processors/progress.py
@@ -234,6 +234,17 @@ def reporting(self, pipe, state):
         if self is not blocks:
             return original(self, pipe, state)
         for name, block in self.sub_blocks.items():
+            # A dw block that does nothing without one input (the latent
+            # anchor, #1181) is neither named nor run when that input is
+            # absent - the job log of a call that never asked for it says
+            # nothing about it, and the latents are untouched either way
+            idle_without = getattr(block, "idle_without", None)
+            if (
+                idle_without
+                and hasattr(state, "get")
+                and state.get(idle_without) is None
+            ):
+                continue
             run_context.emit("log", message=f"{label}: {name}")
             # A block boundary is a cancellation checkpoint the lead-in
             # otherwise has none of
diff --git a/tests/test_h3_anchor.py b/tests/test_h3_anchor.py
index b07ead85..1fff48cd 100644
--- a/tests/test_h3_anchor.py
+++ b/tests/test_h3_anchor.py
@@ -113,10 +113,27 @@ def test_segments_after_the_first_get_segment_ones_frames(self):
         assert len(first) == 4
         assert first[1].getpixel((0, 0)) == (0, 100, 150)
 
+    def test_segments_after_the_first_name_segment_1_as_the_source(self):
+        # #1181: the job log names where a chain's anchor came from
+        pipeline = FakePipeline(video_output)
+        sources = []
+        run_once = pipeline._run_once
+
+        def recording(arguments):
+            sources.append(pipeline.latent_anchor_source)
+            return run_once(arguments)
+
+        pipeline._run_once = recording
+        run_chain(pipeline, {"segments": 3, "anchor": "segment_1"}, {"prompt": "p"})
+
+        assert sources == [None, "segment 1", "segment 1"]
+        assert pipeline.latent_anchor_source is None
+
     def test_no_anchor_by_default(self):
         pipeline = FakePipeline(video_output)
         run_chain(pipeline, {"segments": 3}, {})
         assert all(LATENT_ANCHOR_INPUT not in call for call in pipeline.calls)
+        assert pipeline.latent_anchor_source is None
 
     def test_an_explicit_none(self):
         pipeline = FakePipeline(video_output)
@@ -416,6 +433,51 @@ def test_frames_are_prepared(self, anchored):
         )
         assert result[LATENT_ANCHOR_INPUT].shape == (5, 4, 6, 3)
 
+    def logged(self, step, arguments):
+        from dw.events import RunContext, activate_context, deactivate_context
+        from dw.pipeline_processors.pipeline import Pipeline
+
+        events = []
+        token = activate_context(RunContext(on_event=events.append))
+        try:
+            Pipeline._with_latent_anchor(step, arguments)
+        finally:
+            deactivate_context(token)
+        return [event["message"] for event in events if event["event"] == "log"]
+
+    def test_no_anchor_logs_nothing(self, anchored):
+        # #1181: a call that never asked for an anchor says nothing about one
+        assert self.logged(self.step(anchored), {"prompt": "x"}) == []
+
+    def test_frames_log_one_line_naming_the_source(self, anchored):
+        (message,) = self.logged(
+            self.step(anchored), {LATENT_ANCHOR_INPUT: self.frames()}
+        )
+        assert message == (
+            "video: latent anchor applied - latent statistics from the frames "
+            "given in the step's arguments"
+        )
+
+    def test_a_chain_segment_logs_its_label_and_segment_1(self, anchored):
+        step = self.step(anchored)
+        step.segment_label = "segment 2/3"
+        step.latent_anchor_source = "segment 1"
+        (message,) = self.logged(step, {LATENT_ANCHOR_INPUT: self.frames()})
+        assert message == (
+            "segment 2/3: latent anchor applied - latent statistics from segment 1"
+        )
+
+    def test_a_reference_is_named(self, anchored, monkeypatch):
+        from dw import argument_media
+
+        monkeypatch.setattr(
+            argument_media, "fetch_video", lambda value, base_dir: self.frames()
+        )
+        (message,) = self.logged(
+            self.step(anchored), {LATENT_ANCHOR_INPUT: "asset:clip.mp4"}
+        )
+        assert message.endswith("latent statistics from 'asset:clip.mp4'")
+
     def test_references_are_refused(self, anchored):
         # With references the call runs ref2va, which has no anchor step: the
         # anchor would otherwise be dropped without a word
diff --git a/tests/test_modular_progress.py b/tests/test_modular_progress.py
index 766d9951..e6a335f7 100644
--- a/tests/test_modular_progress.py
+++ b/tests/test_modular_progress.py
@@ -265,6 +265,79 @@ def record(context, event):
     ]
 
 
+class FakeIdleBlock(FakeNamedBlock):
+    """A dw block that is a pass-through without one input - the latent
+    anchor step's shape (#1181)."""
+
+    idle_without = "latent_anchor"
+
+
+class FakeAnchoredPipeline:
+    """The blocks H3 runs with dw's anchor step after `after_denoise`, over a
+    real PipelineState that does or does not carry `latent_anchor`."""
+
+    def __init__(self, anchor):
+        self.anchor = anchor
+        self.ran = []
+        self.denoise = FakeDenoiseBlock(2)
+        self._blocks = FakeSequentialBlocks(
+            {
+                "denoise.after_denoise": FakeNamedBlock(self.denoise.run),
+                "denoise.dw_latent_anchor": FakeIdleBlock(
+                    lambda: self.ran.append("anchor")
+                ),
+                "decode": FakeNamedBlock(),
+            }
+        )
+
+    @property
+    def blocks(self):
+        return copy.deepcopy(self._blocks)
+
+    def __call__(self, prompt=None, num_inference_steps=None, generator=None):
+        from diffusers.modular_pipelines.modular_pipeline import PipelineState
+
+        state = PipelineState()
+        if self.anchor is not None:
+            state.set("latent_anchor", self.anchor)
+        self._blocks(self, state)
+        return FakeOutput()
+
+
+def test_an_idle_block_is_neither_named_nor_run_without_its_input():
+    """#1181: an H3 call that never asked for a latent anchor logged an
+    anchor phase anyway. Without its input the step is a pass-through, so it
+    is skipped: nothing in the log names it, and the latents are untouched."""
+    pipeline = FakeAnchoredPipeline(anchor=None)
+    events = _events(pipeline)
+
+    assert _logs(events) == [
+        "acme/model: denoise.after_denoise",
+        "acme/model: decode",
+    ]
+    assert pipeline.ran == []
+
+
+def test_an_idle_block_is_named_and_run_when_its_input_is_given():
+    pipeline = FakeAnchoredPipeline(anchor=[object()])
+    events = _events(pipeline)
+
+    assert _logs(events) == [
+        "acme/model: denoise.after_denoise",
+        "acme/model: denoise.dw_latent_anchor",
+        "acme/model: decode",
+    ]
+    assert pipeline.ran == ["anchor"]
+
+
+def test_the_anchor_step_is_idle_without_latent_anchor():
+    """The real step declares the input the narrator checks."""
+    from dw.pipeline_processors.h3_anchor_steps import DwH3LatentAnchorStep
+    from dw.pipeline_processors.h3_rules import LATENT_ANCHOR_INPUT
+
+    assert DwH3LatentAnchorStep.idle_without == LATENT_ANCHOR_INPUT
+
+
 def test_the_block_dispatch_is_handed_back_unpatched():
     """The patch is on the class, so leaving it in place would outlive the
     run and report blocks into whatever context came next."""

From 44f385c80f2052bbb032a2f3d502f2561e317b86 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 16:36:14 -0600
Subject: [PATCH 53/74] feat(h3): #1181 - narrator names the anchor block only
 when set, but always runs it

Arch review: the narrator decided whether the anchor step ran. Now it
only leaves the block unnamed without latent_anchor; the block's own
pass-through stays the one decision. ARCHITECTURE.md's Step progress and
H3 rows record reported_blocks, idle_without and the anchor's source log.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md                      |  4 ++--
 dw/pipeline_processors/h3_anchor_steps.py |  5 +++--
 dw/pipeline_processors/progress.py        | 15 ++++++++-------
 tests/test_modular_progress.py            |  9 +++++----
 4 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index ca836fb3..10844cae 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -30,7 +30,7 @@ one to open.
 | On-demand and offload | `dw/pipeline_processors/placement.py`: `place_component`, `has_component_group_offload` | On-demand residency and `group_offload` cannot both be set on one component, and either one stops the whole pipeline from being moved to the device. | `tests/test_modular_pipeline.py::TestOnDemandResidency::test_group_offload_and_on_demand_together_are_rejected`, `tests/test_modular_pipeline.py::TestOffloadDevice::test_component_group_offload_is_not_moved_to_device` |
 | Quantized component cache | `dw/quantized_cache.py`: `load_components`, `from_pretrained`, `cache_key` | A key that can't pin the weights isn't cached, the cache never fails a job, and the save comes before pruning, so an entry is the whole component. | `tests/test_quantized_cache.py` |
 | LoRAs | `dw/pipeline_processors/adapters.py`: `active_loras`, `load_loras` | A `loras` entry whose `model_name` is null is switched off. | `tests/test_lora_disable.py::TestLoadLoras::test_an_all_null_entry_loads_nothing` |
-| Step progress | `dw/pipeline_processors/progress.py`: `reported_progress_bars` | A pipeline with no step callback reports its denoise steps through its progress bars. | `tests/test_modular_progress.py::test_every_advance_of_the_bar_is_reported` |
+| Step progress | `dw/pipeline_processors/progress.py`: `reported_progress_bars`, `reported_blocks` | A pipeline with no step callback reports its denoise steps through its progress bars. `reported_blocks` names each top-level block of a sequential modular graph in a `log` event as it begins; a dw block that declares `idle_without = <input>` is left unnamed when that input is absent from the state, and is still run - whether a block acts is the block's own decision, never the narrator's. | `tests/test_modular_progress.py::test_every_advance_of_the_bar_is_reported`, `tests/test_modular_progress.py::test_an_idle_block_is_unnamed_but_still_run_without_its_input`, `tests/test_modular_progress.py::test_an_idle_block_is_named_and_run_when_its_input_is_given` |
 | Step timing | `dw/pipeline_processors/timing.py`: `StepTimer`, `step_timer`, `timed_step`, `mark` | Only a pipeline step that ran is timed, on the worker's monotonic clock: `step_timer` opens a `StepTimer` for a step with a `pipeline` or `pipeline_reference` and None for any other, which also hides an outer step's timer from a composed child. Load, call, denoise start, each pass and save reach the open timer through `mark`, and nowhere else. Denoise start is never taken inside a compiled region: a whole-compiled denoiser (`_compiled_call_impl` set) has that callable wrapped for the call instead of a forward pre-hook, which `fullgraph: true` cannot trace. A span with a missing mark is null, never guessed. `record_step` puts the reading on the step's manifest entry and `step_end`, and a reused step gets no `timing`. A chain sums its pipeline calls into one `timing` with `segments: N`. `ManifestEntry.timing` declares the shape (`docs/WORKSPACES.md` *Step timing*). | `tests/test_step_timing.py::test_a_missing_denoise_start_nulls_encode_and_first_pass`, `tests/test_step_timing.py::test_a_reused_step_carries_no_timing_even_when_one_is_passed`, `tests/test_step_timing.py::test_a_chain_sums_its_segments_and_takes_one_median_over_both`, `tests/test_step_timing.py::test_a_task_step_run_through_the_step_loop_has_no_timing`, `tests/test_step_timing.py::test_the_denoise_hook_marks_the_first_forward_once_and_removes_itself`, `tests/test_step_timing.py::test_a_fullgraph_compiled_denoiser_runs_and_still_gets_the_start_mark` |
 | Step-cache skip counts | `dw/pipeline_processors/timing.py`: `CacheCounter`, `counting_forwards`, `report_cache_stats`, `StepTimer.cache_counted`; `dw/pipeline_processors/components.py`: `stateful_cache_context`; `dw/pipeline_processors/mag_calibration.py`: `get_cache_transformer`, `captured_mag_calibration` | For `first_block` and `mag`, a forward hook on the cache transformer counts computed or skipped once per forward, reading the leader hook's `should_compute` inside the cond/uncond `cache_context` the pipeline opened, so classifier-free guidance counts both forwards. `stateful_cache_context` registers the hook for one pipeline call; its `finally` emits `cache_stats` before `_reset_stateful_cache()` and removes the hook after the event. Private diffusers state that has moved gives null counts and one `cache_stats_unavailable` warning, and never fails the run; `taylorseer`, `faster` and `text_kv` report type and config only, with no warning. The denoiser a cache attaches to (`get_cache_transformer`) and the MagCache calibration capture (`captured_mag_calibration`, the end-of-run log record turned into `cache_stats`-side job events, #891/#927) live in `mag_calibration.py`, which `components.py` imports and re-exports for `pipeline.py`, never the reverse. The counts reach the step's `timing.cache` only through `StepTimer.cache_counted`; a chain sums its calls' counts, and a null in any call nulls the sum. A step with no `cache` has no `cache` key and no event (`docs/ACCELERATION.md` *Reading the skip counts*). | `tests/test_cache_stats.py::test_cfg_counts_two_forwards_per_step_and_they_add_up`, `tests/test_cache_stats.py::test_the_event_names_the_step_and_segment_and_goes_out_before_the_reset`, `tests/test_cache_stats.py::test_moved_hook_state_nulls_the_counts_warns_once_and_the_run_finishes`, `tests/test_cache_stats.py::test_the_private_diffusers_names_are_where_the_counter_reads_them`, `tests/test_cache_stats.py::test_the_timer_sums_counts_and_nulls_on_any_null`, `tests/test_cache_stats.py::test_types_without_a_decision_report_type_only_and_no_warning` |
 | Vendored model ports | `dw/vendored/` (`audio8_tts_model.py`, `mmaudio_model.py`, `mmaudio_sync_model.py`, `mmaudio_clip.py`) | Upstream model code dw carries is one module per port, pinned to the commit or Hub revision its docstring names, so it can be diffed against its source and re-pinned as a unit; a port reaches dw only for the validated media loaders and is imported only by the task that runs it. Like `dw/community_pipelines/`, the package is outside the architecture ratchet (`EXCLUDED` in `scripts/arch_metrics.py`): a port keeps upstream's shape, which the module and function ceilings are not written for. | `tests/test_arch_metrics.py::test_vendored_model_ports_are_not_counted`, `tests/test_mmaudio_model.py::test_modules_are_imported_only_by_the_sound_effects_task`, `tests/test_mmaudio_sync_model.py::test_module_is_imported_only_by_the_sound_effects_task` |
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` adds E(noisy) - E(clean) to each video guide's clean encode when `guide_noise` is set, on `h3_rules.guide_noise_taper`, and encodes once without it; `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
+| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`, decided by the block itself; it declares `idle_without = "latent_anchor"` so the step-progress narrator leaves it unnamed then; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`, and `run_chain` sets `pipeline.latent_anchor_source` to `segment 1` for those segments only; `Pipeline._with_latent_anchor` emits one `log` line per anchored call naming that source, the quoted reference, or the step's frames; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` adds E(noisy) - E(clean) to each video guide's clean encode when `guide_noise` is set, on `h3_rules.guide_noise_taper`, and encodes once without it; `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_no_anchor_logs_nothing`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_a_chain_segment_logs_its_label_and_segment_1`, `tests/test_modular_progress.py::test_the_anchor_step_is_idle_without_latent_anchor`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
diff --git a/dw/pipeline_processors/h3_anchor_steps.py b/dw/pipeline_processors/h3_anchor_steps.py
index f14bf679..c45ed041 100644
--- a/dw/pipeline_processors/h3_anchor_steps.py
+++ b/dw/pipeline_processors/h3_anchor_steps.py
@@ -47,8 +47,9 @@ def anchor_statistics(components, frames, height, width):
 
 class DwH3LatentAnchorStep(ModularPipelineBlocks):
     model_name = "minimax-h3"
-    # Without this input the step is a pass-through, so the block narrator
-    # skips it - no anchor phase in a log that never asked for one (#1181)
+    # Without this input the step is a pass-through (its own decision,
+    # below), so the block narrator leaves it unnamed - no anchor phase in a
+    # log that never asked for one (#1181). The narrator still runs it
     idle_without = LATENT_ANCHOR_INPUT
 
     @property
diff --git a/dw/pipeline_processors/progress.py b/dw/pipeline_processors/progress.py
index b52b636f..39967707 100644
--- a/dw/pipeline_processors/progress.py
+++ b/dw/pipeline_processors/progress.py
@@ -235,17 +235,18 @@ def reporting(self, pipe, state):
             return original(self, pipe, state)
         for name, block in self.sub_blocks.items():
             # A dw block that does nothing without one input (the latent
-            # anchor, #1181) is neither named nor run when that input is
-            # absent - the job log of a call that never asked for it says
-            # nothing about it, and the latents are untouched either way
+            # anchor, #1181) is not named when that input is absent - the
+            # job log of a call that never asked for it says nothing about
+            # it. It still runs: whether it acts is the block's own call
+            # (a pass-through without its input), not the narrator's
             idle_without = getattr(block, "idle_without", None)
-            if (
+            unnamed = (
                 idle_without
                 and hasattr(state, "get")
                 and state.get(idle_without) is None
-            ):
-                continue
-            run_context.emit("log", message=f"{label}: {name}")
+            )
+            if not unnamed:
+                run_context.emit("log", message=f"{label}: {name}")
             # A block boundary is a cancellation checkpoint the lead-in
             # otherwise has none of
             run_context.check_cancelled()
diff --git a/tests/test_modular_progress.py b/tests/test_modular_progress.py
index e6a335f7..57a11de9 100644
--- a/tests/test_modular_progress.py
+++ b/tests/test_modular_progress.py
@@ -304,10 +304,11 @@ def __call__(self, prompt=None, num_inference_steps=None, generator=None):
         return FakeOutput()
 
 
-def test_an_idle_block_is_neither_named_nor_run_without_its_input():
+def test_an_idle_block_is_unnamed_but_still_run_without_its_input():
     """#1181: an H3 call that never asked for a latent anchor logged an
-    anchor phase anyway. Without its input the step is a pass-through, so it
-    is skipped: nothing in the log names it, and the latents are untouched."""
+    anchor phase anyway. Without its input nothing in the log names it - but
+    the narrator still calls it: whether the block acts is the block's own
+    decision (a pass-through there), not the narrator's (arch review)."""
     pipeline = FakeAnchoredPipeline(anchor=None)
     events = _events(pipeline)
 
@@ -315,7 +316,7 @@ def test_an_idle_block_is_neither_named_nor_run_without_its_input():
         "acme/model: denoise.after_denoise",
         "acme/model: decode",
     ]
-    assert pipeline.ran == []
+    assert pipeline.ran == ["anchor"]
 
 
 def test_an_idle_block_is_named_and_run_when_its_input_is_given():

From 576f139612b35533eb867eca0e3d197c281628ef Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 17:20:59 -0600
Subject: [PATCH 54/74] feat(docs): #1184 - record H3 dialogue pacing probes
 (split, split+action, control)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../2026-11-h3-dialogue-pacing-probes.md      | 146 ++++++++++++++++++
 1 file changed, 146 insertions(+)
 create mode 100644 docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md

diff --git a/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md b/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
new file mode 100644
index 00000000..f7e69a18
--- /dev/null
+++ b/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
@@ -0,0 +1,146 @@
+# MiniMax-H3 dialogue pacing: record-only probes (#1180 stage A, #1184)
+
+Probe date: 2026-11-02. Server `lem`, develop @ 05ffa7ea, diffusers 0.42.1.
+Agent: claude-opus-5-5 (anthropic). Workspace `qa-h3-dialogue-pacing`.
+No engine or skill file was changed. This document only records results.
+
+Every probe ran on H3 T2VA at 960x544, in the skill's dialogue configuration
+(`minimax-h3` *Hard rules*: dialogue shots drop the turbo LoRA):
+- int4 sdnq (`weights_dtype` `{int4}`), `lora_model_name: null`, no turbo LoRA;
+- 12 steps, video shift 12 and audio shift 3;
+- sparse attention (tau 0.5, top-k 0.15), no cache;
+- 124 frames (5.167 s at 24 fps).
+
+**Arm A is a non-turbo re-run of row 1's r150 line** (2026-10 probes, *Probe 1*,
+job be681f38d5a8). It is not the same configuration: row 1 ran turbo at 9 steps.
+Its numbers are this run's control, not row 1's.
+
+The three arms, one prompt each, identical except for the `<d>` blocks:
+
+- **A (control)**: `<d>[English] I left the light on for you, Daniel.</d>`
+- **B (split)**: `<d>[English] I left the light on.</d> <d>[English] For you, Daniel.</d>`
+- **C (split + action)**: `<d>[English] I left the light on.</d> He sniffs and looks down at the table. <d>[English] For you, Daniel.</d>`
+
+The shared text around them: `Task: T2VA. Duration: 5.17 seconds. One man sits at a
+kitchen table, facing the camera, in warm evening light. He speaks to the camera in a
+mid-pitched baritone voice, a neutral American accent and a natural speaking rate:
+<d>…</d> overall_soundscape: a quiet kitchen, a faint refrigerator hum.
+non_diegetic_music: N/A`
+
+Each arm ran on seeds 1001 and 2002, all three arms in one job per seed.
+
+**The bar** (plan v2, stage A):
+- "Split paces the line" is **confirmed** when both B seeds have a lead under
+  1.0 s and a delivered rate ≤ 4.5 words/s, and neither A seed does.
+- "An action between segments paces it" is **confirmed** by the same test on C
+  against A, plus the action rendered at its break on both C seeds.
+- "The action prose is spoken" is a **warning** if it shows on even one seed.
+- If A shows no lead-in, B and C are inconclusive.
+
+Delivered rate is the 2026-10 definition: words divided by the time from the
+first word's start to the last word's end. Per-segment rates are given too.
+Tail is 5.167 s minus the end of "Daniel."
+
+## Verdicts
+
+| # | Rule | Verdict | Jobs | Observation |
+| --- | --- | --- | --- | --- |
+| 1 | "Split paces the line." | **confirmed** by the bar, with a caveat | 150751a7522c, 900ea1581d22 | A has a lead-in on both seeds (2.14 s, 2.72 s) and is spoken at 4.71 and 5.41 words/s, so it fails the bar. B leads at 0.70 s and 0.64 s, with delivered rates of 2.23 and 2.26 words/s, so it passes. **Caveat:** the split doesn't slow the words. Segment 1 is still spoken at 6.25 and 6.6 words/s. The rate falls because H3 puts a 1.9 s silent gap between the segments, not because the speech is slower. See *Arm B*. |
+| 2 | "An action between segments paces it." | **refuted** (the action condition fails) | 150751a7522c, 900ea1581d22 | Lead and rate pass on both C seeds (lead 0.44 s and 0.66 s, delivered 1.93 and 2.16 words/s), and the break is a 2.1–2.5 s silence. But the action doesn't render at its break on either seed. The mouth stays closed through the break and no sniff is visible. The look-down comes after segment 2 has started, at about 4.0–5.0 s on s1001 and 4.5–5.0 s on s2002. See *Arm C*. |
+| 3 | "The action prose is spoken." | **no**: 0 of 6 clips | 150751a7522c, 900ea1581d22 | None of "sniffs", "looks", "down" or "table" appears in any transcript, and none of the arms' words was added or dropped. No warning. |
+
+## Measurements
+
+Times are in seconds from clip start. **Lead** is the onset of the first
+spoken word. Onsets and gaps come from the RMS envelope, which agrees with the
+Whisper word timestamps within 0.1 s at each onset (see *Method notes*).
+Transcripts are `H3DialoguePacingProbe-words_{a.3,b.4,c.5}-0.json` in each run.
+
+| Arm | Seed | Lead | Gap at the break | Seg 1 w/s | Seg 2 w/s | Delivered w/s | Tail | Action word | Mouth moves in silence | Action at its break |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| A | 1001 | 2.14 | n/a (no break) | n/a | n/a | 4.71 (8 / 1.70) | 1.33 | none | **possibly**: lips parted or open at 0.0–1.5 s in the silent lead-in | n/a |
+| A | 2002 | 2.72 | n/a | n/a | n/a | 5.41 (8 / 1.48) | 0.97 | none | no: closed or neutral at 0.0–2.5 s | n/a |
+| B | 1001 | 0.70 | about 1.9 (1.6–3.5) | 6.25 | about 4.1 | 2.23 | 0.89 | none | no: closed or neutral through the gap | n/a |
+| B | 2002 | 0.64 | about 1.9 (1.5–3.4) | 6.6 | about 3.8 | 2.26 | 0.99 | none | no: closed through the gap | n/a |
+| C | 1001 | 0.44 | about 2.5 (1.3–3.8) | 6.25 | about 3.8 | 1.93 | 0.59 | none | no: closed through the break | **no**: no sniff; head and gaze drop at 4.0–5.0 s, after the break |
+| C | 2002 | 0.66 | about 2.1 (1.5–3.6) | 5.8 | 4.05 | 2.16 | 0.81 | none | no: closed or smiling through the break | **no**: head down at 4.5–5.0 s, after the break |
+
+### Arm A: the control
+
+The lead-in that row 1 found under turbo holds without it. Both seeds open on
+2.1–2.7 s of silence and then speak the 8-word line in about 1.5–1.7 s. The
+tails (0.97–1.33 s) are longer than row 1's turbo tails. On s1001 the lips
+are parted, and may move, in the silent lead-in. At 0.5 s sampling that is
+ambiguous: it could be a held open mouth rather than speech-like movement.
+On s2002 the mouth is closed until speech starts.
+
+### Arm B: the split
+
+Both seeds start segment 1 within 0.7 s, speak it fast (about 6.3–6.6 words/s),
+leave a silent gap of about 1.9 s, and speak segment 2 more slowly (about
+3.8–4.1 words/s). The mouth is closed through the gap on both seeds. The
+pacing the split buys is the gap and a slower second segment. The first
+segment stays at the fast rate A uses for the whole line.
+
+### Arm C: the split and an action
+
+The timing matches B: an early lead, a fast first segment, a silent break of
+2.1–2.5 s and a slower second segment. The action prose doesn't fill the break
+on screen. On both seeds the man sits still with his mouth closed through
+it. The look-down renders late, overlapping or following segment 2. On
+s1001, a quiet sound at 2.4–2.9 s (−51 to −58 dBFS, against speech at about
+−40 dBFS) sits in the break. It could be a breath or a sniff, but nothing
+visible goes with it, and it isn't transcribed. So C passes the lead and rate
+test but fails the "action rendered at its break" condition on both seeds.
+
+### What this says about the rules
+
+- A split into two `<d>` blocks removes the silent lead-in on both seeds and
+  puts a pause between the blocks. By the bar's delivered-rate definition, that
+  paces the line. It doesn't slow the first block's words.
+- Action prose between the blocks gives no pacing beyond what the split already
+  gives, and H3 doesn't stage the action in the break. Here it rendered late.
+- Action prose between `<d>` blocks isn't spoken (0 of 6 clips, counting A and
+  B, which have none, as controls).
+
+## For Don: listening
+
+No "more natural" claim is made here. That is a listening verdict, and it's
+Don's. The clips to compare, A against C:
+
+| Seed | Job | Run | A (control) | C (split + action) |
+| --- | --- | --- | --- | --- |
+| 1001 | 150751a7522c | 20261102-230056-b618eb3c | `final/a_control-0.0.mp4` | `final/c_split_action-0.0.mp4` |
+| 2002 | 900ea1581d22 | 20261102-230859-f997d065 | `final/a_control-0.0.mp4` | `final/c_split_action-0.0.mp4` |
+
+B is in the same runs, as `final/b_split-0.0.mp4`.
+
+## Not run
+
+None. All three arms ran on both seeds.
+
+## Method notes and deviations
+
+- **Jobs.** 150751a7522c (seed 1001, 443 s) and 900ea1581d22 (seed 2002,
+  435 s). Both ran on cuda:0 (RTX 3090) and succeeded. Workflow id
+  `H3DialoguePacingProbe`, recoverable with `get_job_workflow`.
+- **The row-1 workspace is gone.** `qa-h3-dialogue-probes` no longer exists,
+  and `get_job_workflow be681f38d5a8` returns no definition. So arm A's prompt
+  was rebuilt from the 2026-10 record's description of the r150 line. It is
+  not a byte-for-byte copy of row 1's prompt.
+- **Batching.** The plan names `qa/h3-probe-batch`. Instead, the arms ran as an
+  equivalent inline workflow: `templates/minimax/shots-batch`'s pipeline
+  configuration without the LoRA, one `shot` step over the three arms
+  (`release_pipeline: true`), then one `transcribe_audio` step per arm
+  (`words_a`, `words_b`, `words_c`, `timestamps: "word"`). The generation
+  arguments are the ones listed at the top of this document.
+- **Whisper "silent" suspects.** The clips are quiet (mean −40 to −43 dBFS),
+  and Whisper flagged most words `suspect: "silent"`. The envelope shows speech
+  at each flagged word, so the words were kept. Lead and tail can be re-read
+  from each transcript's first-word start and last-word end within 0.1 s.
+- **Long "for" chunks.** In B, Whisper stretches the word "for" across the
+  gap (1.50–3.68 s on s1001, 1.40–3.52 s on s2002). The segment-2 onset and
+  the gap are taken from the envelope, where the silence is unambiguous.
+  That is why the segment-2 rates are marked "about".
+- **Frames.** Sampled at 0.5 s steps (`get_output_frames`), so mouth and action
+  judgements are coarse. A movement shorter than 0.5 s could be missed.

