Step 4b failed in production:

  RuntimeError: longcat LoRA training needs `dataset_config` — a path to a
  SimpleTuner data-backend config describing your captioned video clips

The refusal is right: LongCat trains through SimpleTuner, which wants captioned
VIDEO clips, and synthesising a dataset from a fighter's reference stills would
train something other than a video LoRA. But the UI offered no way to supply one,
so the button could not succeed — and the tool was already sitting on exactly the
corpus SimpleTuner asks for. Every clip it renders was rendered FROM a prompt, and
that prompt is kept beside it in videos/prompts.json. So it now assembles the
dataset rather than demanding it.

Per fighter and per location, against a LongCat video model:

  <out>/video_datasets/character_<name>__<slug>/
    clips/0000.mp4 -> ../../../videos/match_a_vs_b_clip03.mp4
    clips/0000.txt            the prompt that produced those pixels
    cache/vae  cache/text
    data_backend.json         sent as dataset_config
    manifest.json             which render each clip came from

Decisions worth stating:

- SYMLINKS, not copies. A fighter appears in dozens of clips; a dataset per
  fighter would otherwise duplicate the whole render per fighter.
- Captions come from the PLAN, never from filenames, so a caption is always the
  text that actually produced the pixels.
- _2x/_2xfps derivatives are never picked up. They are the upscaler's and the
  frame interpolator's output; training on them teaches their artefacts.
- An outcome clip is training data for BOTH fighters — they are both on screen.
- Clips shorter than the training frame count are filtered out HERE, because
  SimpleTuner's loader drops them silently. A dataset where every clip is too
  short refuses with the numbers: "all 12 clips are shorter than 93 frames
  (longest 70)". Likewise "only 3 usable clips, need 8" and "no rendered clips
  yet (run the Videos step first)" — a skip with a reason, not a stack trace.
- Rebuilt from scratch each time, so a symlink to a deleted render cannot fail
  deep inside SimpleTuner's loader instead of here.

Two knobs on the Run page, in the config and on the CLI: Training clip frames
(default 93 — one whole LongCat segment, and 4n+1 as the VAE requires) and
Minimum usable clips (default 8; upstream suggests 50-100, this is a floor so a
part-rendered show does not quietly train on three).

The backend schema is upstream's documented one