From 340a8984ff0dcad15d9422688a619c59b89ff0e5 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 17:26:02 -0600
Subject: [PATCH 55/74] docs(proposals): #1166 - record fix-forward #1181 in
 the design record

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../complete/h3-guide-chain-drift-complete.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/docs/proposals/complete/h3-guide-chain-drift-complete.md b/docs/proposals/complete/h3-guide-chain-drift-complete.md
index eafe6104..6f1b7ae8 100644
--- a/docs/proposals/complete/h3-guide-chain-drift-complete.md
+++ b/docs/proposals/complete/h3-guide-chain-drift-complete.md
@@ -152,6 +152,24 @@ segment 1 (about 22 dB). Neither arm changes segment 1 (diagnostics
 long-lived cuda:0 worker, and the cause wasn't found. The three arms were re-run
 with `rerun_job` and matched A exactly. Case verified: C-F681.
 
+### Fix-forward #1181: latent anchor observability
+
+The feature's final check failed C-F673 bullet 4. Every H3 call logged a
+`denoise.dw_latent_anchor` phase, unanchored calls and segment 1 too, and no
+event said when an anchor was applied or to which segment. So the events
+couldn't tell an anchored chain from an unanchored one. This was a stage-1 build
+miss, not a plan gap, so there was no re-plan.
+
+- **Built:** the anchor step logs when it applies an anchor, naming its source.
+  When `latent_anchor` is unset, the step still runs but the progress narrator
+  (`progress.py` `reported_blocks`) emits no line naming it. Shipped at
+  `44f385c8` (merged `05ffa7ea`).
+- **Bounces: 1** (architecture). The first build (`b5cf0fae`) was bounced
+  for giving the H3 latent anchor a second owner outside its map row's
+  modules. The rebuild keeps the step always running and has the narrator
+  decide only whether to log it.
+  Case verified: C-F673, all four bullets.
+
 ## Cost and bounces
 
 | Stage | Bounces | Cost |
@@ -159,6 +177,7 @@ with `rerun_job` and matched A exactly. Case verified: C-F681.
 | #1173 | 3 (architecture, C-F671 1c, SE-F061) | Not recorded on the stage |
 | #1174 | 0 | Not recorded on the stage |
 | #1175 | 1 (C-F681, arc segment 1) | Not recorded on the stage. Lem time ran over the plan's 3 h by the three arc re-runs and their diagnostics |
+| #1181 (fix-forward, C-F673 bullet 4) | 1 (architecture, second owner) | Not recorded on the stage |
 
 ## Deferred and open
 

From be098c7c2ccc01cefab877792f6c104876896da9 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 17:46:38 -0600
Subject: [PATCH 56/74] docs(proposals): #1184 - per-clip suspect handling and
 the judged rate in the pacing audit

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../2026-11-h3-dialogue-pacing-probes.md      | 49 +++++++++++++------
 1 file changed, 34 insertions(+), 15 deletions(-)

diff --git a/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md b/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
index f7e69a18..d1681a6c 100644
--- a/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
+++ b/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
@@ -41,6 +41,14 @@ Delivered rate is the 2026-10 definition: words divided by the time from the
 first word's start to the last word's end. Per-segment rates are given too.
 Tail is 5.167 s minus the end of "Daniel."
 
+**Which rate the verdicts use.** Every arm is judged by the delivered
+(whole-line) rate, which is the rate the plan's bar names. The per-segment
+rates are reported but not judged. By the per-segment rate, segment 1 runs at
+5.8–6.6 words/s in every B and C clip. So on that reading both B seeds and
+both C seeds fail the 4.5 words/s bar, and "split paces the line" would be
+**refuted**. The verdict in row 1 below holds only because the bar measures
+the delivered rate.
+
 ## Verdicts
 
 | # | Rule | Verdict | Jobs | Observation |
@@ -52,18 +60,26 @@ Tail is 5.167 s minus the end of "Daniel."
 ## Measurements
 
 Times are in seconds from clip start. **Lead** is the onset of the first
-spoken word. Onsets and gaps come from the RMS envelope, which agrees with the
-Whisper word timestamps within 0.1 s at each onset (see *Method notes*).
-Transcripts are `H3DialoguePacingProbe-words_{a.3,b.4,c.5}-0.json` in each run.
-
-| Arm | Seed | Lead | Gap at the break | Seg 1 w/s | Seg 2 w/s | Delivered w/s | Tail | Action word | Mouth moves in silence | Action at its break |
-| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| A | 1001 | 2.14 | n/a (no break) | n/a | n/a | 4.71 (8 / 1.70) | 1.33 | none | **possibly**: lips parted or open at 0.0–1.5 s in the silent lead-in | n/a |
-| A | 2002 | 2.72 | n/a | n/a | n/a | 5.41 (8 / 1.48) | 0.97 | none | no: closed or neutral at 0.0–2.5 s | n/a |
-| B | 1001 | 0.70 | about 1.9 (1.6–3.5) | 6.25 | about 4.1 | 2.23 | 0.89 | none | no: closed or neutral through the gap | n/a |
-| B | 2002 | 0.64 | about 1.9 (1.5–3.4) | 6.6 | about 3.8 | 2.26 | 0.99 | none | no: closed through the gap | n/a |
-| C | 1001 | 0.44 | about 2.5 (1.3–3.8) | 6.25 | about 3.8 | 1.93 | 0.59 | none | no: closed through the break | **no**: no sniff; head and gaze drop at 4.0–5.0 s, after the break |
-| C | 2002 | 0.66 | about 2.1 (1.5–3.6) | 5.8 | 4.05 | 2.16 | 0.81 | none | no: closed or smiling through the break | **no**: head down at 4.5–5.0 s, after the break |
+spoken word. The envelope agrees with the Whisper word timestamps within 0.1 s
+at the lead and the tail. It doesn't agree at segment 2's onset wherever
+Whisper stretches "for" across the gap (see *Method notes*).
+
+Every clip's transcript output is `words_{a,b,c}` in the job named, i.e.
+`H3DialoguePacingProbe/<run>/final/H3DialoguePacingProbe-words_{a.3,b.4,c.5}-0.json`.
+There is no stray early word in any clip, so each lead has one reading.
+
+| Arm | Seed | Job | Lead | Gap at the break | Seg 1 w/s | Seg 2 w/s | Delivered w/s | Tail | Action word | `suspect` chunks, and how counted | Mouth moves in silence | Action at its break |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| A | 1001 | 150751a7522c | 2.14 | n/a (no break) | n/a | n/a | 4.71 (8 / 1.70) | 1.33 | none | all 8 `silent`; counted as speech (envelope shows speech at each) | **possibly**: lips parted or open at 0.0–1.5 s in the silent lead-in | n/a |
+| A | 2002 | 900ea1581d22 | 2.72 | n/a | n/a | n/a | 5.41 (8 / 1.48) | 0.97 | none | all 8 `silent`; counted as speech (envelope) | no: closed or neutral at 0.0–2.5 s | n/a |
+| B | 1001 | 150751a7522c | 0.70 | about 1.9 (1.6–3.5, envelope) | 6.25 | about 4.1 | 2.23 | 0.89 | none | "for" `long` (1.50–3.68); counted as speech, but its onset is taken from the envelope (about 3.5), not Whisper | no: closed or neutral through the gap | n/a |
+| B | 2002 | 900ea1581d22 | 0.64 | about 1.9 (1.5–3.4, envelope) | 6.6 | about 3.8 | 2.26 | 0.99 | none | all 8 `silent`; counted as speech (envelope). "for" also stretched 1.40–3.52, onset from the envelope | no: closed through the gap | n/a |
+| C | 1001 | 150751a7522c | 0.44 | about 2.5 (1.3–3.8, envelope) | 6.25 | about 3.8 | 1.93 | 0.59 | none | all 8 `silent`; counted as speech (envelope). "for" stretched 2.50–3.96, onset from the envelope | no: closed through the break | **no**: no sniff; head and gaze drop at 4.0–5.0 s, after the break |
+| C | 2002 | 900ea1581d22 | 0.66 | about 2.1 (1.5–3.6, envelope) | 5.8 | 4.05 | 2.16 | 0.81 | none | all 8 `silent`; counted as speech (envelope) | no: closed or smiling through the break | **no**: head down at 4.5–5.0 s, after the break |
+
+Lead and tail are read from the transcript (the first chunk's `start`, and
+5.167 s minus the last chunk's `end`), `suspect` chunks included. The gap and
+the segment-2 onset come from the RMS envelope wherever Whisper stretches "for".
 
 ### Arm A: the control
 
@@ -134,9 +150,12 @@ None. All three arms ran on both seeds.
   (`release_pipeline: true`), then one `transcribe_audio` step per arm
   (`words_a`, `words_b`, `words_c`, `timestamps: "word"`). The generation
   arguments are the ones listed at the top of this document.
-- **Whisper "silent" suspects.** The clips are quiet (mean −40 to −43 dBFS),
-  and Whisper flagged most words `suspect: "silent"`. The envelope shows speech
-  at each flagged word, so the words were kept. Lead and tail can be re-read
+- **Whisper "silent" suspects.** The clips are quiet (mean −40 to −43 dBFS,
+  peaks −18 to −23), and the job warnings call each clip "quiet overall".
+  In 5 of the 6 transcripts every word is flagged `suspect: "silent"`. The
+  exception is B s1001, where only "for" is flagged (`long`). The envelope
+  shows speech at each flagged word, so every `silent` chunk is counted as
+  speech. The per-clip table says so row by row. Lead and tail can be re-read
   from each transcript's first-word start and last-word end within 0.1 s.
 - **Long "for" chunks.** In B, Whisper stretches the word "for" across the
   gap (1.50–3.68 s on s1001, 1.40–3.52 s on s2002). The segment-2 onset and

From 6af849096ffd7f2211d35926be4c1751adfb03c8 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 18:18:30 -0600
Subject: [PATCH 57/74] docs(proposals): #1184 - C s1001 action placement is a
 borderline yes; C verdict reads 1 of 2 seeds

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../2026-11-h3-dialogue-pacing-probes.md      | 21 ++++++++++++-------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md b/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
index d1681a6c..9a77ea08 100644
--- a/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
+++ b/docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md
@@ -54,7 +54,7 @@ the delivered rate.
 | # | Rule | Verdict | Jobs | Observation |
 | --- | --- | --- | --- | --- |
 | 1 | "Split paces the line." | **confirmed** by the bar, with a caveat | 150751a7522c, 900ea1581d22 | A has a lead-in on both seeds (2.14 s, 2.72 s) and is spoken at 4.71 and 5.41 words/s, so it fails the bar. B leads at 0.70 s and 0.64 s, with delivered rates of 2.23 and 2.26 words/s, so it passes. **Caveat:** the split doesn't slow the words. Segment 1 is still spoken at 6.25 and 6.6 words/s. The rate falls because H3 puts a 1.9 s silent gap between the segments, not because the speech is slower. See *Arm B*. |
-| 2 | "An action between segments paces it." | **refuted** (the action condition fails) | 150751a7522c, 900ea1581d22 | Lead and rate pass on both C seeds (lead 0.44 s and 0.66 s, delivered 1.93 and 2.16 words/s), and the break is a 2.1–2.5 s silence. But the action doesn't render at its break on either seed. The mouth stays closed through the break and no sniff is visible. The look-down comes after segment 2 has started, at about 4.0–5.0 s on s1001 and 4.5–5.0 s on s2002. See *Arm C*. |
+| 2 | "An action between segments paces it." | **refuted** (the action condition fails) | 150751a7522c, 900ea1581d22 | Lead and rate pass on both C seeds (lead 0.44 s and 0.66 s, delivered 1.93 and 2.16 words/s), and the break is a 2.1–2.5 s silence. But the action landed at the break on 1 of 2 seeds, at the window edge, and the bar needs both. On s1001 the look-down begins by 4.0 s, 0.2 s after the gap's envelope end (3.8 s), inside the ±0.5 s window (borderline). On s2002 the head is level at 4.0 s and down at 4.5 s, after the window closes at 4.1 s (no). On neither seed does the man move during the break itself: the mouth stays closed and no sniff is visible. See *Arm C*. |
 | 3 | "The action prose is spoken." | **no**: 0 of 6 clips | 150751a7522c, 900ea1581d22 | None of "sniffs", "looks", "down" or "table" appears in any transcript, and none of the arms' words was added or dropped. No warning. |
 
 ## Measurements
@@ -74,8 +74,8 @@ There is no stray early word in any clip, so each lead has one reading.
 | A | 2002 | 900ea1581d22 | 2.72 | n/a | n/a | n/a | 5.41 (8 / 1.48) | 0.97 | none | all 8 `silent`; counted as speech (envelope) | no: closed or neutral at 0.0–2.5 s | n/a |
 | B | 1001 | 150751a7522c | 0.70 | about 1.9 (1.6–3.5, envelope) | 6.25 | about 4.1 | 2.23 | 0.89 | none | "for" `long` (1.50–3.68); counted as speech, but its onset is taken from the envelope (about 3.5), not Whisper | no: closed or neutral through the gap | n/a |
 | B | 2002 | 900ea1581d22 | 0.64 | about 1.9 (1.5–3.4, envelope) | 6.6 | about 3.8 | 2.26 | 0.99 | none | all 8 `silent`; counted as speech (envelope). "for" also stretched 1.40–3.52, onset from the envelope | no: closed through the gap | n/a |
-| C | 1001 | 150751a7522c | 0.44 | about 2.5 (1.3–3.8, envelope) | 6.25 | about 3.8 | 1.93 | 0.59 | none | all 8 `silent`; counted as speech (envelope). "for" stretched 2.50–3.96, onset from the envelope | no: closed through the break | **no**: no sniff; head and gaze drop at 4.0–5.0 s, after the break |
-| C | 2002 | 900ea1581d22 | 0.66 | about 2.1 (1.5–3.6, envelope) | 5.8 | 4.05 | 2.16 | 0.81 | none | all 8 `silent`; counted as speech (envelope) | no: closed or smiling through the break | **no**: head down at 4.5–5.0 s, after the break |
+| C | 1001 | 150751a7522c | 0.44 | about 2.5 (1.3–3.8, envelope) | 6.25 | about 3.8 | 1.93 | 0.59 | none | all 8 `silent`; counted as speech (envelope). "for" stretched 2.50–3.96, onset from the envelope | no: closed through the break | **yes (borderline)**: no visible sniff; the look-down begins by 4.0 s (frame 96; level at 3.5 s, fully down 4.5–5.12 s), 0.2 s after the gap's envelope end at 3.8 s, inside the ±0.5 s window at its edge |
+| C | 2002 | 900ea1581d22 | 0.66 | about 2.1 (1.5–3.6, envelope) | 5.8 | 4.05 | 2.16 | 0.81 | none | all 8 `silent`; counted as speech (envelope) | no: closed or smiling through the break | **no**: head level at 4.0 s, down at 4.5–5.0 s, after the ±0.5 s window closes at 4.1 s |
 
 Lead and tail are read from the transcript (the first chunk's `start`, and
 5.167 s minus the last chunk's `end`), `suspect` chunks included. The gap and
@@ -103,11 +103,15 @@ segment stays at the fast rate A uses for the whole line.
 The timing matches B: an early lead, a fast first segment, a silent break of
 2.1–2.5 s and a slower second segment. The action prose doesn't fill the break
 on screen. On both seeds the man sits still with his mouth closed through
-it. The look-down renders late, overlapping or following segment 2. On
-s1001, a quiet sound at 2.4–2.9 s (−51 to −58 dBFS, against speech at about
+it. The look-down comes as segment 2 starts. On s1001 it begins by 4.0 s,
+0.2 s after the gap ends, which is inside the ±0.5 s window at its edge, so
+that cell is a borderline yes. On s2002 it comes at 4.5 s, after the window.
+On s1001, a quiet sound at 2.4–2.9 s (−51 to −58 dBFS, against speech at about
 −40 dBFS) sits in the break. It could be a breath or a sniff, but nothing
-visible goes with it, and it isn't transcribed. So C passes the lead and rate
-test but fails the "action rendered at its break" condition on both seeds.
+visible goes with it, and it isn't transcribed. Don may want to listen for it
+at about 2.5 s in `150751a7522c` `final/c_split_action-0.0.mp4`. So C passes
+the lead and rate test but meets the "action rendered at its break" condition
+on 1 of 2 seeds (s1001, borderline), and the bar needs both.
 
 ### What this says about the rules
 
@@ -115,7 +119,8 @@ test but fails the "action rendered at its break" condition on both seeds.
   puts a pause between the blocks. By the bar's delivered-rate definition, that
   paces the line. It doesn't slow the first block's words.
 - Action prose between the blocks gives no pacing beyond what the split already
-  gives, and H3 doesn't stage the action in the break. Here it rendered late.
+  gives, and H3 doesn't stage the action in the break. Here it rendered as
+  segment 2 began: at the window's edge on one seed, after it on the other.
 - Action prose between `<d>` blocks isn't spoken (0 of 6 clips, counting A and
   B, which have none, as controls).
 

From b32a1c1401bf7a05bcd812b4056cd59df3165e87 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 18:31:13 -0600
Subject: [PATCH 58/74] feat(plugin): #1185 - minimax-h3: splitting a short
 line into two <d> blocks removes the lead-in; action prose between blocks is
 not a pacing rule

From the stage A record (docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md,
pacing rows 1-3). check-script line in SKILL.md: pass each <d> block's contents,
never the prose between blocks.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 plugins/dw/skills/minimax-h3/SKILL.md         |  5 ++--
 .../skills/minimax-h3/references/dialogue.md  | 27 +++++++++++++++++--
 2 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/plugins/dw/skills/minimax-h3/SKILL.md b/plugins/dw/skills/minimax-h3/SKILL.md
index 841c4bc4..c54bb8ff 100644
--- a/plugins/dw/skills/minimax-h3/SKILL.md
+++ b/plugins/dw/skills/minimax-h3/SKILL.md
@@ -179,8 +179,9 @@ A wanted look (clay, paper): `references/style.md`. Film-term camera or look: th
    portrait's framing imposed on every shot - `at` late in
    a chain for drift sharpening to noise, and `get_output_audio` for a
    voice-over without affect. It returns sound, not text; to confirm a
-   take's lines, run `templates/check-script` with them as `lines` (markup
-   and all) and read its `findings` with `get_output_text`. Dialogue gaps (`shot_dead_air`) need room tone,
+   take's lines, run `templates/check-script` with them as `lines` (each
+   `<d>` block's contents, markup and all, never the prose between blocks,
+   which H3 doesn't speak) and read its `findings` with `get_output_text`. Dialogue gaps (`shot_dead_air`) need room tone,
    in that reference. Then `get_gallery_metadata` for duration/audio and hand the
    user the gallery `url`.
 5. After a run worth keeping, `get_job_workflow` and `save_workflow` it;
diff --git a/plugins/dw/skills/minimax-h3/references/dialogue.md b/plugins/dw/skills/minimax-h3/references/dialogue.md
index 36222344..96ec85d9 100644
--- a/plugins/dw/skills/minimax-h3/references/dialogue.md
+++ b/plugins/dw/skills/minimax-h3/references/dialogue.md
@@ -9,6 +9,11 @@ diffusers-workflow repo; "row N" below is its verdict table. A rule is listed
 only where both seeds showed the effect when the rule was broken and neither
 did when it was kept.
 
+Lines that cite "pacing row N" come from a second record,
+`docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md` (#1184, November
+2026). It ran the skill's non-turbo dialogue settings (no LoRA, 12 steps,
+shifts 12/3, int4) at 960x544, 124 frames, one speaker, seeds 1001 and 2002.
+
 ## The rule
 
 - **Don't name a thing the shot shouldn't show.** Naming something as off
@@ -28,14 +33,25 @@ Measured, not a limit the model enforces (row 1):
 - **A short line is not stretched to fill the clip.** H3 holds silence first
   (2–3.4 s of lead-in for 8–10 words in 5.17 s), then speaks fast, at 5–6
   words/s. In one of those clips the speaker's mouth moved during the silent
-  lead-in. Size the line to the clip, or shorten the clip, when the timing
-  of speech matters.
+  lead-in. Size the line to the clip, shorten the clip, or split the line
+  (below) when the timing of speech matters.
 - **A line at about 2.5–4 written words/s fills the clip**: delivered at
   3.2–4.5 words/s, with phrase pauses of 0.5–2 s nearer the low end. At
   124 frames (5.17 s) that is about 13–21 words; at 345 frames (14.4 s),
   about 36–57.
 - **The tail** after the last word ran 0.09–0.88 s, median about 0.37 s.
   Leaving a second of slack is a safety margin, not something H3 needs.
+- **Splitting a short line into two `<d>` blocks removes the lead-in**
+  (pacing row 1). As one block, `I left the light on for you, Daniel.` opened
+  on 2.14 and 2.72 s of silence and was delivered at 4.71 and 5.41 words/s.
+  Split at its sentence break, `<d>[English] I left the light on.</d>
+  <d>[English] For you, Daniel.</d>`, it started at 0.70 and 0.64 s and was
+  delivered at 2.23 and 2.26 words/s. The split doesn't slow the words: the
+  first block was still spoken at 6.25 and 6.6 words/s. The pacing is a
+  silent gap of about 1.9 s that H3 puts between the blocks. Measured only
+  in the non-turbo dialogue settings, at 124 frames, with one speaker.
+- **Prose between `<d>` blocks isn't spoken** (0 of 6 clips, pacing row 3),
+  so it doesn't count toward the line's length.
 
 Prompts of any length reach the model whole: the H3 text encoder doesn't
 truncate (row 7).
@@ -76,6 +92,13 @@ rule and none is worth prompt words yet:
     and no source word (`2026-10-h3-aiskills-probes.md` row P2, #1059).
     `templates/voice-clone` (Audio8-TTS, #1121) is another source for that
     reference: it speaks a new line in a real speaker's cloned voice, with consent.
+- Refuted, though the action landed on one seed of two:
+  - a body action written between two `<d>` blocks (`He sniffs and looks
+    down at the table.`) doesn't pace a line beyond what the split alone
+    gives, and H3 doesn't stage it in the gap. The man sat still through the
+    break on both seeds. The look-down came as the second block began, at
+    the edge of the break on one seed and after it on the other (pacing
+    row 2).
 - Not settled:
   - whether `<breath>` adds a beat: it may lengthen a pause H3 already takes
     at a sentence break, but it doesn't create one (row 6b);

From 406434408a7b4e6cfd8284e62863216bc7c15e87 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Mon, 2 Nov 2026 19:48:05 -0600
Subject: [PATCH 59/74] docs(proposals): #1180 - design record for H3 dialogue
 pacing; todo entry

Close-out of #1180 (stages #1184, #1185). Enhancer follow-up filed as #1186.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../complete/h3-dialogue-pacing-complete.md   | 91 +++++++++++++++++++
 docs/proposals/todo.md                        |  8 ++
 2 files changed, 99 insertions(+)
 create mode 100644 docs/proposals/complete/h3-dialogue-pacing-complete.md

diff --git a/docs/proposals/complete/h3-dialogue-pacing-complete.md b/docs/proposals/complete/h3-dialogue-pacing-complete.md
new file mode 100644
index 00000000..5e97fb4f
--- /dev/null
+++ b/docs/proposals/complete/h3-dialogue-pacing-complete.md
@@ -0,0 +1,91 @@
+# H3 dialogue pacing: split `<d>` blocks and body actions between them (#1180)
+
+Written by model `claude-opus-5-5` via provider `anthropic` (close-out,
+2026-11-02). Plan v2 approved by Don on 2026-11-02 (non-turbo, no arm D,
+Q2–Q5 at their defaults) as "build small"; two stages, #1184 (probe) and
+#1185 (skill lines), both verified. The probe record is
+`docs/proposals/audits/2026-11-h3-dialogue-pacing-probes.md`; this doc is
+the design record around it.
+
+## The idea
+
+A post on X about **Seedance 2.5** dialogue (author and URL not recorded)
+listed five tips for natural delivery: careful punctuation, short lines with
+pauses, body actions as punctuation between lines, anchor gestures, and
+splitting the generation. The 2026-10 dialogue probes (row 1) had measured
+that H3 doesn't pace a short line across the clip: 2–3.4 s of silence, then
+5–6 words/s. The only lever was sizing the line to the clip. The question
+was whether splitting the line into `<d>` segments, with or without a body
+action between them, spreads the speech instead.
+
+## Decisions
+
+- **Line:** 8 words (`I left the light on for you, Daniel.`, row 1's r150
+  line, Q5), not the issue's ~12, which row 1 showed already mostly fills a
+  124-frame clip.
+- **Non-turbo (Don's ruling):** the skill's dialogue settings
+  (`lora_model_name: null`, 12 steps, shifts 12/3, int4), because dialogue
+  shots drop the turbo LoRA. Arm A was re-run as the control rather than
+  compared with row 1's turbo numbers.
+- **Arms:** A one block, B two blocks with a sentence break, C B plus
+  "He sniffs and looks down at the table." between them. Arm D (anchor
+  gesture) dropped (Q1).
+- **Listening (Q2):** verdicts rest on measures only; no "more natural"
+  claim without Don's listening verdict.
+- **check-script (Q3):** if a rule landed, SKILL.md says to pass only the
+  `<d>` contents as `lines`.
+- **Enhancer (Q4):** not changed here; proposed as its own `idea` at
+  close-out (see *Follow-ups*).
+- **Out of scope:** punctuation alone (rows 6/6b), tip 5 (already in the
+  skill), other models, turbo runs, engine or check-script code.
+
+## What the probe found
+
+Two jobs on lem (develop @ 05ffa7ea, diffusers 0.42.1), `150751a7522c`
+(seed 1001) and `900ea1581d22` (seed 2002), all three arms per job,
+~15 GPU min in total against a 50 min cap.
+
+| # | Rule | Verdict |
+| --- | --- | --- |
+| 1 | Split paces the line | **confirmed**, with a caveat: lead 0.70 / 0.64 s and 2.23 / 2.26 words/s delivered, against A's 2.14 / 2.72 s and 4.71 / 5.41. Segment 1 is still spoken at 6.25 / 6.6 words/s; the pacing is a ~1.9 s silent gap between blocks and a slower segment 2. |
+| 2 | An action between segments paces it | **refuted**: timing matches B, but H3 doesn't stage the action in the break; the look-down lands as segment 2 starts (window edge on s1001, after it on s2002). |
+| 3 | The action prose is spoken | **no**, 0 of 6 clips. |
+
+No listening verdict from Don was recorded, so neither the doc nor the skill
+makes a naturalness claim. The A/C clips for listening are listed in the
+audit doc's *For Don: listening*.
+
+## What was built
+
+- **Stage A (#1184):** the audit doc above. Docs only.
+- **Stage B (#1185):** `plugins/dw/skills/minimax-h3/references/dialogue.md`
+  gained the split technique under *How long a line can be* (arm B prompt,
+  its numbers, the caveat, the configuration limit), a line that prose
+  between blocks isn't spoken and doesn't count toward line length, the
+  "short line" bullet now offering "split the line", and a refuted bullet
+  for C under *Probed and not a rule*. SKILL.md's check-script line says
+  `lines` are each `<d>` block's contents, never the prose between blocks
+  (11,933 bytes, under the 12,288 cap). No plugin version bump: the plugin's
+  version is the engine's (`plugins/dw/README.md`), and harnest#182 amended
+  C-F692 to match. No `prompt-lint.md` note (optional).
+
+## Bounces per stage
+
+- **#1184:** two. First, the hand-off didn't carry what C-F688–C-F690
+  needed to reproduce the per-clip table; second, C-F690's action-placement
+  call for C s1001 was revised to a borderline yes and the C verdict to
+  1 of 2 seeds. Both fixes were docs only; no GPU re-run.
+- **#1185:** none on the build. C-F692 couldn't pass as first written (it
+  expected a version bump, and the tester's shell had no `wc`); Don amended
+  the case (harnest#182), measured the byte cap himself, and closed the
+  stage.
+
+Stage costs: not recorded in the stage comments, so left out.
+
+## Follow-ups
+
+- **Enhancer `idea`** (Q4): `dw/workflows/h3_context_ir.json` writes one
+  `<d>` block per line. Filed as #1186: whether the enhancer
+  should split a short line into two blocks at a sentence break.
+- **Not settled:** whether the split sounds more natural (Don's listening
+  call); other clip lengths, turbo, and more than one speaker were not probed.
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index 61419342..d0100891 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -557,6 +557,14 @@ remaining deferred fix is recorded in
   none ignored. Record, including what was deferred (the 7 unmeasured
   moves, tier 2 in `cinematic-addons.md`):
   `complete/cinematic-controls-complete.md`.
+- **H3 dialogue pacing: split `<d>` blocks** (#1180, stages #1184, #1185),
+  shipped 2026-11-02. A non-turbo probe on lem found that splitting a short
+  line into two `<d>` blocks removes the silent lead-in on both seeds (the
+  first block is still fast; the pause sits between blocks). Body-action
+  prose between blocks adds nothing and isn't spoken. `minimax-h3`'s
+  `dialogue.md` and check-script line say so. Probe record:
+  `audits/2026-11-h3-dialogue-pacing-probes.md`; design record:
+  `complete/h3-dialogue-pacing-complete.md`. Enhancer follow-up: #1186.
 
 ## Declined
 

From 03f63594f5179cb20b239681a5ee38a68e2abcbc Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:25:43 -0600
Subject: [PATCH 60/74] fix(h3): #1174 - guide_noise encodes the noised guide
 once; the paired encode was E(noisy)

E(clean) + (E(noisy) - E(clean)) under one encode seed is E(noisy), so the
clean encode was a wasted video-VAE pass per guide per segment and the
docstrings described arithmetic the code did not do. Same latents, one
encode. Tests, guide, map row and the design record say so.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md                          |  2 +-
 docs/WORKFLOW_GUIDE.md                        | 13 +++---
 .../complete/h3-guide-chain-drift-complete.md |  8 ++--
 docs/proposals/todo.md                        |  4 +-
 dw/pipeline_processors/h3_guide_steps.py      | 20 ++++-----
 dw/pipeline_processors/h3_rules.py            |  2 +-
 tests/test_h3_guide_noise.py                  | 42 +++++++++----------
 7 files changed, 44 insertions(+), 47 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 10844cae..7b954cd5 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`, decided by the block itself; it declares `idle_without = "latent_anchor"` so the step-progress narrator leaves it unnamed then; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`, and `run_chain` sets `pipeline.latent_anchor_source` to `segment 1` for those segments only; `Pipeline._with_latent_anchor` emits one `log` line per anchored call naming that source, the quoted reference, or the step's frames; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` adds E(noisy) - E(clean) to each video guide's clean encode when `guide_noise` is set, on `h3_rules.guide_noise_taper`, and encodes once without it; `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_no_anchor_logs_nothing`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_a_chain_segment_logs_its_label_and_segment_1`, `tests/test_modular_progress.py::test_the_anchor_step_is_idle_without_latent_anchor`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
+| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`, decided by the block itself; it declares `idle_without = "latent_anchor"` so the step-progress narrator leaves it unnamed then; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`, and `run_chain` sets `pipeline.latent_anchor_source` to `segment 1` for those segments only; `Pipeline._with_latent_anchor` emits one `log` line per anchored call naming that source, the quoted reference, or the step's frames; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` encodes each video guide's noised pixels in place of the clean ones when `guide_noise` is set, on `h3_rules.guide_noise_taper`, one encode per guide either way (PrefixStream's paired encode under one seed is that encode, so the clean one is not run); `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_no_anchor_logs_nothing`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_a_chain_segment_logs_its_label_and_segment_1`, `tests/test_modular_progress.py::test_the_anchor_step_is_idle_without_latent_anchor`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
diff --git a/docs/WORKFLOW_GUIDE.md b/docs/WORKFLOW_GUIDE.md
index d73f15e8..2e2ddf9f 100644
--- a/docs/WORKFLOW_GUIDE.md
+++ b/docs/WORKFLOW_GUIDE.md
@@ -1825,13 +1825,12 @@ with a visible change of pace. `guide_noise` (an H3 `t2va`/`fl2va` argument, a n
 [0, 1), about 0.4 to start) loosens every video guide on the step: each guide frame gets
 Gaussian noise of standard deviation `amplitude x 255` on its 0-255 pixels, where the
 amplitude falls on a cosine taper from `guide_noise` at the guide's first frame to 0 at
-its last, so the frames nearest the new material are held clean. The guide is encoded
-twice with the same encode seed, clean and noisy, and E(noisy) - E(clean) is added to the
-clean latent; the noise draw is seeded per guide (`keyframe_encode_seed` + the guide's
-index), so a seeded run repeats. The rows are still held, never denoised, and the row
-plan, the refine schedule and guide audio (`"audio": true`) are unchanged.
-`guide_noise: 0` (or absent) skips the second encode, and the latent is bit-identical to
-a step without it.
+its last, so the frames nearest the new material are held clean. The noised frames are
+encoded once, under the same encode seed as a clean guide; the noise draw is seeded per
+guide (`keyframe_encode_seed` + the guide's index), so a seeded run repeats. The rows are
+still held, never denoised, and the row plan, the refine schedule and guide audio
+(`"audio": true`) are unchanged. `guide_noise: 0` (or absent) adds no noise, and the
+latent is bit-identical to a step without it.
 
 It is a step argument, so in a `continuity: "guide"` chain it rides to every segment and
 loosens the carried guide too.
diff --git a/docs/proposals/complete/h3-guide-chain-drift-complete.md b/docs/proposals/complete/h3-guide-chain-drift-complete.md
index 6f1b7ae8..97f645c9 100644
--- a/docs/proposals/complete/h3-guide-chain-drift-complete.md
+++ b/docs/proposals/complete/h3-guide-chain-drift-complete.md
@@ -107,9 +107,11 @@ Merged as `bdbe4723` on `develop` (commits 3dbf8a04, 9363e97c), deployed to lem.
 - **Cosine taper** per guide frame (`h3_rules.guide_noise_taper`): `guide_noise`
   at the first frame, exactly 0 at the last, the one next to the join. A
   one-frame guide stays clean.
-- **Paired encode** in `DwH3GuideLayoutStep._encode_guides`: the clean and the
-  noisy pixels are encoded with the same seed, and `E(noisy) − E(clean)` is added
-  to the clean latent, so 0 is exactly today's latent. Guide rows are held as
+- **Noised encode** in `DwH3GuideLayoutStep._encode_guides`: the noisy pixels
+  are encoded once, under the clean guide's seed, so 0 is exactly today's
+  latent. (It first shipped as PrefixStream's paired encode, `E(clean) +
+  (E(noisy) − E(clean))`, which under one encode seed is `E(noisy)` with a
+  wasted clean encode; the 0.19.0 release review folded it.) Guide rows are held as
   before, the row plan and refine schedule are unchanged, and guide audio rows
   are untouched.
 - A warning when nothing is noised (no `guides` and no guide chain).
diff --git a/docs/proposals/todo.md b/docs/proposals/todo.md
index d0100891..5311d8c0 100644
--- a/docs/proposals/todo.md
+++ b/docs/proposals/todo.md
@@ -40,8 +40,8 @@ remaining deferred fix is recorded in
 - **H3 guide-chain drift counters** (#1166, stages #1173-#1175), shipped
   2026-11-02. Two off-by-default H3 options: `latent_anchor` with
   `chain.anchor: "segment_1"`, a pre-decode per-channel latent statistics
-  match to segment 1, and `guide_noise`, a cosine-tapered paired-encode noise
-  on video guides. A 10-segment, two-scene probe on lem found the anchor
+  match to segment 1, and `guide_noise`, cosine-tapered pixel noise on video
+  guides before their encode. A 10-segment, two-scene probe on lem found the anchor
   (with or without guide noise) best on a static scene and worst on a
   moving-camera arc, and guide noise alone the only arm that didn't hurt the
   arc. No template or skill sets either until Don's taste verdict (Q6);
diff --git a/dw/pipeline_processors/h3_guide_steps.py b/dw/pipeline_processors/h3_guide_steps.py
index 45039363..c7450966 100644
--- a/dw/pipeline_processors/h3_guide_steps.py
+++ b/dw/pipeline_processors/h3_guide_steps.py
@@ -314,8 +314,8 @@ def inputs(self):
                 default=None,
                 description=(
                     "Fixed guide noise in [0, 1): pixel noise on every video guide, tapered from this std at "
-                    "the guide's first frame to none at its last, put into the guide's latent by a paired "
-                    "encode (the same encode seed for the noisy and clean frames). The rows stay held. "
+                    "the guide's first frame to none at its last, encoded in place of the clean frames "
+                    "under the same encode seed. The rows stay held. "
                     "0 or unset is the guide as it is; audio is never noised."
                 ),
             ),
@@ -378,10 +378,11 @@ def _encode_guides(
         """([latents], [positions]): each guide VAE-encoded on the canvas, and
         its rows' rotary positions, timed from the target frame it lands on.
 
-        With `guide_noise`, each guide is also encoded with tapered pixel noise
-        (`noised_guide_pixels`) under the same encode seed, and the difference
-        E(noisy) - E(clean) is added to the clean latent - PrefixStream's paired
-        encode, so 0 leaves today's latent exactly."""
+        With `guide_noise`, the guide's pixels get tapered noise
+        (`noised_guide_pixels`) before the one encode, under the same encode
+        seed as a clean guide - PrefixStream's guide noise. (Its paired encode,
+        E(clean) + (E(noisy) - E(clean)), is E(noisy) under one seed, so the
+        clean encode is never run.) 0 leaves today's latent exactly."""
         device = components._execution_device
         target_time = before_denoise._temporal_position_grid(
             laid["num_latent_frames"], float(num_text)
@@ -396,15 +397,12 @@ def _encode_guides(
                 raise ValueError(f"guides[{index}]: {problem}")
             pixels = fit_guide_frames(frames, laid["height"], laid["width"])
             pixels = torch.from_numpy(np.ascontiguousarray(pixels))
-            encoded = self._encode_pixels(components, pixels, device)
             if guide_noise:
-                noisy = noised_guide_pixels(
+                pixels = noised_guide_pixels(
                     pixels, guide_noise, components.keyframe_encode_seed + index
                 )
-                encoded = encoded + (
-                    self._encode_pixels(components, noisy, device) - encoded
-                )
                 logger.info(f"Guide {index}: guide_noise {guide_noise} on its frames")
+            encoded = self._encode_pixels(components, pixels, device)
             latents.append(encoded)
             positions.append(
                 guide_position_ids(
diff --git a/dw/pipeline_processors/h3_rules.py b/dw/pipeline_processors/h3_rules.py
index fe0fd456..c74cb79a 100644
--- a/dw/pipeline_processors/h3_rules.py
+++ b/dw/pipeline_processors/h3_rules.py
@@ -459,7 +459,7 @@ def not_h3(pipeline, argument=HOLD_AUDIO_INPUT, taken_by=H3_WORKFLOWS):
 
 # Fixed guide noise (#1166, after PrefixStream): pixel noise on every video
 # guide, tapered by `guide_noise_taper` to clean at the guide's last frame, put
-# into its latent by a paired encode (h3_guide_steps.DwH3GuideLayoutStep). The
+# into its latent by encoding the noised pixels (h3_guide_steps.DwH3GuideLayoutStep). The
 # rows stay held condition rows; audio is never noised.
 GUIDE_NOISE_INPUT = "guide_noise"
 
diff --git a/tests/test_h3_guide_noise.py b/tests/test_h3_guide_noise.py
index 28270ba9..073d7b0b 100644
--- a/tests/test_h3_guide_noise.py
+++ b/tests/test_h3_guide_noise.py
@@ -1,5 +1,5 @@
 """MiniMax-H3 fixed guide noise (#1166, #1174): the cosine taper, the noised
-pixels, the paired encode in the guide layout step, the pre-run checks and the
+pixels, the noised encode in the guide layout step, the pre-run checks and the
 call-time refusal of `guide_noise`."""
 
 import math
@@ -167,7 +167,7 @@ def video_guide(frame=17, n=22, fill=100):
     return {"video": np.full((n, 32, 32, 3), fill, np.uint8), "frame": frame}
 
 
-class TestPairedEncode:
+class TestNoisedEncode:
     @pytest.mark.parametrize("noise", [None, 0, 0.0])
     def test_zero_is_todays_latent_with_one_encode_per_guide(self, encodes, noise):
         guides = [video_guide(0), video_guide(17)]
@@ -187,42 +187,40 @@ def test_zero_is_the_plain_encode(self, encodes):
         expected = encodes_value(pixels)
         assert torch.equal(state.get("condition_latents")[0], expected)
 
-    def test_noise_encodes_twice_per_guide_with_one_seed(self, encodes):
+    def test_noise_encodes_once_per_guide_with_the_clean_seed(self, encodes):
         run_step([video_guide(0), video_guide(17)], 0.4)
-        assert len(encodes) == 4
-        # Clean then noisy, per guide, under the same encode seed
-        for clean, noisy in (encodes[0:2], encodes[2:4]):
-            assert clean[1] == noisy[1] == 100
-            assert clean[0].dtype == torch.uint8
-            assert noisy[0].dtype == torch.float32
-            assert clean[0].shape == noisy[0].shape
-
-    def test_result_is_clean_plus_the_encode_difference(self, encodes):
+        # The noisy pixels, encoded once each, under the clean encode seed
+        assert len(encodes) == 2
+        for noisy, seed in encodes:
+            assert seed == 100
+            assert noisy.dtype == torch.float32
+
+    def test_result_is_the_noisy_encode(self, encodes):
         state = run_step([video_guide()], 0.4)
-        (clean_pixels, _), (noisy_pixels, _) = encodes
-        clean = encodes_value(clean_pixels)
+        ((noisy_pixels, _),) = encodes
+        clean = encodes_value(torch.full_like(noisy_pixels, 100))
         noisy = encodes_value(noisy_pixels)
         assert not torch.equal(clean, noisy)
-        result = state.get("condition_latents")[0]
-        assert torch.equal(result, clean + (noisy - clean))
+        assert torch.equal(state.get("condition_latents")[0], noisy)
 
     def test_the_noisy_frames_are_the_tapered_noise(self, encodes):
         run_step([video_guide()], 0.4)
-        (clean_pixels, _), (noisy_pixels, _) = encodes
+        ((noisy_pixels, _),) = encodes
+        clean_pixels = torch.full_like(noisy_pixels, 100)
         # (1, c, n, h, w): the last frame is the join, left clean
-        assert torch.equal(noisy_pixels[:, :, -1], clean_pixels[:, :, -1].float())
-        assert not torch.equal(noisy_pixels[:, :, 0], clean_pixels[:, :, 0].float())
+        assert torch.equal(noisy_pixels[:, :, -1], clean_pixels[:, :, -1])
+        assert not torch.equal(noisy_pixels[:, :, 0], clean_pixels[:, :, 0])
 
     def test_another_guide_gets_another_noise_seed(self, encodes):
         run_step([video_guide(0, fill=100), video_guide(17, fill=100)], 0.4)
-        first = encodes[1][0]
-        second = encodes[3][0]
+        first = encodes[0][0]
+        second = encodes[1][0]
         assert not torch.equal(first, second)
 
     def test_a_run_repeats(self, encodes):
         run_step([video_guide()], 0.4)
         run_step([video_guide()], 0.4)
-        assert torch.equal(encodes[1][0], encodes[3][0])
+        assert torch.equal(encodes[0][0], encodes[1][0])
 
 
 def encodes_value(pixels):

From 266263984ba80ff23a8b1e7bb87f25d511ea8414 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:25:43 -0600
Subject: [PATCH 61/74] fix(tasks): face_drift loop no longer shadows the shot
 index; compound join checks bounds first

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/tasks/face_drift.py      | 4 ++--
 dw/tasks/script_findings.py | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/dw/tasks/face_drift.py b/dw/tasks/face_drift.py
index 887928b0..1b4373b4 100644
--- a/dw/tasks/face_drift.py
+++ b/dw/tasks/face_drift.py
@@ -244,8 +244,8 @@ def _measure_shot(video, index, shot, media, models, embedding, samples):
     where = _where(index, shot.get("name"), first, last, media.fps)
     indexes = _sample_indexes(start, count, media.frame_count, samples)
     matches = []
-    for index in indexes:
-        faces = _embeddings(detect, recognizer, _picture_at(video, index))
+    for frame in indexes:
+        faces = _embeddings(detect, recognizer, _picture_at(video, frame))
         if faces:
             matches.append(_best_match(recognizer, embedding, faces))
     return _shot_measurement(where, matches, len(indexes), len(matches))
diff --git a/dw/tasks/script_findings.py b/dw/tasks/script_findings.py
index d9026460..dc219302 100644
--- a/dw/tasks/script_findings.py
+++ b/dw/tasks/script_findings.py
@@ -195,8 +195,10 @@ def _merge_compounds(words, other):
     i = 0
     while i < len(words):
         for span in (3, 2):
+            if i + span > len(words):
+                continue
             joined = "".join(words[i : i + span])
-            if i + span <= len(words) and joined in targets:
+            if joined in targets:
                 out.append(joined)
                 i += span
                 break

From 8b21b25602c074ee4cad29b684e1a791fd5f928d Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:25:55 -0600
Subject: [PATCH 62/74] refactor(h3): ratchet - anchor statistics in
 h3_anchor_stats so the block never imports its importer

`h3_anchor_steps` read `channel_statistics` and `match_channel_statistics`
from `h3_anchor`, which lazily imports the block in `anchor_block()`: grimp
counts the lazy edge, so the pair was an import cycle. The arithmetic now
lives in `h3_anchor_stats.py` (torch only), `h3_anchor` re-exports it and the
block imports from there - the layering `h3_mask_cells` set.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/pipeline_processors/h3_anchor.py       | 46 +++--------------------
 dw/pipeline_processors/h3_anchor_stats.py | 46 +++++++++++++++++++++++
 dw/pipeline_processors/h3_anchor_steps.py |  2 +-
 3 files changed, 53 insertions(+), 41 deletions(-)
 create mode 100644 dw/pipeline_processors/h3_anchor_stats.py

diff --git a/dw/pipeline_processors/h3_anchor.py b/dw/pipeline_processors/h3_anchor.py
index 6d68bb6f..e7aca83a 100644
--- a/dw/pipeline_processors/h3_anchor.py
+++ b/dw/pipeline_processors/h3_anchor.py
@@ -31,8 +31,12 @@
 
 import logging
 
-import torch
-
+from .h3_anchor_stats import (  # noqa: F401 - re-exported
+    CHANNEL_DIMS,
+    STD_FLOOR,
+    channel_statistics,
+    match_channel_statistics,
+)
 from .h3_hold import core_denoise_sequences
 from .h3_guides import guide_frames_array
 from .h3_rules import (
@@ -44,44 +48,6 @@
 
 logger = logging.getLogger("dw")
 
-# The floor a generated channel's std is held to, so a flat channel is moved
-# onto the anchor's mean rather than divided by zero
-STD_FLOOR = 1e-6
-# The dims a (batch, channel, frames, height, width) latent is reduced over
-CHANNEL_DIMS = (0, 2, 3, 4)
-
-
-def channel_statistics(latents):
-    """(mean, std) per channel of a (B, C, T, H, W) latent, each shaped
-    (1, C, 1, 1, 1), in float32 - over every batch item and latent position."""
-    values = latents.to(torch.float32)
-    mean = values.mean(dim=CHANNEL_DIMS, keepdim=True)
-    std = values.std(dim=CHANNEL_DIMS, keepdim=True, unbiased=False)
-    return mean, std
-
-
-def match_channel_statistics(latents, anchor_mean, anchor_std):
-    """`latents` (B, C, T, H, W) rescaled per channel to `anchor_mean` and
-    `anchor_std`: `(x - mean_x) / std_x * std_a + mean_a`, computed in float32
-    and returned in the latent's own dtype and device."""
-    if latents.dim() != 5:
-        raise ValueError(
-            f"latent_anchor works on a (batch, channel, frames, height, width) "
-            f"video latent, got shape {tuple(latents.shape)}"
-        )
-    values = latents.to(torch.float32)
-    mean, std = channel_statistics(values)
-    anchor_mean = anchor_mean.to(values.device, torch.float32).reshape(1, -1, 1, 1, 1)
-    anchor_std = anchor_std.to(values.device, torch.float32).reshape(1, -1, 1, 1, 1)
-    if anchor_mean.shape[1] != values.shape[1]:
-        raise ValueError(
-            f"latent_anchor has {anchor_mean.shape[1]} latent channels and the "
-            f"render {values.shape[1]}"
-        )
-    matched = (values - mean) / std.clamp_min(STD_FLOOR) * anchor_std + anchor_mean
-    return matched.to(latents.dtype)
-
-
 def anchor_block():
     """The anchor step class (imports diffusers on first use)."""
     from .h3_anchor_steps import DwH3LatentAnchorStep
diff --git a/dw/pipeline_processors/h3_anchor_stats.py b/dw/pipeline_processors/h3_anchor_stats.py
new file mode 100644
index 00000000..0c5fc984
--- /dev/null
+++ b/dw/pipeline_processors/h3_anchor_stats.py
@@ -0,0 +1,46 @@
+"""The latent statistics arithmetic behind MiniMax-H3's `latent_anchor`
+(dw/pipeline_processors/h3_anchor.py, which re-exports these names): a (B, C, T,
+H, W) latent's per-channel mean and std, and the rescale of one latent onto
+another's statistics. The block (`h3_anchor_steps.py`) reads them from here, so
+the block never imports its importer - the layering `h3_mask_cells.py` set.
+Torch only: no diffusers.
+"""
+
+import torch
+
+# The floor a generated channel's std is held to, so a flat channel is moved
+# onto the anchor's mean rather than divided by zero
+STD_FLOOR = 1e-6
+# The dims a (batch, channel, frames, height, width) latent is reduced over
+CHANNEL_DIMS = (0, 2, 3, 4)
+
+
+def channel_statistics(latents):
+    """(mean, std) per channel of a (B, C, T, H, W) latent, each shaped
+    (1, C, 1, 1, 1), in float32 - over every batch item and latent position."""
+    values = latents.to(torch.float32)
+    mean = values.mean(dim=CHANNEL_DIMS, keepdim=True)
+    std = values.std(dim=CHANNEL_DIMS, keepdim=True, unbiased=False)
+    return mean, std
+
+
+def match_channel_statistics(latents, anchor_mean, anchor_std):
+    """`latents` (B, C, T, H, W) rescaled per channel to `anchor_mean` and
+    `anchor_std`: `(x - mean_x) / std_x * std_a + mean_a`, computed in float32
+    and returned in the latent's own dtype and device."""
+    if latents.dim() != 5:
+        raise ValueError(
+            f"latent_anchor works on a (batch, channel, frames, height, width) "
+            f"video latent, got shape {tuple(latents.shape)}"
+        )
+    values = latents.to(torch.float32)
+    mean, std = channel_statistics(values)
+    anchor_mean = anchor_mean.to(values.device, torch.float32).reshape(1, -1, 1, 1, 1)
+    anchor_std = anchor_std.to(values.device, torch.float32).reshape(1, -1, 1, 1, 1)
+    if anchor_mean.shape[1] != values.shape[1]:
+        raise ValueError(
+            f"latent_anchor has {anchor_mean.shape[1]} latent channels and the "
+            f"render {values.shape[1]}"
+        )
+    matched = (values - mean) / std.clamp_min(STD_FLOOR) * anchor_std + anchor_mean
+    return matched.to(latents.dtype)
diff --git a/dw/pipeline_processors/h3_anchor_steps.py b/dw/pipeline_processors/h3_anchor_steps.py
index c45ed041..74bf386e 100644
--- a/dw/pipeline_processors/h3_anchor_steps.py
+++ b/dw/pipeline_processors/h3_anchor_steps.py
@@ -18,7 +18,7 @@
     OutputParam,
 )
 
-from .h3_anchor import channel_statistics, match_channel_statistics
+from .h3_anchor_stats import channel_statistics, match_channel_statistics
 from .h3_guide_steps import fit_guide_frames
 from .h3_rules import LATENT_ANCHOR_INPUT, snap_guide_length
 

From 98daa4bee99f837c560172e0c2be903b00d68c23 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:25:55 -0600
Subject: [PATCH 63/74] refactor(h3): ratchet - the H3 argument wrappers move
 to pipeline_h3 as a Pipeline mixin

pipeline.py had grown past the 1,100-line ceiling. `_with_held_audio`,
`_with_guides`, `_with_guide_noise`, `_with_latent_anchor`, `_with_mask` and
`_check_refine`, with `latent_anchor_source`, are one group - the per-call
H3 argument checks - and now sit in `H3Arguments`, which `Pipeline` mixes in,
so `Pipeline._with_guides` and the rest are still named from there and the
log lines are unchanged.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/pipeline_processors/pipeline.py    | 276 +-----------------------
 dw/pipeline_processors/pipeline_h3.py | 292 ++++++++++++++++++++++++++
 2 files changed, 294 insertions(+), 274 deletions(-)
 create mode 100644 dw/pipeline_processors/pipeline_h3.py

diff --git a/dw/pipeline_processors/pipeline.py b/dw/pipeline_processors/pipeline.py
index c4ae8061..a35d45fe 100644
--- a/dw/pipeline_processors/pipeline.py
+++ b/dw/pipeline_processors/pipeline.py
@@ -32,32 +32,11 @@
     attention_slicing_requested,
     place_component,
 )
-from .h3_anchor import anchor_refusal, prepared_anchor
-from .h3_guides import guide_frames_array, guides_refusal, prepared_guide
-from .h3_hold import hold_audio_reference, holds_audio, refines
-from .h3_mask import binarized, edits_regions
 from .h3_rules import (
-    GUIDES_INPUT,
-    GUIDE_LIMIT,
-    GUIDE_NOISE_INPUT,
-    LATENT_ANCHOR_INPUT,
-    HELD_AUDIO_OUTPUT,
-    HELD_AUDIO_RATE_OUTPUT,
-    HOLD_AUDIO_INPUT,
-    MASK_IMAGE_INPUT,
-    MASK_INPUTS,
-    REFINE_SIGMAS_INPUT,
-    REFINE_STRENGTH_INPUT,
     REFINE_TILES_INPUT,
-    REGENERATE_MASK,
-    SOURCE_VIDEO_INPUT,
-    edits_region,
-    guide_noise_problem,
-    mask_problems,
-    media_problems,
-    refine_problems,
 )
 from .h3_tiles import run_tiled_refine
+from .pipeline_h3 import H3Arguments, latent_anchor_source  # noqa: F401 - re-exported
 from .progress import reported_blocks, reported_progress_bars
 from .timing import denoise_start_hook, is_compiled, mark
 from .remote import remote_text_encoder
@@ -185,17 +164,7 @@ def apply_runtime_settings(pipeline_definition, model):
     apply_sparse_settings(pipeline_definition.get("configuration", {}), model)
 
 
-def latent_anchor_source(chain_source, value):
-    """What a `latent_anchor` names, for the job log (#1181): the chain
-    segment that supplied it, the reference given, or frames passed in."""
-    if chain_source:
-        return chain_source
-    if isinstance(value, str):
-        return f"'{value}'"
-    return "the frames given in the step's arguments"
-
-
-class Pipeline:
+class Pipeline(H3Arguments):
     """
     Manages pipeline initialization, configuration, and execution.
     Handles loading of models, schedulers, and adapters.
@@ -746,247 +715,6 @@ def _call_pipeline(self, arguments, attn_backend):
             mark("call_ended")
             return output
 
-    def _with_held_audio(self, arguments):
-        """`hold_audio` as the reference the H3 hold block takes, and the
-        held track asked for alongside `audio` (dw/pipeline_processors/h3_hold.py).
-
-        Raises:
-            ValueError: If this pipeline cannot hold audio, or the value is not audio
-        """
-        held = arguments.get(HOLD_AUDIO_INPUT)
-        if held is None:
-            if HOLD_AUDIO_INPUT not in arguments:
-                return arguments
-            # A template's null `hold_audio` variable is no hold at all, the
-            # same call as one that never named the argument
-            return {k: v for k, v in arguments.items() if k != HOLD_AUDIO_INPUT}
-        if not holds_audio(self.pipeline):
-            raise ValueError(
-                f"Step '{self.name}': hold_audio is a MiniMax-H3 argument "
-                f"(t2va, fl2va or ref2va), and {type(self.pipeline).__name__} "
-                f"cannot hold a soundtrack"
-            )
-        arguments = dict(arguments)
-        arguments[HOLD_AUDIO_INPUT] = hold_audio_reference(held, self.base_dir)
-        output = arguments.get("output")
-        # A single output is written as a string; the held keys ride along
-        # only when the track itself is asked for
-        names = [output] if isinstance(output, str) else list(output or [])
-        if "audio" in names:
-            arguments["output"] = names + [HELD_AUDIO_OUTPUT, HELD_AUDIO_RATE_OUTPUT]
-        return arguments
-
-    def _with_guides(self, arguments):
-        """`guides` as the H3 guide layout takes them - each clip as uint8
-        frames cut to a whole-latent length (dw/pipeline_processors/h3_guides.py).
-        An empty list is no guides.
-
-        A guide with `"audio": true` also carries its video's soundtrack, as
-        `audio` and `sample_rate`; with `audio` false or absent it carries none.
-
-        Raises:
-            ValueError: If this pipeline cannot take guides, the step also passes
-                `references`, a guide is not `{video, frame, audio?}` with a video,
-                or `audio` is true on a video with no audio
-        """
-        guides = arguments.get(GUIDES_INPUT)
-        if guides is None:
-            return arguments
-        arguments = dict(arguments)
-        if isinstance(guides, (list, tuple)) and not guides:
-            del arguments[GUIDES_INPUT]
-            return arguments
-        where = f"Step '{self.name}': guides"
-        refusal = guides_refusal(self.pipeline)
-        if refusal:
-            raise ValueError(f"{where}: {refusal}")
-        if arguments.get("references") is not None:
-            raise ValueError(
-                f"{where} cannot be combined with references - ref2va lays out "
-                f"its own conditioning; use guides on t2va or fl2va"
-            )
-        if not isinstance(guides, (list, tuple)):
-            raise ValueError(
-                f"{where} must be a list of {{video, frame}}, got {type(guides).__name__}"
-            )
-        if len(guides) > GUIDE_LIMIT:
-            raise ValueError(
-                f"{where} takes at most {GUIDE_LIMIT} clips, got {len(guides)}"
-            )
-        prepared = [
-            prepared_guide(where, index, guide, self.base_dir)
-            for index, guide in enumerate(guides)
-        ]
-        arguments[GUIDES_INPUT] = prepared
-        return arguments
-
-    def _with_guide_noise(self, arguments):
-        """`guide_noise` checked before the call: a number in [0, 1) on a
-        pipeline with the guide layout (dw/pipeline_processors/h3_guide_steps.py).
-        None or 0 is dropped, so the call is today's.
-
-        Raises:
-            ValueError: If the value is not a number in [0, 1), or this
-                pipeline cannot take guides
-        """
-        if GUIDE_NOISE_INPUT not in arguments:
-            return arguments
-        value = arguments[GUIDE_NOISE_INPUT]
-        problem = None if value is None else guide_noise_problem(value)
-        if value is None or (value == 0 and problem is None):
-            arguments = dict(arguments)
-            del arguments[GUIDE_NOISE_INPUT]
-            return arguments
-        where = f"Step '{self.name}'"
-        if problem:
-            raise ValueError(f"{where}: {problem}")
-        refusal = guides_refusal(self.pipeline)
-        if refusal:
-            raise ValueError(f"{where}: {GUIDE_NOISE_INPUT} noises guides - {refusal}")
-        if arguments.get("references") is not None:
-            raise ValueError(
-                f"{where}: {GUIDE_NOISE_INPUT} cannot be combined with references - "
-                f"ref2va lays out its own conditioning; use it on t2va or fl2va"
-            )
-        arguments = dict(arguments)
-        arguments[GUIDE_NOISE_INPUT] = float(value)
-        return arguments
-
-    def _with_latent_anchor(self, arguments):
-        """`latent_anchor` as the H3 anchor step takes it - uint8 frames cut to
-        a whole-latent length (dw/pipeline_processors/h3_anchor.py). None is no
-        anchor.
-
-        Raises:
-            ValueError: If this pipeline has no anchor step (not MiniMax-H3
-                t2va/fl2va), the step also passes `references` (the call would
-                run ref2va, which has no anchor step), or the value is not
-                frames of a video
-        """
-        value = arguments.get(LATENT_ANCHOR_INPUT)
-        if value is None:
-            return arguments
-        where = f"Step '{self.name}': {LATENT_ANCHOR_INPUT}"
-        refusal = anchor_refusal(self.pipeline)
-        if refusal:
-            raise ValueError(f"{where}: {refusal}")
-        if arguments.get("references") is not None:
-            raise ValueError(
-                f"{where} cannot be combined with references - the call would run "
-                f"ref2va, which has no anchor step; use latent_anchor on t2va or fl2va"
-            )
-        arguments = dict(arguments)
-        arguments[LATENT_ANCHOR_INPUT] = prepared_anchor(where, value, self.base_dir)
-        # The one line that says the anchor applies to this call - without it
-        # the job log shows only a block name (#1181)
-        label = getattr(self, "segment_label", None) or self.name
-        source = latent_anchor_source(
-            getattr(self, "latent_anchor_source", None), value
-        )
-        emit_log(f"{label}: latent anchor applied - latent statistics from {source}")
-        return arguments
-
-    def _with_mask(self, arguments):
-        """A regional edit's `source_video` as uint8 frames and its mask as the
-        bool frames the H3 source block maps to rows, under `REGENERATE_MASK`
-        (dw/pipeline_processors/h3_mask.py).
-
-        A step without `width`, `height` or `num_frames` renders the source's.
-
-        Raises:
-            ValueError: If this pipeline cannot take a regional edit, the
-                arguments break `mask_problems`, the mask is all black, or the
-                source or mask does not fit the canvas
-        """
-        takes = edits_regions(self.pipeline)
-        # An inpaint pipeline's own mask_image is no regional edit
-        if not edits_region(arguments, takes):
-            return arguments
-        where = f"Step '{self.name}'"
-        if not takes:
-            raise ValueError(
-                f"{where}: {SOURCE_VIDEO_INPUT} is a MiniMax-H3 argument (t2va, "
-                f"fl2va or ref2va), and {type(self.pipeline).__name__} cannot "
-                f"edit a region"
-            )
-        problems = mask_problems(arguments, chained=self.segment_label is not None)
-        if problems:
-            raise ValueError(f"{where}: {problems[0]}")
-        arguments = dict(arguments)
-        try:
-            source = guide_frames_array(arguments[SOURCE_VIDEO_INPUT])
-        except ValueError as e:
-            raise ValueError(f"{where}: {SOURCE_VIDEO_INPUT} is not a video: {e}")
-        arguments[SOURCE_VIDEO_INPUT] = source
-        frames, height, width = source.shape[:3]
-        for key, value in (
-            ("width", width),
-            ("height", height),
-            ("num_frames", frames),
-        ):
-            if arguments.get(key) is None:
-                arguments[key] = value
-        problems = media_problems(
-            SOURCE_VIDEO_INPUT,
-            source.shape[:3],
-            arguments["width"],
-            arguments["height"],
-            arguments["num_frames"],
-        )
-        name = next((n for n in MASK_INPUTS if arguments.get(n) is not None), None)
-        if name is not None:
-            value = arguments.pop(name)
-            try:
-                if name == MASK_IMAGE_INPUT:
-                    mask = binarized(value)
-                else:
-                    mask = binarized(guide_frames_array(value))
-            except (AttributeError, ValueError) as e:
-                kind = "an image" if name == MASK_IMAGE_INPUT else "a video"
-                raise ValueError(f"{where}: {name} is not {kind}: {e}")
-            if not mask.any():
-                raise ValueError(
-                    f"{where}: {name} is all black, so nothing would regenerate "
-                    f"- white marks the region to regenerate"
-                )
-            problems += media_problems(
-                name,
-                mask.shape,
-                arguments["width"],
-                arguments["height"],
-                arguments["num_frames"],
-            )
-            arguments[REGENERATE_MASK] = mask
-        if problems:
-            raise ValueError(f"{where}: {problems[0]}")
-        return arguments
-
-    def _check_refine(self, arguments):
-        """Refuse a `refine_strength` the H3 refine block cannot run, before the
-        call (dw/pipeline_processors/h3_hold.py).
-
-        Raises:
-            ValueError: If this pipeline cannot refine, the strength, the
-                `refine_sigmas` schedule or the step count is out of range, or the
-                step passes no `latents` or `hold_audio`
-        """
-        given = [
-            name
-            for name in (REFINE_STRENGTH_INPUT, REFINE_SIGMAS_INPUT, REFINE_TILES_INPUT)
-            if arguments.get(name) is not None
-        ]
-        if not given:
-            return
-        where = f"Step '{self.name}': {given[0]}"
-        if not refines(self.pipeline):
-            raise ValueError(
-                f"{where} is a MiniMax-H3 argument (t2va, fl2va or ref2va), and "
-                f"{type(self.pipeline).__name__} cannot refine"
-            )
-        problems = refine_problems(arguments)
-        if problems:
-            raise ValueError(f"Step '{self.name}': {problems[0]}")
-
     def _takes_step_callback(self):
         """Whether this pipeline names `callback_on_step_end` in its own
         signature. Only a pipeline that names the parameter explicitly gets
diff --git a/dw/pipeline_processors/pipeline_h3.py b/dw/pipeline_processors/pipeline_h3.py
new file mode 100644
index 00000000..01c32223
--- /dev/null
+++ b/dw/pipeline_processors/pipeline_h3.py
@@ -0,0 +1,292 @@
+"""The MiniMax-H3 argument wrappers `Pipeline` runs before a call
+(`_call_pipeline`, dw/pipeline_processors/pipeline.py): each takes the call's
+arguments, checks the H3 argument it owns against the loaded pipeline and hands
+back the arguments the block graph takes, or raises the step's refusal. They are
+methods of `Pipeline` through the `H3Arguments` mixin, so `Pipeline._with_guides`
+and the rest are still named from there; the rules they apply live in
+`h3_rules.py` and the owners of each block (`h3_hold`, `h3_guides`, `h3_anchor`,
+`h3_mask`), never here.
+"""
+
+from .h3_anchor import anchor_refusal, prepared_anchor
+from .h3_guides import guide_frames_array, guides_refusal, prepared_guide
+from .h3_hold import hold_audio_reference, holds_audio, refines
+from .h3_mask import binarized, edits_regions
+from .h3_rules import (
+    GUIDES_INPUT,
+    GUIDE_LIMIT,
+    GUIDE_NOISE_INPUT,
+    LATENT_ANCHOR_INPUT,
+    HELD_AUDIO_OUTPUT,
+    HELD_AUDIO_RATE_OUTPUT,
+    HOLD_AUDIO_INPUT,
+    MASK_IMAGE_INPUT,
+    MASK_INPUTS,
+    REFINE_SIGMAS_INPUT,
+    REFINE_STRENGTH_INPUT,
+    REFINE_TILES_INPUT,
+    REGENERATE_MASK,
+    SOURCE_VIDEO_INPUT,
+    edits_region,
+    guide_noise_problem,
+    mask_problems,
+    media_problems,
+    refine_problems,
+)
+from ..events import emit_log
+
+
+def latent_anchor_source(chain_source, value):
+    """What a `latent_anchor` names, for the job log (#1181): the chain
+    segment that supplied it, the reference given, or frames passed in."""
+    if chain_source:
+        return chain_source
+    if isinstance(value, str):
+        return f"'{value}'"
+    return "the frames given in the step's arguments"
+
+
+class H3Arguments:
+    """The H3 argument wrappers, mixed into `Pipeline` (which supplies `name`,
+    `pipeline`, `base_dir`, `segment_label` and `latent_anchor_source`)."""
+
+    def _with_held_audio(self, arguments):
+        """`hold_audio` as the reference the H3 hold block takes, and the
+        held track asked for alongside `audio` (dw/pipeline_processors/h3_hold.py).
+
+        Raises:
+            ValueError: If this pipeline cannot hold audio, or the value is not audio
+        """
+        held = arguments.get(HOLD_AUDIO_INPUT)
+        if held is None:
+            if HOLD_AUDIO_INPUT not in arguments:
+                return arguments
+            # A template's null `hold_audio` variable is no hold at all, the
+            # same call as one that never named the argument
+            return {k: v for k, v in arguments.items() if k != HOLD_AUDIO_INPUT}
+        if not holds_audio(self.pipeline):
+            raise ValueError(
+                f"Step '{self.name}': hold_audio is a MiniMax-H3 argument "
+                f"(t2va, fl2va or ref2va), and {type(self.pipeline).__name__} "
+                f"cannot hold a soundtrack"
+            )
+        arguments = dict(arguments)
+        arguments[HOLD_AUDIO_INPUT] = hold_audio_reference(held, self.base_dir)
+        output = arguments.get("output")
+        # A single output is written as a string; the held keys ride along
+        # only when the track itself is asked for
+        names = [output] if isinstance(output, str) else list(output or [])
+        if "audio" in names:
+            arguments["output"] = names + [HELD_AUDIO_OUTPUT, HELD_AUDIO_RATE_OUTPUT]
+        return arguments
+
+    def _with_guides(self, arguments):
+        """`guides` as the H3 guide layout takes them - each clip as uint8
+        frames cut to a whole-latent length (dw/pipeline_processors/h3_guides.py).
+        An empty list is no guides.
+
+        A guide with `"audio": true` also carries its video's soundtrack, as
+        `audio` and `sample_rate`; with `audio` false or absent it carries none.
+
+        Raises:
+            ValueError: If this pipeline cannot take guides, the step also passes
+                `references`, a guide is not `{video, frame, audio?}` with a video,
+                or `audio` is true on a video with no audio
+        """
+        guides = arguments.get(GUIDES_INPUT)
+        if guides is None:
+            return arguments
+        arguments = dict(arguments)
+        if isinstance(guides, (list, tuple)) and not guides:
+            del arguments[GUIDES_INPUT]
+            return arguments
+        where = f"Step '{self.name}': guides"
+        refusal = guides_refusal(self.pipeline)
+        if refusal:
+            raise ValueError(f"{where}: {refusal}")
+        if arguments.get("references") is not None:
+            raise ValueError(
+                f"{where} cannot be combined with references - ref2va lays out "
+                f"its own conditioning; use guides on t2va or fl2va"
+            )
+        if not isinstance(guides, (list, tuple)):
+            raise ValueError(
+                f"{where} must be a list of {{video, frame}}, got {type(guides).__name__}"
+            )
+        if len(guides) > GUIDE_LIMIT:
+            raise ValueError(
+                f"{where} takes at most {GUIDE_LIMIT} clips, got {len(guides)}"
+            )
+        prepared = [
+            prepared_guide(where, index, guide, self.base_dir)
+            for index, guide in enumerate(guides)
+        ]
+        arguments[GUIDES_INPUT] = prepared
+        return arguments
+
+    def _with_guide_noise(self, arguments):
+        """`guide_noise` checked before the call: a number in [0, 1) on a
+        pipeline with the guide layout (dw/pipeline_processors/h3_guide_steps.py).
+        None or 0 is dropped, so the call is today's.
+
+        Raises:
+            ValueError: If the value is not a number in [0, 1), or this
+                pipeline cannot take guides
+        """
+        if GUIDE_NOISE_INPUT not in arguments:
+            return arguments
+        value = arguments[GUIDE_NOISE_INPUT]
+        problem = None if value is None else guide_noise_problem(value)
+        if value is None or (value == 0 and problem is None):
+            arguments = dict(arguments)
+            del arguments[GUIDE_NOISE_INPUT]
+            return arguments
+        where = f"Step '{self.name}'"
+        if problem:
+            raise ValueError(f"{where}: {problem}")
+        refusal = guides_refusal(self.pipeline)
+        if refusal:
+            raise ValueError(f"{where}: {GUIDE_NOISE_INPUT} noises guides - {refusal}")
+        if arguments.get("references") is not None:
+            raise ValueError(
+                f"{where}: {GUIDE_NOISE_INPUT} cannot be combined with references - "
+                f"ref2va lays out its own conditioning; use it on t2va or fl2va"
+            )
+        arguments = dict(arguments)
+        arguments[GUIDE_NOISE_INPUT] = float(value)
+        return arguments
+
+    def _with_latent_anchor(self, arguments):
+        """`latent_anchor` as the H3 anchor step takes it - uint8 frames cut to
+        a whole-latent length (dw/pipeline_processors/h3_anchor.py). None is no
+        anchor.
+
+        Raises:
+            ValueError: If this pipeline has no anchor step (not MiniMax-H3
+                t2va/fl2va), the step also passes `references` (the call would
+                run ref2va, which has no anchor step), or the value is not
+                frames of a video
+        """
+        value = arguments.get(LATENT_ANCHOR_INPUT)
+        if value is None:
+            return arguments
+        where = f"Step '{self.name}': {LATENT_ANCHOR_INPUT}"
+        refusal = anchor_refusal(self.pipeline)
+        if refusal:
+            raise ValueError(f"{where}: {refusal}")
+        if arguments.get("references") is not None:
+            raise ValueError(
+                f"{where} cannot be combined with references - the call would run "
+                f"ref2va, which has no anchor step; use latent_anchor on t2va or fl2va"
+            )
+        arguments = dict(arguments)
+        arguments[LATENT_ANCHOR_INPUT] = prepared_anchor(where, value, self.base_dir)
+        # The one line that says the anchor applies to this call - without it
+        # the job log shows only a block name (#1181)
+        label = getattr(self, "segment_label", None) or self.name
+        source = latent_anchor_source(
+            getattr(self, "latent_anchor_source", None), value
+        )
+        emit_log(f"{label}: latent anchor applied - latent statistics from {source}")
+        return arguments
+
+    def _with_mask(self, arguments):
+        """A regional edit's `source_video` as uint8 frames and its mask as the
+        bool frames the H3 source block maps to rows, under `REGENERATE_MASK`
+        (dw/pipeline_processors/h3_mask.py).
+
+        A step without `width`, `height` or `num_frames` renders the source's.
+
+        Raises:
+            ValueError: If this pipeline cannot take a regional edit, the
+                arguments break `mask_problems`, the mask is all black, or the
+                source or mask does not fit the canvas
+        """
+        takes = edits_regions(self.pipeline)
+        # An inpaint pipeline's own mask_image is no regional edit
+        if not edits_region(arguments, takes):
+            return arguments
+        where = f"Step '{self.name}'"
+        if not takes:
+            raise ValueError(
+                f"{where}: {SOURCE_VIDEO_INPUT} is a MiniMax-H3 argument (t2va, "
+                f"fl2va or ref2va), and {type(self.pipeline).__name__} cannot "
+                f"edit a region"
+            )
+        problems = mask_problems(arguments, chained=self.segment_label is not None)
+        if problems:
+            raise ValueError(f"{where}: {problems[0]}")
+        arguments = dict(arguments)
+        try:
+            source = guide_frames_array(arguments[SOURCE_VIDEO_INPUT])
+        except ValueError as e:
+            raise ValueError(f"{where}: {SOURCE_VIDEO_INPUT} is not a video: {e}")
+        arguments[SOURCE_VIDEO_INPUT] = source
+        frames, height, width = source.shape[:3]
+        for key, value in (
+            ("width", width),
+            ("height", height),
+            ("num_frames", frames),
+        ):
+            if arguments.get(key) is None:
+                arguments[key] = value
+        problems = media_problems(
+            SOURCE_VIDEO_INPUT,
+            source.shape[:3],
+            arguments["width"],
+            arguments["height"],
+            arguments["num_frames"],
+        )
+        name = next((n for n in MASK_INPUTS if arguments.get(n) is not None), None)
+        if name is not None:
+            value = arguments.pop(name)
+            try:
+                if name == MASK_IMAGE_INPUT:
+                    mask = binarized(value)
+                else:
+                    mask = binarized(guide_frames_array(value))
+            except (AttributeError, ValueError) as e:
+                kind = "an image" if name == MASK_IMAGE_INPUT else "a video"
+                raise ValueError(f"{where}: {name} is not {kind}: {e}")
+            if not mask.any():
+                raise ValueError(
+                    f"{where}: {name} is all black, so nothing would regenerate "
+                    f"- white marks the region to regenerate"
+                )
+            problems += media_problems(
+                name,
+                mask.shape,
+                arguments["width"],
+                arguments["height"],
+                arguments["num_frames"],
+            )
+            arguments[REGENERATE_MASK] = mask
+        if problems:
+            raise ValueError(f"{where}: {problems[0]}")
+        return arguments
+
+    def _check_refine(self, arguments):
+        """Refuse a `refine_strength` the H3 refine block cannot run, before the
+        call (dw/pipeline_processors/h3_hold.py).
+
+        Raises:
+            ValueError: If this pipeline cannot refine, the strength, the
+                `refine_sigmas` schedule or the step count is out of range, or the
+                step passes no `latents` or `hold_audio`
+        """
+        given = [
+            name
+            for name in (REFINE_STRENGTH_INPUT, REFINE_SIGMAS_INPUT, REFINE_TILES_INPUT)
+            if arguments.get(name) is not None
+        ]
+        if not given:
+            return
+        where = f"Step '{self.name}': {given[0]}"
+        if not refines(self.pipeline):
+            raise ValueError(
+                f"{where} is a MiniMax-H3 argument (t2va, fl2va or ref2va), and "
+                f"{type(self.pipeline).__name__} cannot refine"
+            )
+        problems = refine_problems(arguments)
+        if problems:
+            raise ValueError(f"Step '{self.name}': {problems[0]}")

From 4ace3b482ba69d472a505dee082656f2a3265fdb Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:26:43 -0600
Subject: [PATCH 64/74] docs: 0.19.0 release notes draft

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 docs/RELEASING.md | 88 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)

diff --git a/docs/RELEASING.md b/docs/RELEASING.md
index 07de1bac..33ae2634 100644
--- a/docs/RELEASING.md
+++ b/docs/RELEASING.md
@@ -49,6 +49,94 @@ A PyPI install gets released `diffusers`, which may lag the newest pipelines; in
 
 GitHub generates notes from commits at tag time. The sections below are the hand-written notes: the newest is drafted at release prep and pasted into the GitHub release body once the tag has published (`gh release edit v<version> --notes-file ...`). Items a branch's author wants the next note to name go in a new section at the top.
 
+### 0.19.0
+
+<!-- Drafted from v0.18.0..develop at release prep (2026-11-03). Paste into the GitHub release body once the tag has published: gh release edit v0.19.0 --notes-file ... -->
+
+This release is about long H3 chains: two off-by-default ways to hold a
+chain's look across its segments, a measurement that says whether the look
+drifted, a LoRA that was probed against burn, and the skill lines that came
+out of the probes. Alongside it, FLUX 3 Klein no longer loads a model it
+never runs, and the web UI draws a chained step as its segments.
+
+**H3 chain drift** (#1166, #1173, #1174, #1181)
+
+- `latent_anchor`, an H3 `t2va`/`fl2va` argument, matches each segment's
+  per-channel latent statistics to a reference clip before decode.
+  `chain.anchor: "segment_1"` uses the chain's own first segment. It is off
+  unless set, and the run logs one line per anchored call naming the source.
+  The argument meets the media location policy at validate: a bare server
+  path, `..`, `file://` or a loopback URL is refused there.
+- `guide_noise`, a number in [0, 1), loosens every video guide on the step:
+  cosine-tapered Gaussian noise from the guide's first frame to clean at
+  its last, encoded once under the guide's seed. A seeded run repeats.
+- A ten-segment, two-scene probe found the anchor best on a static scene and
+  worst on a moving-camera arc, and guide noise alone the only arm that did
+  not hurt the arc. No template or skill sets either yet; the H3 skill's
+  `chains.md` says what the community reports agree on (#1167).
+- Imagineer13's anti-burn LoRA is in the catalog as
+  `minimax-h3/h3-antiburn`, marked `trial` (#1151, #1159). On a five-segment
+  chain it held segment 1's skin, knit and wall where the unaided chain had
+  burnt by segment 5.
+
+**Measuring drift** (#1152, #1160, #1139, #1158)
+
+- `compare_outputs(names, texture_bands=true)` reports the RMS texture
+  energy per output per moment in three wavelength bands, fine (2-8 px), mid
+  (8-32 px) and coarse (32-128 px), on luma and both chroma differences,
+  over one shared crop, with ratios to the baseline. One name is allowed,
+  so a chain's own segments can be read across. It is a measurement, not a
+  score; the guide's *Texture bands* section says how to read it.
+- `get_gallery_metadata` carries `pcm_sha256`, a SHA-256 of the decoded
+  samples, with `sample_format` and `frames`, for any file with a
+  soundtrack: an identity check for audio over MCP.
+
+**Assessment fixes**
+
+- A `stills_to_video` clip's silent track no longer raises `near_silent` or
+  mono-duplication warnings, `assess_output` no longer reports
+  `shot_dead_air` on its shots, and a cut whose shots are all silent by
+  design no longer warns near-silent (#1161, #1162, #1163). One predicate
+  decides it everywhere.
+- `shot_span_overrun` findings carry severity `warn`; `seam_hole` leaves
+  `rules_applied` when it was skipped on every seam (#1179).
+- `face_drift` measurements and findings name the shot's index, frames and
+  times (#1165), and the `script-to-video` skill says what to do on one
+  (#1178).
+- `check-script` no longer counts a compound Whisper closed that the script
+  wrote open ('candle crumbs' / 'candlecrumbs') against line similarity
+  (#1172).
+
+**FLUX 3 Klein** (#1168)
+
+- Loading Klein on an empty 24 GB card no longer runs out of memory: the
+  pipeline loads only its four components, not the repository's 18.8 GB
+  prompt upsampler, and `configuration.remove_blocks` pops the upsampler
+  block off the modular graph. The step cache keys follow the names.
+- The `controlnet` template passes Canny's thresholds beside `image`, where
+  the detector reads them.
+
+**Skills**
+
+- `minimax-h3`: splitting a short line into two `<d>` blocks removes the
+  lead-in; action prose between blocks is not a pacing rule (#1180, #1184,
+  #1185). `check-script` takes each block's contents, never the prose between.
+- `cinematography`: one light phrase per value in the LTX-2.5, H3 and Klein
+  compile tables (#1142); six named moves measured per video family (#1148).
+- `series-episodes` keeps cast portraits with an extensionless asset name
+  (#1154); the sitcom multicam master sets `move: static` (#1157).
+
+**Web UI and docs**
+
+- A chained step draws its segments: the count when the definition gives
+  it, what each seam carries, and the running segment while the job runs.
+- A workspace named in the URL that the cached listing lacks is refetched
+  once before the UI decides it is gone.
+- Every guide had an accuracy and concision pass, with page screenshots in
+  the README and SERVER guide. The workflow schema's `$id` names this
+  repository, and the HTTP API's OpenAPI document and the schema are linked
+  from the docs.
+
 ### 0.18.0
 
 <!-- Drafted from v0.17.0..develop at release prep (2026-11-01). Paste into the GitHub release body once the tag has published: gh release edit v0.18.0 --notes-file ... -->

From 32f7f81060db04e21c9800515b53b80a8934741b Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:27:04 -0600
Subject: [PATCH 65/74] refactor(chain): ratchet - the segment spill moves to
 chain_spill; run_chain hands its result to _stitched

chain.py had passed the 1,100-line ceiling and run_chain the 150-line
function limit. `SegmentSpill`, `SegmentedFrames`, `SavedFrames` and the
spilled-frames replay are one group - a chain's segments on disk - and now
live in `chain_spill.py`, re-exported from `chain` where result.py,
previous_results.py and assess.py import them. The AudioVideo a chain hands
back is built by `_stitched`, taking the tail off `run_chain`.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/pipeline_processors/chain.py       | 214 ++-----------------------
 dw/pipeline_processors/chain_spill.py | 215 ++++++++++++++++++++++++++
 2 files changed, 229 insertions(+), 200 deletions(-)
 create mode 100644 dw/pipeline_processors/chain_spill.py

diff --git a/dw/pipeline_processors/chain.py b/dw/pipeline_processors/chain.py
index 0cd4741c..a13627b1 100644
--- a/dw/pipeline_processors/chain.py
+++ b/dw/pipeline_processors/chain.py
@@ -32,26 +32,27 @@
 import gc
 import logging
 import math
-import os
 import sys
-from collections.abc import Sequence
 from dataclasses import dataclass
 
 import numpy
 import torch
-from PIL import Image
-from diffusers.utils import is_av_available
 
 from .. import empty_device_cache
 from ..media_types import AudioVideo, fit_codec_padding
 from ..output_extraction import get_artifact_list
-from ..writers import encode_video, frames_for_encoding, output_file_path
 from ..events import emit_log
 from ..shots import shot_record, without_samples
 from ..dsp import as_channels_samples, slice_samples
 from ..task_domains import frames_to_samples
 from ..tasks.joins import equal_power_crossfade_join
 from ..tasks.video_utils import extract_frame, frames_as_pil_list
+from .chain_spill import (  # noqa: F401 - SegmentedFrames and SavedFrames re-exported
+    SavedFrames,
+    SegmentedFrames,
+    SegmentSpill,
+    spilled_frames,
+)
 from .h3_anchor import anchor_refusal
 from .h3_guides import guides_refusal
 from .h3_hold import hold_audio_reference
@@ -228,187 +229,6 @@ class Segment:
     head_trim: int  # frames dropped from its head on the output timeline
 
 
-class SegmentedFrames:
-    """Chained segments spilled to disk, replayed at save time.
-
-    Iterating yields one uint8 (frames, height, width, 3) torch tensor per
-    segment file - the chunk shape encode_video streams from - so the final
-    video is written holding only one segment in memory at a time.
-    """
-
-    def __init__(self, paths, total_frames=None, keep_files=False, stored_frames=None):
-        """
-        Args:
-            paths: The segment files, in output order
-            total_frames: Frames to yield in total - the match_audio tail trim.
-                None yields every stored frame
-            keep_files: Leave the segment files in place after cleanup()
-            stored_frames: Frames across all the files, when known - what
-                frame_count reports, since len() counts files
-        """
-        self.paths = list(paths)
-        self.stored_frames = stored_frames
-        self.total_frames = total_frames
-        self.keep_files = keep_files
-        # True once cleanup() has actually removed the files (not when
-        # keep_files skipped that) - a cached Result pointing at a cleaned-up
-        # SegmentedFrames must not be served to a later miss, since the files
-        # a replay would open are gone. See Result.retainable.
-        self.cleaned = False
-
-    def __len__(self):
-        """Chunk count - one per segment file."""
-        return len(self.paths)
-
-    @property
-    def frame_count(self):
-        """Frames the replay yields, or None when the files were not counted."""
-        if self.stored_frames is None:
-            return None
-        if self.total_frames is None:
-            return self.stored_frames
-        return min(self.stored_frames, self.total_frames)
-
-    def __iter__(self):
-        remaining = self.total_frames
-        for path in self.paths:
-            frames = _decode_segment(path)
-            if remaining is not None:
-                frames = frames[:remaining]
-                remaining -= len(frames)
-            if len(frames):
-                yield frames
-            if remaining == 0:
-                return
-
-    def cleanup(self):
-        """Remove the segment files once the final video is safely written."""
-        if self.keep_files:
-            return
-        for path in self.paths:
-            try:
-                os.remove(path)
-            except FileNotFoundError:
-                pass
-        self.cleaned = True
-
-
-class SavedFrames(Sequence):
-    """The frames of a chain's finished video, read back from the file it saved.
-
-    Once SegmentedFrames.cleanup() has removed the segment files, a later step
-    that names the chain's result (previous_result:) gets these instead: a
-    frame sequence - length is the frame count - decoded from the final file
-    the first time a frame is asked for, so a chain nobody consumes never pays
-    for the decode.
-    """
-
-    def __init__(self, path, count=None, cleaned=True):
-        self.path = path
-        self._count = count
-        self._frames = None
-        # Result.retainable: the segment files this replaced are gone, and a
-        # cache entry must not outlive the file this one reads
-        self.cleaned = cleaned
-
-    def _load(self):
-        if self._frames is None:
-            from ..media import decode_rgb_frames
-
-            self._frames = [Image.fromarray(f) for f in decode_rgb_frames(self.path)]
-            self._count = len(self._frames)
-        return self._frames
-
-    def __len__(self):
-        if self._count is None:
-            self._load()
-        return self._count
-
-    def __getitem__(self, index):
-        return self._load()[index]
-
-
-class SegmentSpill:
-    """Writes each completed segment to disk as a playable mp4.
-
-    Files are named {prefix}.{iteration}.segment-{index:03d}.mp4 in the
-    workflow's output directory - a crashed chain leaves them behind, ready to
-    salvage with gather_videos + concat_videos.
-    """
-
-    def __init__(self, pipeline, config):
-        output_dir = getattr(pipeline, "output_dir", None)
-        file_prefix = getattr(pipeline, "file_prefix", None)
-        if not output_dir or not file_prefix:
-            raise ValueError(
-                "save_segments needs the workflow's output directory - it is "
-                "only available when the chain runs through a workflow"
-            )
-        if not is_av_available():
-            raise ValueError(
-                "save_segments writes mp4 segment files with PyAV - install "
-                "it with: pip install av"
-            )
-        if config.fps is None:
-            raise ValueError(
-                "save_segments needs the frame rate to encode segment files - "
-                "set 'fps' on the chain or a 'frame_rate' pipeline argument"
-            )
-
-        # The same step can chain more than once (previous_result fan-out) -
-        # a per-wrapper counter keeps each iteration's files apart
-        iteration = getattr(pipeline, "_chain_iteration", -1) + 1
-        pipeline._chain_iteration = iteration
-
-        self.output_dir = output_dir
-        self.base_name = f"{file_prefix}.{iteration}"
-        self.fps = config.fps
-        self.paths = []
-        self.frame_count = 0
-
-    def write(self, frames, audio, sample_rate):
-        """Encode one trimmed segment to disk and record its path.
-
-        Args:
-            frames: The segment's on-timeline PIL frames
-            audio: The segment's on-timeline generated audio as
-                (channels, samples) numpy, or None - muxed in so a crashed
-                chain leaves fully playable segments
-            sample_rate: Sample rate of that audio
-        """
-        # A crashed or completed chain leaves segment files behind on disk,
-        # ready to salvage - and the per-wrapper iteration counter (base_name)
-        # restarts at 0 in a fresh process, so a rerun must dedupe through the
-        # same '-N' convention every other output goes through rather than
-        # silently overwriting them.
-        path = output_file_path(
-            self.output_dir,
-            f"{self.base_name}.segment-{len(self.paths):03d}.mp4",
-        )
-
-        audio_track = None
-        if audio is not None and audio.shape[1] and sample_rate is not None:
-            audio_track = torch.from_numpy(numpy.ascontiguousarray(audio))
-
-        encode_video(
-            frames_for_encoding(frames),
-            fps=self.fps,
-            output_path=path,
-            audio=audio_track,
-            audio_sample_rate=sample_rate if audio_track is not None else None,
-        )
-        self.paths.append(path)
-        self.frame_count += len(frames)
-        logger.info(f"Saved chain segment to {path}")
-
-
-def _decode_segment(path):
-    """Read a segment file back as a uint8 (frames, height, width, 3) tensor."""
-    from ..media import decode_rgb_frames
-
-    return torch.from_numpy(numpy.stack(decode_rgb_frames(path), axis=0))
-
-
 def run_chain(pipeline, chain_definition, arguments):
     """Run a pipeline's chain and stitch the segments into one video.
 
@@ -542,8 +362,15 @@ def run_chain(pipeline, chain_definition, arguments):
         pipeline.segment_label = None
         pipeline.latent_anchor_source = None
 
+    return _stitched(config, spill, frames, audio, audio_rate, shots)
+
+
+def _stitched(config, spill, frames, audio, audio_rate, shots):
+    """The chain's one AudioVideo: the frames (replayed from disk when
+    spilled), and either the joined generated audio, the original match_audio
+    track, or no audio at all."""
     if spill is not None:
-        frames = _spilled_frames(config, spill)
+        frames = spilled_frames(config, spill)
 
     if config.source_audio is not None:
         # The video matches the track's duration; the original, unsliced audio
@@ -563,19 +390,6 @@ def run_chain(pipeline, chain_definition, arguments):
     return AudioVideo(frames, audio, audio_rate, fps=config.fps, shots=shots)
 
 
-def _spilled_frames(config, spill):
-    """A spilled chain's frames, replayed lazily from its segment files.
-
-    match_audio overshoots by design - the tail trim happens as the lazy
-    frames replay, so the files themselves stay whole."""
-    return SegmentedFrames(
-        spill.paths,
-        config.total_frames if config.source_audio is not None else None,
-        config.keep_segments,
-        spill.frame_count,
-    )
-
-
 def _segment_arguments(
     arguments, config, segment, continuity, carry, anchor_frames=None
 ):
diff --git a/dw/pipeline_processors/chain_spill.py b/dw/pipeline_processors/chain_spill.py
new file mode 100644
index 00000000..141d23a2
--- /dev/null
+++ b/dw/pipeline_processors/chain_spill.py
@@ -0,0 +1,215 @@
+"""A chain's segments on disk (`save_segments`, dw/pipeline_processors/chain.py):
+`SegmentSpill` writes each finished segment as a playable mp4 beside the
+workflow's outputs, `SegmentedFrames` replays those files one segment at a time
+when the final video is written, and `SavedFrames` is what a later step reads
+once the segment files are gone - the finished video, decoded on first use.
+`chain.py` re-exports the three; `dw/result.py`, `dw/previous_results.py` and
+`dw/tasks/assess.py` import them from there.
+"""
+
+import logging
+import os
+from collections.abc import Sequence
+
+import numpy
+import torch
+from PIL import Image
+from diffusers.utils import is_av_available
+
+from ..writers import encode_video, frames_for_encoding, output_file_path
+
+logger = logging.getLogger("dw")
+
+
+class SegmentedFrames:
+    """Chained segments spilled to disk, replayed at save time.
+
+    Iterating yields one uint8 (frames, height, width, 3) torch tensor per
+    segment file - the chunk shape encode_video streams from - so the final
+    video is written holding only one segment in memory at a time.
+    """
+
+    def __init__(self, paths, total_frames=None, keep_files=False, stored_frames=None):
+        """
+        Args:
+            paths: The segment files, in output order
+            total_frames: Frames to yield in total - the match_audio tail trim.
+                None yields every stored frame
+            keep_files: Leave the segment files in place after cleanup()
+            stored_frames: Frames across all the files, when known - what
+                frame_count reports, since len() counts files
+        """
+        self.paths = list(paths)
+        self.stored_frames = stored_frames
+        self.total_frames = total_frames
+        self.keep_files = keep_files
+        # True once cleanup() has actually removed the files (not when
+        # keep_files skipped that) - a cached Result pointing at a cleaned-up
+        # SegmentedFrames must not be served to a later miss, since the files
+        # a replay would open are gone. See Result.retainable.
+        self.cleaned = False
+
+    def __len__(self):
+        """Chunk count - one per segment file."""
+        return len(self.paths)
+
+    @property
+    def frame_count(self):
+        """Frames the replay yields, or None when the files were not counted."""
+        if self.stored_frames is None:
+            return None
+        if self.total_frames is None:
+            return self.stored_frames
+        return min(self.stored_frames, self.total_frames)
+
+    def __iter__(self):
+        remaining = self.total_frames
+        for path in self.paths:
+            frames = _decode_segment(path)
+            if remaining is not None:
+                frames = frames[:remaining]
+                remaining -= len(frames)
+            if len(frames):
+                yield frames
+            if remaining == 0:
+                return
+
+    def cleanup(self):
+        """Remove the segment files once the final video is safely written."""
+        if self.keep_files:
+            return
+        for path in self.paths:
+            try:
+                os.remove(path)
+            except FileNotFoundError:
+                pass
+        self.cleaned = True
+
+
+class SavedFrames(Sequence):
+    """The frames of a chain's finished video, read back from the file it saved.
+
+    Once SegmentedFrames.cleanup() has removed the segment files, a later step
+    that names the chain's result (previous_result:) gets these instead: a
+    frame sequence - length is the frame count - decoded from the final file
+    the first time a frame is asked for, so a chain nobody consumes never pays
+    for the decode.
+    """
+
+    def __init__(self, path, count=None, cleaned=True):
+        self.path = path
+        self._count = count
+        self._frames = None
+        # Result.retainable: the segment files this replaced are gone, and a
+        # cache entry must not outlive the file this one reads
+        self.cleaned = cleaned
+
+    def _load(self):
+        if self._frames is None:
+            from ..media import decode_rgb_frames
+
+            self._frames = [Image.fromarray(f) for f in decode_rgb_frames(self.path)]
+            self._count = len(self._frames)
+        return self._frames
+
+    def __len__(self):
+        if self._count is None:
+            self._load()
+        return self._count
+
+    def __getitem__(self, index):
+        return self._load()[index]
+
+
+class SegmentSpill:
+    """Writes each completed segment to disk as a playable mp4.
+
+    Files are named {prefix}.{iteration}.segment-{index:03d}.mp4 in the
+    workflow's output directory - a crashed chain leaves them behind, ready to
+    salvage with gather_videos + concat_videos.
+    """
+
+    def __init__(self, pipeline, config):
+        output_dir = getattr(pipeline, "output_dir", None)
+        file_prefix = getattr(pipeline, "file_prefix", None)
+        if not output_dir or not file_prefix:
+            raise ValueError(
+                "save_segments needs the workflow's output directory - it is "
+                "only available when the chain runs through a workflow"
+            )
+        if not is_av_available():
+            raise ValueError(
+                "save_segments writes mp4 segment files with PyAV - install "
+                "it with: pip install av"
+            )
+        if config.fps is None:
+            raise ValueError(
+                "save_segments needs the frame rate to encode segment files - "
+                "set 'fps' on the chain or a 'frame_rate' pipeline argument"
+            )
+
+        # The same step can chain more than once (previous_result fan-out) -
+        # a per-wrapper counter keeps each iteration's files apart
+        iteration = getattr(pipeline, "_chain_iteration", -1) + 1
+        pipeline._chain_iteration = iteration
+
+        self.output_dir = output_dir
+        self.base_name = f"{file_prefix}.{iteration}"
+        self.fps = config.fps
+        self.paths = []
+        self.frame_count = 0
+
+    def write(self, frames, audio, sample_rate):
+        """Encode one trimmed segment to disk and record its path.
+
+        Args:
+            frames: The segment's on-timeline PIL frames
+            audio: The segment's on-timeline generated audio as
+                (channels, samples) numpy, or None - muxed in so a crashed
+                chain leaves fully playable segments
+            sample_rate: Sample rate of that audio
+        """
+        # A crashed or completed chain leaves segment files behind on disk,
+        # ready to salvage - and the per-wrapper iteration counter (base_name)
+        # restarts at 0 in a fresh process, so a rerun must dedupe through the
+        # same '-N' convention every other output goes through rather than
+        # silently overwriting them.
+        path = output_file_path(
+            self.output_dir,
+            f"{self.base_name}.segment-{len(self.paths):03d}.mp4",
+        )
+
+        audio_track = None
+        if audio is not None and audio.shape[1] and sample_rate is not None:
+            audio_track = torch.from_numpy(numpy.ascontiguousarray(audio))
+
+        encode_video(
+            frames_for_encoding(frames),
+            fps=self.fps,
+            output_path=path,
+            audio=audio_track,
+            audio_sample_rate=sample_rate if audio_track is not None else None,
+        )
+        self.paths.append(path)
+        self.frame_count += len(frames)
+        logger.info(f"Saved chain segment to {path}")
+
+
+def _decode_segment(path):
+    """Read a segment file back as a uint8 (frames, height, width, 3) tensor."""
+    from ..media import decode_rgb_frames
+
+    return torch.from_numpy(numpy.stack(decode_rgb_frames(path), axis=0))
+
+
+def spilled_frames(config, spill):
+    """A spilled chain's frames, replayed lazily from its segment files.
+
+    match_audio overshoots by design - the tail trim happens as the lazy
+    frames replay, so the files themselves stay whole."""
+    return SegmentedFrames(
+        spill.paths,
+        config.total_frames if config.source_audio is not None else None,
+        config.keep_segments,
+        spill.frame_count,
+    )

From e39ef9d75f77b556d445a575d980ab630a22ff95 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:28:30 -0600
Subject: [PATCH 66/74] refactor(ui): ratchet - chain summary moves to
 flowChain.ts

flow.ts was over the 600-line ceiling and chainSummary at complexity 16.
The chain group (ChainSummary, the engine defaults, the summary) is its
own module, and the summary is split into a block reader, one helper per
continuity mode, the seam label and the count, none above 10. Its tests
move with it and run against chainSummary directly; flow.test.ts keeps
the one that the node carries the summary.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 ui/src/lib/flow.test.ts      |  83 +++++----------------------
 ui/src/lib/flow.ts           |  92 +-----------------------------
 ui/src/lib/flowChain.test.ts |  86 ++++++++++++++++++++++++++++
 ui/src/lib/flowChain.ts      | 107 +++++++++++++++++++++++++++++++++++
 4 files changed, 209 insertions(+), 159 deletions(-)
 create mode 100644 ui/src/lib/flowChain.test.ts
 create mode 100644 ui/src/lib/flowChain.ts

diff --git a/ui/src/lib/flow.test.ts b/ui/src/lib/flow.test.ts
index 56ad27e5..e3f5d067 100644
--- a/ui/src/lib/flow.test.ts
+++ b/ui/src/lib/flow.test.ts
@@ -441,79 +441,22 @@ describe('the shared reference cases', () => {
 })
 
 describe('chained segments', () => {
-  const chainStep = (chain: Record<string, unknown>) => ({
-    name: 'clip',
-    pipeline: { configuration: { component_type: 'ModularPipeline' }, chain },
-  })
-
-  it("reads a declared segment count and the seam's last-frame bookkeeping", () => {
-    // The chained-segments template's shape: the count and continuity are
-    // variables, which a realized workflow always carries
+  // The summary itself is flowChain.test.ts; here, that the node carries it
+  it('puts the chain summary on the node, null on a step without one', () => {
     const wf = {
-      variables: { segments: 3, continuity: 'last_frame', anchor: 'segment_1' },
       steps: [
-        chainStep({
-          segments: 'variable:segments',
-          continuity: 'variable:continuity',
-          anchor: 'variable:anchor',
-          trim_frames: 2,
-          crossfade_ms: 80,
-        }),
+        {
+          name: 'clip',
+          pipeline: {
+            configuration: { component_type: 'ModularPipeline' },
+            chain: { segments: 2 },
+          },
+        },
+        step('plain', {}),
       ],
     }
-    expect(dataFlowGraph(wf).nodes[0].chain).toEqual({
-      segments: 3,
-      fromAudio: false,
-      seam: 'last frame · trim 2 · 80ms',
-      anchor: true,
-    })
-  })
-
-  it('falls back to the engine defaults the block leaves out', () => {
-    const wf = { steps: [chainStep({ segments: 2 })] }
-    expect(dataFlowGraph(wf).nodes[0].chain).toEqual({
-      segments: 2,
-      fromAudio: false,
-      seam: 'last frame · trim 1 · 75ms',
-      anchor: false,
-    })
-  })
-
-  it('names what a guide or a video reference carries across the seam', () => {
-    const seam = (chain: Record<string, unknown>) =>
-      dataFlowGraph({ steps: [chainStep({ segments: 2, ...chain })] }).nodes[0]
-        .chain!.seam
-    expect(seam({ continuity: 'guide' })).toBe('guide · 22 frames')
-    expect(seam({ continuity: 'guide', guide_frames: 39 })).toBe(
-      'guide · 39 frames',
-    )
-    expect(seam({ continuity: 'last_segment' })).toBe('video reference')
-    expect(
-      seam({
-        continuity: 'last_segment',
-        carry_frames: 24,
-        carry_audio: false,
-      }),
-    ).toBe('video reference · 24 frames · no audio')
-    expect(seam({ continuity: 'variable:unset' })).toBe('carry')
-  })
-
-  it('leaves the count unknown when the length comes from audio or an unset variable', () => {
-    const fromAudio = dataFlowGraph({
-      steps: [chainStep({ match_audio: true })],
-    }).nodes[0].chain!
-    expect(fromAudio.segments).toBeNull()
-    expect(fromAudio.fromAudio).toBe(true)
-    const unset = dataFlowGraph({
-      steps: [chainStep({ segments: 'variable:missing' })],
-    }).nodes[0].chain!
-    expect(unset.segments).toBeNull()
-    expect(unset.fromAudio).toBe(false)
-  })
-
-  it('is null on a step with no chain', () => {
-    expect(dataFlowGraph({ steps: [step('plain', {})] }).nodes[0].chain).toBe(
-      null,
-    )
+    const [clip, plain] = dataFlowGraph(wf).nodes
+    expect(clip.chain?.segments).toBe(2)
+    expect(plain.chain).toBeNull()
   })
 })
diff --git a/ui/src/lib/flow.ts b/ui/src/lib/flow.ts
index 41e7b898..6dae2781 100644
--- a/ui/src/lib/flow.ts
+++ b/ui/src/lib/flow.ts
@@ -16,6 +16,7 @@ import {
   reference,
   referenceName,
 } from './references'
+import { chainSummary, type ChainSummary } from './flowChain'
 
 export interface StepFlow {
   name: string
@@ -230,28 +231,11 @@ export interface FlowNode {
    * names the run's members carry after the `@`. Null when there is no
    * for_each or the list cannot be read statically. */
   members: string[] | null
-  /** What the step's pipeline `chain` block runs, or null without one. */
+  /** What the step's pipeline `chain` block runs, or null without one
+   * (flowChain.ts). */
   chain: ChainSummary | null
 }
 
-/** A chained pipeline step, read off its `chain` block: the pipeline runs
- * once per segment inside the one step, carrying continuity across each
- * seam, and the segments are stitched into one video
- * (dw/pipeline_processors/chain.py). */
-export interface ChainSummary {
-  /** The segment count, when the definition gives it - a literal or a
-   * declared variable, which a realized workflow always holds. Null under
-   * `match_audio`, or for a variable left unset. */
-  segments: number | null
-  /** True when the audio's length decides the count at run time. */
-  fromAudio: boolean
-  /** What each seam carries into the next segment, in a few words. */
-  seam: string
-  /** True under `anchor: "segment_1"`: segment 1's latents anchor every
-   * later segment's (#1166). */
-  anchor: boolean
-}
-
 export interface FlowEdge {
   from: string
   to: string
@@ -322,76 +306,6 @@ function forEachMembers(
   )
 }
 
-// The chain block's engine defaults (dw/workflow_schema.json $defs/chain)
-const CHAIN_DEFAULTS: Record<string, unknown> = {
-  continuity: 'last_frame',
-  trim_frames: 1,
-  crossfade_ms: 75,
-  guide_frames: 22,
-  carry_audio: true,
-  anchor: 'none',
-}
-
-/** The step's `chain` block summarized for the graph, or null. A value
- * may be a `variable:`, read from the declared variables - undefined when
- * it names none, or one left null. */
-function chainSummary(
-  workflow: Record<string, any>,
-  step: Record<string, any>,
-): ChainSummary | null {
-  const chain = step.pipeline?.chain
-  if (chain === null || typeof chain !== 'object') return null
-  const read = (key: string): unknown => {
-    const value = key in chain ? chain[key] : CHAIN_DEFAULTS[key]
-    const name = referenceName(value, VARIABLE)
-    if (name === null) return value
-    return workflow.variables?.[name] ?? undefined
-  }
-  const segments = read('segments')
-  const parts: Array<string | null> = []
-  switch (read('continuity')) {
-    case 'last_frame': {
-      const trim = read('trim_frames')
-      const fade = read('crossfade_ms')
-      parts.push(
-        'last frame',
-        typeof trim === 'number' ? `trim ${trim}` : null,
-        typeof fade === 'number' ? `${fade}ms` : null,
-      )
-      break
-    }
-    case 'guide': {
-      const frames = read('guide_frames')
-      parts.push(
-        'guide',
-        typeof frames === 'number' ? `${frames} frames` : null,
-      )
-      break
-    }
-    case 'last_segment': {
-      const frames = read('carry_frames')
-      parts.push(
-        'video reference',
-        typeof frames === 'number' ? `${frames} frames` : null,
-      )
-      break
-    }
-    default:
-      parts.push('carry')
-  }
-  if (read('continuity') !== 'last_frame' && read('carry_audio') === false)
-    parts.push('no audio')
-  return {
-    segments:
-      typeof segments === 'number' && segments >= 1 && !chain.match_audio
-        ? segments
-        : null,
-    fromAudio: chain.match_audio === true,
-    seam: parts.filter(Boolean).join(' · '),
-    anchor: read('anchor') === 'segment_1',
-  }
-}
-
 /** The read-only data-flow view's graph: one node per step, one edge per
  * `previous_result:<step>` reference (labeled with the attribute that
  * carries it), entry points flagged, and fan-in points - steps combining
diff --git a/ui/src/lib/flowChain.test.ts b/ui/src/lib/flowChain.test.ts
new file mode 100644
index 00000000..43b2bee0
--- /dev/null
+++ b/ui/src/lib/flowChain.test.ts
@@ -0,0 +1,86 @@
+import { describe, expect, it } from 'vitest'
+import { chainSummary } from './flowChain'
+
+describe('chained segments', () => {
+  const chainStep = (chain: Record<string, unknown>) => ({
+    name: 'clip',
+    pipeline: { configuration: { component_type: 'ModularPipeline' }, chain },
+  })
+  const summary = (
+    chain: Record<string, unknown>,
+    variables: Record<string, unknown> = {},
+  ) => chainSummary({ variables }, chainStep(chain))
+
+  it("reads a declared segment count and the seam's last-frame bookkeeping", () => {
+    // The chained-segments template's shape: the count and continuity are
+    // variables, which a realized workflow always carries
+    expect(
+      summary(
+        {
+          segments: 'variable:segments',
+          continuity: 'variable:continuity',
+          anchor: 'variable:anchor',
+          trim_frames: 2,
+          crossfade_ms: 80,
+        },
+        { segments: 3, continuity: 'last_frame', anchor: 'segment_1' },
+      ),
+    ).toEqual({
+      segments: 3,
+      fromAudio: false,
+      seam: 'last frame · trim 2 · 80ms',
+      anchor: true,
+    })
+  })
+
+  it('falls back to the engine defaults the block leaves out', () => {
+    expect(chainSummary({}, chainStep({ segments: 2 }))).toEqual({
+      segments: 2,
+      fromAudio: false,
+      seam: 'last frame · trim 1 · 75ms',
+      anchor: false,
+    })
+  })
+
+  it('names what a guide or a video reference carries across the seam', () => {
+    const seam = (chain: Record<string, unknown>) =>
+      summary({ segments: 2, ...chain })!.seam
+    expect(seam({ continuity: 'guide' })).toBe('guide · 22 frames')
+    expect(seam({ continuity: 'guide', guide_frames: 39 })).toBe(
+      'guide · 39 frames',
+    )
+    expect(seam({ continuity: 'last_segment' })).toBe('video reference')
+    expect(
+      seam({
+        continuity: 'last_segment',
+        carry_frames: 24,
+        carry_audio: false,
+      }),
+    ).toBe('video reference · 24 frames · no audio')
+    expect(seam({ continuity: 'variable:unset' })).toBe('carry')
+  })
+
+  it('keeps the audio under a last-frame seam, whatever carry_audio says', () => {
+    expect(summary({ segments: 2, carry_audio: false })!.seam).toBe(
+      'last frame · trim 1 · 75ms',
+    )
+  })
+
+  it('leaves the count unknown when the length comes from audio or an unset variable', () => {
+    const fromAudio = summary({ match_audio: true })!
+    expect(fromAudio.segments).toBeNull()
+    expect(fromAudio.fromAudio).toBe(true)
+    const unset = summary({ segments: 'variable:missing' })!
+    expect(unset.segments).toBeNull()
+    expect(unset.fromAudio).toBe(false)
+  })
+
+  it('is null on a step with no chain', () => {
+    expect(
+      chainSummary({}, { name: 'plain', task: { command: 'noop' } }),
+    ).toBeNull()
+    expect(
+      chainSummary({}, { name: 'plain', pipeline: { chain: null } }),
+    ).toBeNull()
+  })
+})
diff --git a/ui/src/lib/flowChain.ts b/ui/src/lib/flowChain.ts
new file mode 100644
index 00000000..4d3640be
--- /dev/null
+++ b/ui/src/lib/flowChain.ts
@@ -0,0 +1,107 @@
+/** A chained pipeline step's `chain` block, summarized for the data-flow
+ * graph (flow.ts). The pipeline runs once per segment inside the one step,
+ * carrying continuity across each seam, and the segments are stitched into
+ * one video (dw/pipeline_processors/chain.py). */
+
+import { VARIABLE, referenceName } from './references'
+
+export interface ChainSummary {
+  /** The segment count, when the definition gives it - a literal or a
+   * declared variable, which a realized workflow always holds. Null under
+   * `match_audio`, or for a variable left unset. */
+  segments: number | null
+  /** True when the audio's length decides the count at run time. */
+  fromAudio: boolean
+  /** What each seam carries into the next segment, in a few words. */
+  seam: string
+  /** True under `anchor: "segment_1"`: segment 1's latents anchor every
+   * later segment's (#1166). */
+  anchor: boolean
+}
+
+// The chain block's engine defaults (dw/workflow_schema.json $defs/chain)
+const CHAIN_DEFAULTS: Record<string, unknown> = {
+  continuity: 'last_frame',
+  trim_frames: 1,
+  crossfade_ms: 75,
+  guide_frames: 22,
+  carry_audio: true,
+  anchor: 'none',
+}
+
+/** A chain value by key, the engine default standing in for one the block
+ * leaves out. */
+type ChainRead = (key: string) => unknown
+
+/** The block's reader. A value may be a `variable:`, read from the declared
+ * variables - undefined when it names none, or one left null. */
+function chainReader(
+  workflow: Record<string, any>,
+  chain: Record<string, unknown>,
+): ChainRead {
+  return (key) => {
+    const value = key in chain ? chain[key] : CHAIN_DEFAULTS[key]
+    const name = referenceName(value, VARIABLE)
+    if (name === null) return value
+    return workflow.variables?.[name] ?? undefined
+  }
+}
+
+/** `label`, then the frame count when the block gives a number. */
+function withFrames(label: string, frames: unknown): string[] {
+  return typeof frames === 'number' ? [label, `${frames} frames`] : [label]
+}
+
+function lastFrameSeam(read: ChainRead): string[] {
+  const trim = read('trim_frames')
+  const fade = read('crossfade_ms')
+  return [
+    'last frame',
+    typeof trim === 'number' ? `trim ${trim}` : null,
+    typeof fade === 'number' ? `${fade}ms` : null,
+  ].filter((part): part is string => part !== null)
+}
+
+/** What the continuity mode carries across the seam, as label words. */
+function continuityParts(read: ChainRead): string[] {
+  switch (read('continuity')) {
+    case 'last_frame':
+      return lastFrameSeam(read)
+    case 'guide':
+      return withFrames('guide', read('guide_frames'))
+    case 'last_segment':
+      return withFrames('video reference', read('carry_frames'))
+    default:
+      return ['carry']
+  }
+}
+
+function seamLabel(read: ChainRead): string {
+  const parts = continuityParts(read)
+  if (read('continuity') !== 'last_frame' && read('carry_audio') === false)
+    parts.push('no audio')
+  return parts.join(' · ')
+}
+
+/** The declared count, or null when it is unknown here or the audio decides. */
+function segmentCount(segments: unknown, matchAudio: unknown): number | null {
+  return typeof segments === 'number' && segments >= 1 && !matchAudio
+    ? segments
+    : null
+}
+
+/** The step's `chain` block summarized for the graph, or null without one. */
+export function chainSummary(
+  workflow: Record<string, any>,
+  step: Record<string, any>,
+): ChainSummary | null {
+  const chain = step.pipeline?.chain
+  if (chain === null || typeof chain !== 'object') return null
+  const read = chainReader(workflow, chain)
+  return {
+    segments: segmentCount(read('segments'), chain.match_audio),
+    fromAudio: chain.match_audio === true,
+    seam: seamLabel(read),
+    anchor: read('anchor') === 'segment_1',
+  }
+}

From b93c7f248ee9750866d1cabd105da4301fc70785 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:28:30 -0600
Subject: [PATCH 67/74] refactor(ui): ratchet - segment chips move to
 FlowSegments.svelte

FlowView.svelte was over the 600-line ceiling. The chained step's
segment chips - the summary line, the chips, the seams and the anchor
tag, with the segment-state rule and their styles - are a child drawn in
the SVG namespace inside the node's <g>, so the DOM FlowView.test.ts
reads is unchanged. The design rule allows --live in it, as in FlowView.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 ui/scripts/design-rules.test.ts       |   5 +-
 ui/src/lib/editor/FlowSegments.svelte | 128 ++++++++++++++++++++++++++
 ui/src/lib/editor/FlowView.svelte     | 105 +++------------------
 3 files changed, 144 insertions(+), 94 deletions(-)
 create mode 100644 ui/src/lib/editor/FlowSegments.svelte

diff --git a/ui/scripts/design-rules.test.ts b/ui/scripts/design-rules.test.ts
index b770d5da..c78fc977 100644
--- a/ui/scripts/design-rules.test.ts
+++ b/ui/scripts/design-rules.test.ts
@@ -11,12 +11,13 @@ const UI = join(dirname(fileURLToPath(import.meta.url)), '..')
 
 // The places --live may appear: the header's running link and VRAM
 // pressure, a running job's row and status chip, the job page's progress
-// bar and current step, the flow view's active step and its legend's key
-// to it, and a model download in flight
+// bar and current step, the flow view's active step and segment and its
+// legend's key to them, and a model download in flight
 const LIVE_FILES = [
   'src/App.svelte',
   'src/app.css',
   'src/lib/editor/FlowLegend.svelte',
+  'src/lib/editor/FlowSegments.svelte',
   'src/lib/editor/FlowView.svelte',
   'src/lib/job/JobProgress.svelte',
   'src/lib/pages/JobsPage.svelte',
diff --git a/ui/src/lib/editor/FlowSegments.svelte b/ui/src/lib/editor/FlowSegments.svelte
new file mode 100644
index 00000000..f571d861
--- /dev/null
+++ b/ui/src/lib/editor/FlowSegments.svelte
@@ -0,0 +1,128 @@
+<svelte:options namespace="svg" />
+
+<!-- The segments a chained step runs, drawn inside its node box: a summary
+     line, one chip per segment in order, each seam's carry written between
+     them. Drawn in the SVG namespace, so it sits inside FlowView's <g>. -->
+<script lang="ts">
+  import type { FlowNode } from '../flow'
+  import {
+    BOX_W,
+    CHAIN_SUMMARY_Y,
+    CHIP_H,
+    SEAM_CHARS,
+    SEGMENT_STEP,
+    SEGMENT_TOP,
+    fit,
+    segmentChips,
+  } from './flowLayout'
+
+  let {
+    node,
+    stepState = '',
+    activeSegment = undefined,
+  }: {
+    /** A node with a `chain` - the caller draws nothing else through here. */
+    node: FlowNode
+    /** The step's run state: 'active', 'failed', 'done', 'pending' or ''. */
+    stepState?: string
+    /** The segment, 1-based, the run is on or last ran - its generating
+     * phases (runstate.ts chainSegments). */
+    activeSegment?: number
+  } = $props()
+
+  const label = $derived.by(() => {
+    if (!node.chain) return ''
+    if (node.chain.fromAudio) return 'chain · length from audio'
+    const n = node.chain.segments
+    return n === null ? 'chain' : `chain · ${n} segment${n === 1 ? '' : 's'}`
+  })
+  const chips = $derived(segmentChips(node))
+
+  // A segment's state follows its step's: every segment of a finished step
+  // is done, and on a running or failed one, those before the segment it
+  // is on are done and that one carries the step's state
+  const segmentStateOf = $derived((segment: number) => {
+    if (stepState === 'done') return 'done'
+    if (stepState !== 'active' && stepState !== 'failed') return ''
+    if (activeSegment === undefined) return ''
+    return segment < activeSegment
+      ? 'done'
+      : segment === activeSegment
+        ? stepState
+        : ''
+  })
+</script>
+
+<text x="11" y={CHAIN_SUMMARY_Y} class="chainsummary">{label}</text>
+{#each Array.from({ length: chips }, (_, i) => i) as i (i)}
+  {@const y = SEGMENT_TOP + i * SEGMENT_STEP}
+  {@const state = segmentStateOf(i + 1)}
+  <g
+    class="segment"
+    class:done={state === 'done'}
+    class:active={state === 'active'}
+    class:failed={state === 'failed'}
+    aria-label={`segment ${i + 1}${state ? ', ' + state : ''}`}
+  >
+    <rect x="10" {y} width={BOX_W - 20} height={CHIP_H} rx="4" class="chip" />
+    <text x="15" y={y + 11} class="chiplabel">segment {i + 1}</text>
+    {#if i === 0 && node.chain?.anchor}
+      <g>
+        <title
+          >segment 1's latents anchor every later segment's (chain anchor:
+          segment_1)</title
+        >
+        <text x={BOX_W - 15} y={y + 11} class="anchortag" text-anchor="end"
+          >anchor</text
+        >
+      </g>
+    {/if}
+  </g>
+  {#if i < chips - 1 && node.chain}
+    <text x="15" y={y + CHIP_H + 10} class="seamlabel"
+      >{#if `↓ ${node.chain.seam}`.length > SEAM_CHARS}<title
+          >{node.chain.seam}</title
+        >{/if}{fit(`↓ ${node.chain.seam}`, SEAM_CHARS, 'head')}</text
+    >
+  {/if}
+{/each}
+
+<style>
+  /* Machine state on the chips' edges, as on the nodes: done green, running
+     the safelight amber (FlowView.svelte's .member chips match). */
+  .segment .chip {
+    fill: var(--panel-2);
+    stroke: var(--line);
+    stroke-width: 1;
+  }
+  .segment.done .chip {
+    stroke: var(--good);
+    stroke-width: 1.5;
+  }
+  .segment.failed .chip {
+    stroke: var(--bad);
+    stroke-width: 1.5;
+  }
+  .segment.active .chip {
+    stroke: var(--live);
+    stroke-width: 1.5;
+    animation: dw-pulse 1.6s ease-in-out infinite;
+  }
+  @media (prefers-reduced-motion: reduce) {
+    .segment.active .chip {
+      animation: none;
+    }
+  }
+  .chiplabel {
+    font-size: 9px;
+    fill: var(--ink);
+    font-family: var(--font-mono);
+  }
+  .chainsummary,
+  .seamlabel,
+  .anchortag {
+    font-size: 9px;
+    fill: var(--muted);
+    font-family: var(--font-mono);
+  }
+</style>
diff --git a/ui/src/lib/editor/FlowView.svelte b/ui/src/lib/editor/FlowView.svelte
index 7b74a931..d399bf46 100644
--- a/ui/src/lib/editor/FlowView.svelte
+++ b/ui/src/lib/editor/FlowView.svelte
@@ -2,10 +2,10 @@
   import { dataFlowGraph, type FlowNode } from '../flow'
   import { formatDuration } from '../format'
   import FlowLegend from './FlowLegend.svelte'
+  import FlowSegments from './FlowSegments.svelte'
   import {
     BOX_H,
     BOX_W,
-    CHAIN_SUMMARY_Y,
     CHIP_CHARS,
     CHIP_CHARS_TIMED,
     CHIP_H,
@@ -14,13 +14,9 @@
     DETAIL_CHARS,
     NAME_CHARS,
     NAME_CHARS_WITH_TAG,
-    SEAM_CHARS,
-    SEGMENT_STEP,
-    SEGMENT_TOP,
     fit,
     heightOf,
     layoutGraph,
-    segmentChips,
   } from './flowLayout'
 
   let {
@@ -96,18 +92,6 @@
           : '',
   )
 
-  // A segment's state follows its step's: every segment of a finished step
-  // is done, and on a running or failed one, those before the segment it
-  // is on are done and that one carries the step's state
-  const segmentStateOf = $derived((node: string, segment: number) => {
-    const state = stateOf(node)
-    if (state === 'done') return 'done'
-    if (state !== 'active' && state !== 'failed') return ''
-    const on = activeSegments[node]
-    if (on === undefined) return ''
-    return segment < on ? 'done' : segment === on ? state : ''
-  })
-
   const graph = $derived(dataFlowGraph(workflow))
   const hasForEach = $derived(graph.nodes.some((node) => node.forEach))
   const hasChain = $derived(
@@ -124,12 +108,6 @@
       : undefined,
   )
 
-  function chainLabel(node: FlowNode): string {
-    if (!node.chain || node.forEach) return ''
-    if (node.chain.fromAudio) return 'chain · length from audio'
-    const n = node.chain.segments
-    return n === null ? 'chain' : `chain · ${n} segment${n === 1 ? '' : 's'}`
-  }
   const canSelect = (name: string) => !!onselect && (selectable?.(name) ?? true)
 
   /** The parts of a node's labels that did not fit, in full, for its tooltip. */
@@ -364,60 +342,11 @@
                   >for_each</text
                 >
               {:else if node.chain}
-                <!-- The segments the one step runs, in order, each seam's
-                     carry written between them; the run's segment from its
-                     generating phases -->
-                <text x="11" y={CHAIN_SUMMARY_Y} class="chainsummary"
-                  >{chainLabel(node)}</text
-                >
-                {#each Array.from({ length: segmentChips(node) }, (_, i) => i) as i (i)}
-                  {@const y = SEGMENT_TOP + i * SEGMENT_STEP}
-                  {@const state = segmentStateOf(node.name, i + 1)}
-                  <g
-                    class="segment"
-                    class:done={state === 'done'}
-                    class:active={state === 'active'}
-                    class:failed={state === 'failed'}
-                    aria-label={`segment ${i + 1}${state ? ', ' + state : ''}`}
-                  >
-                    <rect
-                      x="10"
-                      {y}
-                      width={BOX_W - 20}
-                      height={CHIP_H}
-                      rx="4"
-                      class="chip"
-                    />
-                    <text x="15" y={y + 11} class="chiplabel"
-                      >segment {i + 1}</text
-                    >
-                    {#if i === 0 && node.chain.anchor}
-                      <g>
-                        <title
-                          >segment 1's latents anchor every later segment's
-                          (chain anchor: segment_1)</title
-                        >
-                        <text
-                          x={BOX_W - 15}
-                          y={y + 11}
-                          class="anchortag"
-                          text-anchor="end">anchor</text
-                        >
-                      </g>
-                    {/if}
-                  </g>
-                  {#if i < segmentChips(node) - 1}
-                    <text x="15" y={y + CHIP_H + 10} class="seamlabel"
-                      >{#if `↓ ${node.chain.seam}`.length > SEAM_CHARS}<title
-                          >{node.chain.seam}</title
-                        >{/if}{fit(
-                        `↓ ${node.chain.seam}`,
-                        SEAM_CHARS,
-                        'head',
-                      )}</text
-                    >
-                  {/if}
-                {/each}
+                <FlowSegments
+                  {node}
+                  stepState={stateOf(node.name)}
+                  activeSegment={activeSegments[node.name]}
+                />
               {/if}
             </g>
             {#if fanIn}
@@ -604,32 +533,27 @@
   }
   /* The entries a for_each step runs, inset beneath its header. Machine
      state on their edges, as on the nodes: done green, running the
-     safelight amber. */
-  .member .chip,
-  .segment .chip {
+     safelight amber. A chained step's segment chips are FlowSegments. */
+  .member .chip {
     fill: var(--panel-2);
     stroke: var(--line);
     stroke-width: 1;
   }
-  .member.done .chip,
-  .segment.done .chip {
+  .member.done .chip {
     stroke: var(--good);
     stroke-width: 1.5;
   }
-  .member.failed .chip,
-  .segment.failed .chip {
+  .member.failed .chip {
     stroke: var(--bad);
     stroke-width: 1.5;
   }
-  .member.active .chip,
-  .segment.active .chip {
+  .member.active .chip {
     stroke: var(--live);
     stroke-width: 1.5;
     animation: dw-pulse 1.6s ease-in-out infinite;
   }
   @media (prefers-reduced-motion: reduce) {
-    .member.active .chip,
-    .segment.active .chip {
+    .member.active .chip {
       animation: none;
     }
   }
@@ -638,10 +562,7 @@
     fill: var(--ink);
     font-family: var(--font-mono);
   }
-  .membersunknown,
-  .chainsummary,
-  .seamlabel,
-  .anchortag {
+  .membersunknown {
     font-size: 9px;
     fill: var(--muted);
     font-family: var(--font-mono);

From a8b937ee56f225497582f3d34dca7a8876729e53 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:28:30 -0600
Subject: [PATCH 68/74] fix(ui): workspace refetch failure keeps the listing

checkWorkspaceExists refetches the listing once for a name the cache
lacks. The catch wiped a listing that had landed and cleared `listed`,
so a transient failure on that refetch emptied the sidebar and the
missing name was never redirected. A failed refetch now keeps the names
and calls the name missing against them; only the first fetch failing
falls back to the default alone. The test drives applyRouteWorkspace
directly: the routers earlier tests imported still listen on hashchange
and would take the rejection.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 ui/src/lib/workspace.svelte.test.ts | 39 +++++++++++++++++++++++++++++
 ui/src/lib/workspace.svelte.ts      | 27 ++++++++++++++------
 2 files changed, 58 insertions(+), 8 deletions(-)

diff --git a/ui/src/lib/workspace.svelte.test.ts b/ui/src/lib/workspace.svelte.test.ts
index ad3759cb..d053bc03 100644
--- a/ui/src/lib/workspace.svelte.test.ts
+++ b/ui/src/lib/workspace.svelte.test.ts
@@ -182,3 +182,42 @@ it('a failed listing does not read as every other workspace being gone', async (
   expect(workspace.current).toBe('studio')
   expect(errorToast).not.toHaveBeenCalled()
 })
+
+it('a refetch that fails keeps the listing and still redirects the missing name', async () => {
+  // Driven through applyRouteWorkspace rather than a hashchange: the routers
+  // earlier tests imported still listen, and one would take the rejection
+  const { applyRouteWorkspace, loadWorkspaces, workspace } =
+    await import('./workspace.svelte')
+  applyRouteWorkspace({
+    kind: 'ws',
+    workspace: 'studio',
+    section: 'gallery',
+    rest: [],
+  })
+  await loadWorkspaces()
+  expect(workspace.names).toEqual(['default', 'studio'])
+  // The refetch a name the cache lacks triggers hits a server hiccup
+  listWorkspaces.mockRejectedValueOnce(new Error('down'))
+  applyRouteWorkspace({
+    kind: 'ws',
+    workspace: 'typo',
+    section: 'gallery',
+    rest: [],
+  })
+  await loadWorkspaces()
+  expect(workspace.names).toEqual(['default', 'studio'])
+  expect(location.hash).toBe('#/ws/default/overview')
+  expect(workspace.current).toBe('default')
+  expect(errorToast).toHaveBeenCalledWith(expect.stringContaining('typo'))
+})
+
+it('a first listing that fails falls back to the default alone, until one lands', async () => {
+  listWorkspaces.mockRejectedValueOnce(new Error('down'))
+  const { invalidateWorkspaces, loadWorkspaces, workspace } =
+    await import('./workspace.svelte')
+  await loadWorkspaces()
+  expect(workspace.names).toEqual(['default'])
+  invalidateWorkspaces()
+  await loadWorkspaces()
+  expect(workspace.names).toEqual(['default', 'studio'])
+})
diff --git a/ui/src/lib/workspace.svelte.ts b/ui/src/lib/workspace.svelte.ts
index 817477ed..7d94606f 100644
--- a/ui/src/lib/workspace.svelte.ts
+++ b/ui/src/lib/workspace.svelte.ts
@@ -83,6 +83,17 @@ export function checkWorkspaceExists(view: RouteView | null): void {
   }
 }
 
+/** The check with `names` counted as fetched for it - a name it lacks is
+ * missing, not refetched again. */
+function checkAgainstFreshListing(): void {
+  fresh = true
+  try {
+    checkWorkspaceExists(lastView)
+  } finally {
+    fresh = false
+  }
+}
+
 // The in-flight/completed listing, shared across every caller - the sidebar
 // and any page that needs names call loadWorkspaces() and would otherwise
 // refetch a listing that has not changed.
@@ -109,15 +120,15 @@ export function loadWorkspaces(): Promise<void> {
         // A route naming a workspace that has gone away (deleted elsewhere,
         // or a stale bookmark) is caught here for the route the app opened
         // on; `applyRouteWorkspace` catches every one after
-        fresh = true
-        try {
-          checkWorkspaceExists(lastView)
-        } finally {
-          fresh = false
-        }
+        checkAgainstFreshListing()
       } catch {
-        listed = false
-        workspace.names = [DEFAULT_WORKSPACE]
+        // The first listing failing leaves the fallback. A refetch failing
+        // (the one `checkWorkspaceExists` runs for a name the cache lacks)
+        // keeps the listing that landed: a server hiccup does not empty the
+        // sidebar, and the name that listing lacks is still called missing,
+        // since that listing is the only answer there is.
+        if (listed) checkAgainstFreshListing()
+        else workspace.names = [DEFAULT_WORKSPACE]
       }
     })()
   }

From c0fc44eddf3b2ab526b45e9e943d999681fe1940 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:30:46 -0600
Subject: [PATCH 69/74] refactor(components): ratchet - load_component hands
 the post-load assembly to _assemble_loaded

load_component had passed the 150-line function limit. Registering the
block configs and reused components, pulling a modular pipeline's weights,
dropping the declared-gone blocks and inserting the MiniMax-H3 blocks is one
step - finishing a loaded component before placement - and is now
`_assemble_loaded`, in the same module, so every patch target stays put.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/pipeline_processors/components.py | 108 ++++++++++++++++-----------
 1 file changed, 64 insertions(+), 44 deletions(-)

diff --git a/dw/pipeline_processors/components.py b/dw/pipeline_processors/components.py
index 2c8e6f0c..3e76a61d 100644
--- a/dw/pipeline_processors/components.py
+++ b/dw/pipeline_processors/components.py
@@ -724,50 +724,13 @@ def from_pretrained():
                 logger.info(f"Creating new {component_name}")
                 component = component_type(**from_pretrained_arguments)
 
-            # Register the shared components before anything is pulled, so the
-            # weights an earlier step already loaded and quantized are the ones
-            # this step runs on rather than a second copy of them. The block
-            # configs go in the same call - update_components takes both
-            update_arguments = get_block_configs(configuration, component)
-            if reused_components and takes_components_after_load:
-                logger.info(
-                    f"Reusing {', '.join(reused_components)} from an earlier step"
-                )
-                update_arguments.update(reused_components)
-            if update_arguments:
-                component.update_components(**update_arguments)
-
-            # Modular pipelines load only their config in from_pretrained - the component
-            # weights are pulled separately by load_components()
-            load_components_arguments = get_load_components_arguments(configuration)
-            if load_components_arguments is not None:
-                if not has_method(component, "load_components"):
-                    raise ValueError(
-                        f"load_components is only supported on modular pipelines, "
-                        f"{component_type.__name__} does not have it"
-                    )
-                logger.info(f"Loading components for {component_name}")
-                # Before pruning, offload and sdnq_optimize, so a cached entry
-                # is the whole component as quantized (dw/quantized_cache.py)
-                quantized_cache.load_components(component, load_components_arguments)
-
-            # Blocks the workflow declares gone (Klein's prompt_upsampler)
-            remove_pipeline_blocks(configuration, component)
-
-            # MiniMax-H3 takes `hold_audio` on every core-denoise workflow; a
-            # no-op on any other pipeline (dw/pipeline_processors/h3_hold.py)
-            insert_audio_hold(component)
-            # ...and `guides` on t2va and fl2va
-            insert_guides(component)
-            # ...and `latent_anchor` on t2va and fl2va (h3_anchor.py)
-            insert_latent_anchor(component)
-            # ...and `source_video` with a mask, a regional edit (h3_mask.py)
-            insert_mask_blocks(component)
-            # ...and the decode-free graph a `refine_tiles` step runs per tile
-            from .h3_tiles import attach_tile_blocks
-
-            if core_denoise_sequences(component):
-                attach_tile_blocks(component)
+            _assemble_loaded(
+                component_name,
+                component_type,
+                configuration,
+                component,
+                reused_components if takes_components_after_load else {},
+            )
 
         if defer_placement:
             # The caller places this itself, once it has finished loading the
@@ -793,6 +756,63 @@ def from_pretrained():
         raise
 
 
+def _assemble_loaded(
+    component_name, component_type, configuration, component, reused_components
+):
+    """Finish a freshly loaded component in place, before placement: register
+    the block configs and the components reused from an earlier step, pull a
+    modular pipeline's weights, drop the blocks the workflow declares gone and
+    put the MiniMax-H3 blocks in.
+
+    Args:
+        reused_components: The shared components a modular pipeline registers
+            after load (`update_components`); empty for a pipeline that took
+            them in its constructor
+    """
+    # Register the shared components before anything is pulled, so the
+    # weights an earlier step already loaded and quantized are the ones
+    # this step runs on rather than a second copy of them. The block
+    # configs go in the same call - update_components takes both
+    update_arguments = get_block_configs(configuration, component)
+    if reused_components:
+        logger.info(f"Reusing {', '.join(reused_components)} from an earlier step")
+        update_arguments.update(reused_components)
+    if update_arguments:
+        component.update_components(**update_arguments)
+
+    # Modular pipelines load only their config in from_pretrained - the component
+    # weights are pulled separately by load_components()
+    load_components_arguments = get_load_components_arguments(configuration)
+    if load_components_arguments is not None:
+        if not has_method(component, "load_components"):
+            raise ValueError(
+                f"load_components is only supported on modular pipelines, "
+                f"{component_type.__name__} does not have it"
+            )
+        logger.info(f"Loading components for {component_name}")
+        # Before pruning, offload and sdnq_optimize, so a cached entry
+        # is the whole component as quantized (dw/quantized_cache.py)
+        quantized_cache.load_components(component, load_components_arguments)
+
+    # Blocks the workflow declares gone (Klein's prompt_upsampler)
+    remove_pipeline_blocks(configuration, component)
+
+    # MiniMax-H3 takes `hold_audio` on every core-denoise workflow; a
+    # no-op on any other pipeline (dw/pipeline_processors/h3_hold.py)
+    insert_audio_hold(component)
+    # ...and `guides` on t2va and fl2va
+    insert_guides(component)
+    # ...and `latent_anchor` on t2va and fl2va (h3_anchor.py)
+    insert_latent_anchor(component)
+    # ...and `source_video` with a mask, a regional edit (h3_mask.py)
+    insert_mask_blocks(component)
+    # ...and the decode-free graph a `refine_tiles` step runs per tile
+    from .h3_tiles import attach_tile_blocks
+
+    if core_denoise_sequences(component):
+        attach_tile_blocks(component)
+
+
 def _warn_float16_on_mps(component_name, from_pretrained_arguments, device):
     """MPS (Apple Silicon) has numerical instability with float16 matmul operations,
     producing NaN values that result in black images. The dtype is left as asked for -

From 1a1907c275ea50aff5b5323723180b4371327802 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:30:46 -0600
Subject: [PATCH 70/74] refactor(server): ratchet - gallery_metadata reads a
 file's provenance through _provenance

gallery_metadata had passed the 150-line function limit. Where a name lives
and which run made it - the asset branch with its kept provenance, the
output branch with the run manifest and listing annotations - is now
`_provenance`, in the same module; the route body is the probe and the
response.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/server/routes/media.py | 105 +++++++++++++++++++++-----------------
 1 file changed, 58 insertions(+), 47 deletions(-)

diff --git a/dw/server/routes/media.py b/dw/server/routes/media.py
index deccd1ab..0c5037bf 100644
--- a/dw/server/routes/media.py
+++ b/dw/server/routes/media.py
@@ -165,10 +165,66 @@ def gallery_metadata(
     `version` are that source run's own - `job.id` is still the one to
     pass `get_job_workflow`, since a pruned run leaves no `workflow.json`
     of its own to reopen. `inputs` is the source run's too (#902)."""
+    name = strip_output_prefix(name)
+    path, source, job, run_id, version, role, inputs, notes = _provenance(
+        request, name, ws
+    )
+    metadata = read_embedded_metadata(path)
+    if metadata is not None and not include_workflow and "workflow" in metadata:
+        metadata = {
+            **{k: v for k, v in metadata.items() if k != "workflow"},
+            "workflow_omitted": True,
+        }
+    extension = os.path.splitext(path)[1].lower()
+    kind = MEDIA_KINDS.get(extension)
+    try:
+        if kind in ("audio", "video"):
+            media = probe_media(
+                path,
+                envelope=envelope,
+                interval_seconds=interval_seconds,
+                pcm_hash=True,
+            )
+        elif kind == "image":
+            media = _image_media(path)
+        else:
+            media = None
+    except ValueError as error:
+        raise HTTPException(status_code=422, detail=str(error)) from error
+    if kind == "image":
+        pass  # no shots in a still
+    elif media is not None and source == "output":
+        # Where each shot of a joined video sits, as the run that wrote
+        # it recorded (dw/shots.py) - null for a file not joined from shots
+        media["shots"] = recorded_shots(ws.outputs, name)
+    elif media is not None and source == "asset":
+        # keep_output carries the source run's shots into a sidecar
+        # manifest beside the asset (#393); a file kept before that fix,
+        # or never joined from shots, has none
+        media["shots"] = shots_beside(path)
+    return {
+        "name": name,
+        "source": source,
+        "metadata": metadata,
+        "job": job,
+        "run_id": run_id,
+        "version": version,
+        "media": media,
+        "findings": level_findings(media, (media or {}).get("shots")),
+        "role": role,
+        "inputs": inputs,
+        **notes,
+    }
+
+
+def _provenance(request, name, ws):
+    """Where `name` lives and which run made it: `(path, source, job, run_id,
+    version, role, inputs, notes)`. An `asset:` name is read from the asset
+    root with the provenance `keep_output` left beside it; anything else is an
+    output, with the run's manifest and the listing's annotations."""
     run_id, version = "", None
     role, inputs = None, None
     notes = {}
-    name = strip_output_prefix(name)
     if is_asset_reference(name):
         path = asset_file(request.app.state, name, ws)
         source, job = "asset", None
@@ -219,52 +275,7 @@ def gallery_metadata(
                 for key in ("note", "note_source", "starred", "run_note")
                 if key in annotated
             }
-    metadata = read_embedded_metadata(path)
-    if metadata is not None and not include_workflow and "workflow" in metadata:
-        metadata = {
-            **{k: v for k, v in metadata.items() if k != "workflow"},
-            "workflow_omitted": True,
-        }
-    extension = os.path.splitext(path)[1].lower()
-    kind = MEDIA_KINDS.get(extension)
-    try:
-        if kind in ("audio", "video"):
-            media = probe_media(
-                path,
-                envelope=envelope,
-                interval_seconds=interval_seconds,
-                pcm_hash=True,
-            )
-        elif kind == "image":
-            media = _image_media(path)
-        else:
-            media = None
-    except ValueError as error:
-        raise HTTPException(status_code=422, detail=str(error)) from error
-    if kind == "image":
-        pass  # no shots in a still
-    elif media is not None and source == "output":
-        # Where each shot of a joined video sits, as the run that wrote
-        # it recorded (dw/shots.py) - null for a file not joined from shots
-        media["shots"] = recorded_shots(ws.outputs, name)
-    elif media is not None and source == "asset":
-        # keep_output carries the source run's shots into a sidecar
-        # manifest beside the asset (#393); a file kept before that fix,
-        # or never joined from shots, has none
-        media["shots"] = shots_beside(path)
-    return {
-        "name": name,
-        "source": source,
-        "metadata": metadata,
-        "job": job,
-        "run_id": run_id,
-        "version": version,
-        "media": media,
-        "findings": level_findings(media, (media or {}).get("shots")),
-        "role": role,
-        "inputs": inputs,
-        **notes,
-    }
+    return path, source, job, run_id, version, role, inputs, notes
 
 
 def _image_media(path):

From 87eb009d6f37be2c3e69c04c603c6fb76a449a9d Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:31:06 -0600
Subject: [PATCH 71/74] docs(architecture): ratchet - rows name
 h3_anchor_stats, pipeline_h3 and chain_spill

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 docs/ARCHITECTURE.md                | 4 ++--
 dw/pipeline_processors/h3_anchor.py | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 10844cae..47531999 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -76,7 +76,7 @@ one to open.
 | Result subfolders | `dw/subfolders.py`: `subfolder_errors`, `dw/security.py`: `SUBFOLDER_PATTERN` | A step's `result.subfolder` is checked for shape at validation. | `tests/test_subfolders.py::TestValidationErrorsIntegration::test_validation_errors_reports_a_bad_subfolder` |
 | Template subfolder roles | `workflows/templates/`, `dw/workflows/` | Every saving step of a template is marked `final` or `intermediate`, with at least one `final`, and packaged builtins stay unmarked. | `tests/test_template_subfolders.py::test_every_saving_step_of_a_template_names_its_role`, `tests/test_template_subfolders.py::test_the_packaged_builtins_stay_unmarked` |
 | Frames carrying their rate | `dw/media_types.py`: `FrameList` (re-exported by `dw/tasks/video_utils.py`), `rated_videos` | The one list of frames (or of videos) that carries `.fps` and `.shots`: a video loaded from a file (`dw/argument_media.py`) and a step that knows its rate but would return bare frame lists both answer it - `h3_tiles`' tiled refine `videos`, and every pipeline step whose pipeline reports its own `fps` (MiniMax-H3's property; `dw/output_extraction.py`: `attach_video_frame_rate`, called by `pipeline.py`'s `_run_once` beside `attach_audio_sample_rate`, #1056) - through `rated_videos`, the batch and each video in it, so `pair_audio` and the result writer read `getattr(video, "fps", None)` instead of fitting and playing at the default 8 fps. A pipeline that reports no rate leaves its lists bare. No second class of that name. | `tests/test_h3_tiles.py::TestTileLoopReporting::test_the_videos_are_dws_one_frame_list`, `tests/test_pipeline_components.py::TestVideoFrameRate::test_a_dict_outputs_videos_carry_the_pipelines_rate`, `tests/test_pipeline_components.py::TestVideoFrameRate::test_a_pipeline_with_no_rate_leaves_the_lists_bare`, `tests/test_modular_output_properties.py::TestTheBasesOwnTakeSavesBesideIt::test_the_base_take_pairs_at_its_own_rate_with_no_padding`, `tests/test_video_utils.py` (the `FrameList` cases) |
-| Writing a video at its rate | `dw/writers.py`: `encode_video`, `exact_frame_rate` | A fractional rate is written as the exact rational (30000/1001, 24000/1001, 60000/1001), never truncated to a whole rate, and no `dw/` or `dw_mcp/` module imports `encode_video` from `diffusers.utils` - every audio-carrying video write goes through `dw.writers.encode_video` (`dw/result.py`, `dw/pipeline_processors/chain.py`). A declared fractional `result.fps` is therefore accepted by `fps_errors`. | `tests/test_fractional_fps.py::test_encode_video_with_audio_keeps_fractional_rate`, `tests/test_fractional_fps.py::test_no_dw_module_calls_the_diffusers_encode_video_directly`, `tests/test_fractional_fps.py::test_the_diffusers_hook_the_wrapper_swaps_still_exists` |
+| Writing a video at its rate | `dw/writers.py`: `encode_video`, `exact_frame_rate` | A fractional rate is written as the exact rational (30000/1001, 24000/1001, 60000/1001), never truncated to a whole rate, and no `dw/` or `dw_mcp/` module imports `encode_video` from `diffusers.utils` - every audio-carrying video write goes through `dw.writers.encode_video` (`dw/result.py`, `dw/pipeline_processors/chain_spill.py` - a chain's segments on disk, `SegmentSpill`, `SegmentedFrames` and `SavedFrames`, re-exported by `chain.py`). A declared fractional `result.fps` is therefore accepted by `fps_errors`. | `tests/test_fractional_fps.py::test_encode_video_with_audio_keeps_fractional_rate`, `tests/test_fractional_fps.py::test_no_dw_module_calls_the_diffusers_encode_video_directly`, `tests/test_fractional_fps.py::test_the_diffusers_hook_the_wrapper_swaps_still_exists` |
 | JSON records beside media | `dw/media_types.py`: `JsonRecord`, `dw/result.py`: `Result.save_artifact` | A dict marked `JsonRecord` is saved whole as one `.json`, whatever content type the step declares, and is never split into one file per key. | `tests/test_face_track.py::test_json_record_is_saved_whole_under_a_video_content_type`, `tests/test_face_track.py::test_the_task_result_saves_crops_as_video_and_track_as_one_json` |
 | Reading a JSON record back | `dw/locations.py`: `load_json_record` | A task reads a saved JSON record (`fit_to_model`'s, `crop_face_track`'s) only through `load_json_record`: the path is confined like any media path, then the extension must be `.json`, then the size is checked against `MAX_JSON_SIZE`, and only then is it parsed. `fit._read_fit` and `face_track._read_track` call it, never `open`. | `tests/test_locations.py::TestLoadJsonRecord::test_traversal_out_of_the_roots_is_refused`, `tests/test_locations.py::TestLoadJsonRecord::test_a_non_json_extension_is_refused`, `tests/test_locations.py::TestLoadJsonRecord::test_an_oversized_file_is_refused_before_it_is_read`, `tests/test_fit_to_model.py::TestRestore::test_fit_path_with_traversal_is_refused` |
 | Shots in a joined video | `dw/shots.py` | Every `AudioVideo` constructor decides what to do with `shots`, and the sample spans are measured from the joined waveform. | `tests/test_shots.py::test_every_audio_video_constructor_site_is_decided`, `tests/test_shots.py::TestConcatVideosShots::test_an_overrun_track_is_measured_not_derived` |
@@ -114,7 +114,7 @@ one to open.
 | Acceleration warnings | `dw/acceleration_warnings.py`: `acceleration_warnings`, `CACHE_MIN_USEFUL_STEPS`, `CUDA_ONLY_ATTENTION_BACKENDS` (the last entry of `WARNING_CHECKS`) | An acceleration setting that can't pay off (a `first_block`/`mag` cache under `CACHE_MIN_USEFUL_STEPS`, any `taylorseer` cache, `compile.fullgraph` with a cache, a cache on a pipeline class with no transformer or whose transformer lacks `enable_cache()`, a CUDA-only `attention_backend` on a non-CUDA device) is a warning and never a refusal, and an unresolvable value (steps, backend, device) leaves it silent. | `tests/test_validation.py::TestAccelerationWarnings::test_warnings_never_refuse`, `tests/test_validation.py::TestAccelerationWarnings::test_steps_that_resolve_to_nothing_are_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unknown_device_is_silent`, `tests/test_validation.py::TestAccelerationWarnings::test_an_unresolvable_backend_is_silent_on_mps` |
 | H3 sparse attention | `dw/sparse_attention.py`: `sparse_call`, `install_sparse`, `install_sparse_for_call`, `apply_sparse_settings`, `SparseH3AttnProcessor`; wired from `dw/pipeline_processors/components.py`: `configure_components` and `dw/pipeline_processors/pipeline.py`: `apply_runtime_settings` | Whether a pipeline call runs sparse is decided once per call, all or nothing (the call's first kernel invocation included); it never changes diffusers' process-wide backend and never reaches `AttentionBackendName`; it installs by a processor swap plus forward hooks, never a `forward` wrapper; and `sparse_tau` is a runtime setting, not part of the weights identity (#931). | `tests/test_sparse_attention_processor.py`, `tests/test_sparse_attention_wiring.py`, `tests/test_sparse_attention_tiles.py`, `tests/test_sparse_attention_backends.py` |
 | H3 adapter partition | `dw/adapter_compatibility.py`; the server's index is `dw/server/deps.py` `lora_index` | The LoRA catalog decides first, on the server only: a `proven` entry's `workflow` refuses a step in the other partition, a `trial`/`rejected` entry's only warns. A file no entry places falls to its name: an FL2VA LoRA on a reference step is refused, and one whose name says neither `ref2v` nor `fl2v` is warned. | `tests/test_h3_adapters.py::TestWhatIsRefused::test_a_keyframe_adapter_on_the_reference_path`, `tests/test_h3_adapters.py::TestWhatIsRefused::test_an_unrecognised_name_is_a_warning_not_a_refusal`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_proven_entry_refuses_the_other_partition`, `tests/test_h3_adapters.py::TestTheCatalogDecidesFirst::test_a_weaker_entry_only_warns`, `tests/test_server_loras.py::TestTheCatalogPlacesAnAdapter::test_a_trial_entry_warns_and_promoting_it_refuses` |
-| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Thirteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `match_channel_statistics`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`, decided by the block itself; it declares `idle_without = "latent_anchor"` so the step-progress narrator leaves it unnamed then; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`, and `run_chain` sets `pipeline.latent_anchor_source` to `segment 1` for those segments only; `Pipeline._with_latent_anchor` emits one `log` line per anchored call naming that source, the quoted reference, or the step's frames; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` adds E(noisy) - E(clean) to each video guide's clean encode when `guide_noise` is set, on `h3_rules.guide_noise_taper`, and encodes once without it; `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline.py` adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_no_anchor_logs_nothing`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_a_chain_segment_logs_its_label_and_segment_1`, `tests/test_modular_progress.py::test_the_anchor_step_is_idle_without_latent_anchor`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
+| H3 audio hold, refine, tiled refine, guide layout, guide noise, latent anchor and regional edit | Fourteen modules, imported one way: `dw/pipeline_processors/h3_rules.py` holds the names and rules, torch- and diffusers-free (`refine_problems`, the per-argument refine rules, `refine_tiles` and the explicit `refine_sigmas` schedule among them, asked before validation and before the call; `tile_plan`, the tile layout on the 32 px grid, shared by validate and the run; `not_h3`, the step rule; `snap_guide_length`, `guide_frame_problem`, `guide_chain_problems`, `guide_end_problem`; `CHAIN_CONTINUITY_MODES`; the chunk constants; and the regional-edit rules `mask_problems`, `media_problems` and `edits_region`, the argument rules shared by validate and `_with_mask`; `media_problems` is asked by validate too, on the files that resolve then, and refuses a `source_video` off the `17n + 5` grid, which the pipeline would otherwise snap up over a padded source), `dw/pipeline_processors/h3_mask_cells.py` holds the mask-to-rows arithmetic (`binarized`, `regenerate_cells`, `keep_rows`, on Pillow's L conversion, torch's adaptive max pool and scipy's dilation, each imported where it runs), which `h3_mask.py` re-exports and `h3_mask_steps.py` reads from there so the blocks never import their importer, `dw/pipeline_processors/h3_hold.py` puts the hold and refine blocks in (`insert_audio_hold`, `core_denoise_sequences`, `refines`; `HELD_LATENTS_OUTPUT` and `encoded_hold_audio`, the hold block's encode handed back to it), and `dw/pipeline_processors/h3_guides.py` the guide layout (`LAYOUT_ANCHORS`, `insert_guides`), which imports `h3_hold` and never the reverse, `dw/pipeline_processors/h3_anchor.py` the latent statistics anchor (`insert_latent_anchor`, `anchor_refusal`; its block in `h3_anchor_steps.py`, imported only by `anchor_block()`; the arithmetic, `channel_statistics` and `match_channel_statistics`, in `h3_anchor_stats.py`, torch only, which `h3_anchor` re-exports and `h3_anchor_steps` reads from there, as `h3_mask_cells` is read, so the block never imports its importer), which imports `h3_hold` and `h3_guides` and never the reverse, and runs right after `after_denoise` in t2va/fl2va only (a no-op without `latent_anchor`, decided by the block itself; it declares `idle_without = "latent_anchor"` so the step-progress narrator leaves it unnamed then; `chain.anchor: "segment_1"` hands segments 2+ segment 1's frames in `chain.py`'s `_segment_arguments`, and `run_chain` sets `pipeline.latent_anchor_source` to `segment 1` for those segments only; `Pipeline._with_latent_anchor` emits one `log` line per anchored call naming that source, the quoted reference, or the step's frames; `dw/guides.py`'s `anchor_errors` is the `latent_anchor` validation check, on `h3_rules.chain_anchor_problem` and the guides step rule `_takes_guides_problem`, asked for the anchor's name, so a step passing `references` (ref2va) is refused as `guides` is; `Pipeline._with_latent_anchor` refuses `references` at run time as `_with_guides` does), and `dw/pipeline_processors/h3_mask.py` the regional edit (`insert_mask_blocks`, `edits_regions`), which imports `h3_hold` and never the reverse; the blocks themselves, and the arithmetic they run, are `dw/pipeline_processors/h3_hold_steps.py` (`encode_audio_span`, `refine_sigmas`, and `explicit_sigmas`, a step's `refine_sigmas` list run verbatim in place of the shift grid) `dw/pipeline_processors/h3_guide_steps.py` (`splice_guide_rows`, and `noised_guide_pixels`, the `guide_noise` pixels: the layout block's `_encode_guides` adds E(noisy) - E(clean) to each video guide's clean encode when `guide_noise` is set, on `h3_rules.guide_noise_taper`, and encodes once without it; `guide_noise_errors`/`guide_noise_warnings` in `dw/guides.py` validate it on `h3_rules.guide_noise_problem` and `_takes_guides_problem`, and `Pipeline._with_guide_noise` drops a 0 and refuses at run time) and `dw/pipeline_processors/h3_mask_steps.py` (the source-latents and mask-blend blocks), which import diffusers when they load, so the three owners import them only inside `blocks()`, `guide_blocks()` and `h3_mask.blocks()` (#790), and `h3_guide_steps` imports `h3_hold_steps` and never the reverse (`_with_guides` in `dw/pipeline_processors/pipeline_h3.py` - the `H3Arguments` mixin that gives `Pipeline` its per-call H3 argument wrappers `_with_held_audio`, `_with_guides`, `_with_guide_noise`, `_with_latent_anchor`, `_with_mask` and `_check_refine`, still named `Pipeline._with_*` - adds its blocks for a `guides` call, and `_with_mask` checks a regional edit's arguments and media and builds the regenerate mask for a `source_video` call), `dw/guides.py`: the `guides` validation check and the chain `continuity: "guide"` check, `guide_chain_errors`, which asks the `guides` check's own step rule (`_takes_guides_problem`, built on `h3_rules.not_h3` and `REFERENCE_WORKFLOWS`) and reads the mode names from `h3_rules.CHAIN_CONTINUITY_MODES`, the names `chain.py`'s `CONTINUITY_MODES` is zipped against (both registered in `dw/validation.py`; a `previous_result:` guide is checked at run time), `dw/pipeline_processors/h3_tiles.py`: the tiled refine (the tile loop, the latent-space feather blend, the per-tile crop of one whole-canvas noise draw, the decode-free graph variant, and the one spatially tiled decode), which imports `h3_rules`, `h3_hold` and `media_types` (for `FrameList`; and `events`, `progress`) and is imported by `pipeline.py` and, lazily, `components.py` (which attaches the variant at load, behind `h3_hold.core_denoise_sequences`, and calls `insert_mask_blocks` at load), never by `h3_rules` or `h3_hold`, `dw/hold_audio.py`: `hold_audio_errors` (a video is held by its soundtrack), `refine_strength_errors`, `mask_errors` (Check `regional_edit`, which probes the source and mask through `ValidationContext.probe` the way `dw/guides.py` probes guide videos) | The one place dw modifies a diffusers modular pipeline's block graph: three blocks go in, before `set_timesteps`, `denoise` and `after_denoise`, in every H3 core-denoise sequence (the whole graph and the `workflow=`-pruned flat graph), and two more for a regional edit: the source block before `set_timesteps` and the blend block inside the `denoise` loop after `update`. All are no-ops without their arguments (`hold_audio` / `refine_strength`, `source_video`). The tiled refine adds a second graph built at load beside them (`h3_tiles.decode_free_blocks`: the decode and text-encoder blocks dropped, every kept block the graph's own instance, so hold and refine stay in) and swaps it into `pipeline._blocks` only for the duration of the one text-encode call and the tile calls (`swapped_blocks`, restored on failure). Every tile runs the hold block, but the track is encoded once per refine: the first tile is asked for the block's encode (`HELD_LATENTS_OUTPUT`) and every later tile's `hold_audio` carries it (`encoded_hold_audio`, an `EncodedAudioReference`), which the block writes in place of running the audio VAE. A `hold_audio` path is opened only after `locations.validate_media_path` against the step's workflow directory. `dw/introspection.py` builds the same graph without weights, hold included, so `get_pipeline_signature` lists a modular pipeline's block inputs. The copies of diffusers behaviour are pinned to the installed diffusers: `shifted_sigma_grid` (the spacing `refine_sigmas` rests on) to stock `set_timesteps`, `GUIDE_FRAMES_PER_CHUNK`/`GUIDE_LATENTS_PER_CHUNK` to the H3 video VAE's `clip_length` and latents per clip, and every name in `LAYOUT_ANCHORS` (`_fill_audio_positions` among them) to `before_denoise`. | `tests/test_h3_hold_audio.py::TestAnchors::test_inserted_next_to_the_anchors_in_every_shape`, `tests/test_h3_hold_audio.py::TestHoldAudioLocation::test_a_path_outside_the_roots_is_refused_at_the_call`, `tests/test_h3_hold_audio.py::TestRefusals::test_a_non_h3_step_is_refused`, `tests/test_h3_hold_audio.py::TestSignature::test_the_h3_signature_shows_hold_audio`, `tests/test_h3_refine.py::TestAnchors::test_inserted_before_denoise_in_every_shape`, `tests/test_h3_tiles.py::TestDecodeFree::test_variant_has_no_decode_and_shares_blocks`, `tests/test_h3_tiles.py::TestTileLoop::test_blocks_are_restored_when_a_tile_fails`, `tests/test_h3_tiles.py::TestTileLoop::test_the_track_is_encoded_by_one_tile_and_handed_to_the_rest`, `tests/test_h3_hold_audio.py::TestHoldBlock::test_a_carried_encode_is_written_without_encoding_again`, `tests/test_h3_tiles.py`, `tests/test_h3_rules.py` (`tile_plan` and the `refine_tiles` rules), `tests/test_h3_mask.py`, `tests/test_h3_refine.py::TestSigmaGridDrift::test_the_full_grid_is_stock_set_timesteps`, `tests/test_h3_refine.py::TestSigmaListOneOwner::test_the_runtime_message_is_the_owners`, `tests/test_h3_refine.py::TestSigmaListSchedule::test_the_block_runs_the_list_unshifted`, `tests/test_h3_guides.py::TestChunkDrift::test_frames_per_chunk_is_the_vae_clip_length`, `tests/test_h3_guides.py::TestChunkDrift::test_latents_per_chunk_is_the_vae_tokens_per_clip`, `tests/test_h3_guides.py::TestAnchors::test_installed_diffusers_has_every_anchor`, `tests/test_guide_chain_validation.py::TestRefused::test_the_step_rule_is_the_guides_rule`, `tests/test_guide_chain_validation.py::TestRefused::test_every_run_mode_is_known_to_validate`, `tests/test_h3_rules.py::test_h3_rules_imports_neither_torch_nor_diffusers`, `tests/test_h3_rules.py::test_the_block_owners_import_no_diffusers`, `tests/test_h3_anchor.py::TestInsert::test_follows_after_denoise`, `tests/test_h3_anchor.py::TestStep::test_without_an_anchor_the_latents_are_untouched`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_no_anchor_logs_nothing`, `tests/test_h3_anchor.py::TestWithLatentAnchor::test_a_chain_segment_logs_its_label_and_segment_1`, `tests/test_modular_progress.py::test_the_anchor_step_is_idle_without_latent_anchor`, `tests/test_h3_anchor.py::TestAnchorErrors::test_an_unknown_anchor_names_the_values`, `tests/test_h3_guide_noise.py` |
 | IC-LoRA reference scale | `workflows/templates/ltx2/` | The five conditioning templates run at `reference_downscale_factor: 1`, and the two upscalers at 2. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_reference_is_encoded_at_the_output_resolution`, `tests/test_ltx2_ic_loras.py::TestTheGenerativeUpscaleMatchesTheSameCard::test_it_loads_the_upscaler_at_factor_two` |
 | IC-LoRA numbers | `workflows/templates/ltx2/` | Every number in the IC-LoRA templates comes from the vendor's model card. | `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_strength_is_the_cards_default`, `tests/test_ltx2_ic_loras.py::TestEachTemplateMatchesItsCard::test_the_defaults_are_the_trained_bucket` |
 | IC-LoRA prompt genre | `prompts/ltx2/` | A stored prompt tagged `ic-lora` is checked against its trained caption form, not against the T2V paragraph rule. | `tests/test_ltx_prompt_library.py::test_an_ic_lora_prompt_is_in_its_trained_form` |
diff --git a/dw/pipeline_processors/h3_anchor.py b/dw/pipeline_processors/h3_anchor.py
index e7aca83a..e744a3e3 100644
--- a/dw/pipeline_processors/h3_anchor.py
+++ b/dw/pipeline_processors/h3_anchor.py
@@ -26,7 +26,9 @@
 
 The concept follows H3MD Director's segment colour anchoring (concept only, no
 code; docs/ATTRIBUTIONS.md). The block module imports diffusers when it loads, so
-it is imported in `anchor_block()` only, as h3_guides does its blocks (#790).
+it is imported in `anchor_block()` only, as h3_guides does its blocks (#790); the
+statistics arithmetic it runs is `h3_anchor_stats.py`, re-exported here, so the
+block never imports this module back.
 """
 
 import logging

From 151106bc33fcc4c33cd6b6224d78083838c59306 Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:31:41 -0600
Subject: [PATCH 72/74] style: ratchet - ruff format on h3_anchor

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 dw/pipeline_processors/h3_anchor.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dw/pipeline_processors/h3_anchor.py b/dw/pipeline_processors/h3_anchor.py
index e744a3e3..800367a1 100644
--- a/dw/pipeline_processors/h3_anchor.py
+++ b/dw/pipeline_processors/h3_anchor.py
@@ -50,6 +50,7 @@
 
 logger = logging.getLogger("dw")
 
+
 def anchor_block():
     """The anchor step class (imports diffusers on first use)."""
     from .h3_anchor_steps import DwH3LatentAnchorStep

From a551f9d54b24d524f221c952dbd8ded2296fbe7e Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:33:32 -0600
Subject: [PATCH 73/74] test(shots): ratchet - the chain's AudioVideo site is
 _stitched

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 tests/test_shots.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/test_shots.py b/tests/test_shots.py
index 5a31a836..c3ea569f 100644
--- a/tests/test_shots.py
+++ b/tests/test_shots.py
@@ -76,7 +76,8 @@ def audio_video(num_frames, level, fps=4, sample_rate=100):
     ("dw/tasks/dissolve_videos.py", "dissolve_videos"): ("populates", 1),
     ("dw/tasks/join_into_song.py", "join_into_song"): ("populates", 1),
     ("dw/tasks/windows.py", "join_windows"): ("remeasures", 1),
-    ("dw/pipeline_processors/chain.py", "run_chain"): ("populates", 2),
+    # run_chain hands its result to _stitched, which builds it
+    ("dw/pipeline_processors/chain.py", "_stitched"): ("populates", 2),
     ("dw/tasks/image_ops.py", "per_frame"): ("carries", 1),
     ("dw/tasks/stabilize.py", "stabilize_video"): ("carries", 1),
     ("dw/tasks/interpolate_frames.py", "interpolate_frames"): ("rescales", 1),

From 61206511f7027eb3caf2f38308504ecffe277cfb Mon Sep 17 00:00:00 2001
From: Don Kackman <dkackman@gmail.com>
Date: Tue, 3 Nov 2026 07:36:37 -0600
Subject: [PATCH 74/74] chore(ratchet): modules 237 -> 243 for 0.19.0

Three shipped with #1173/#1174/#1160 (h3_anchor, h3_anchor_steps,
texture_bands) and three are the release-prep splits that cleared the
size, length and cycle ratchets (h3_anchor_stats, pipeline_h3,
chain_spill). Every other metric is at or under its baseline.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 docs/stabilization/baseline.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/stabilization/baseline.json b/docs/stabilization/baseline.json
index 22ec4ff2..3da08bc9 100644
--- a/docs/stabilization/baseline.json
+++ b/docs/stabilization/baseline.json
@@ -1,5 +1,5 @@
 {
-  "modules": 237,
+  "modules": 243,
   "modules_over_size_ceiling": 0,
   "functions_over_150_lines": 0,
   "prefix_literals": 0,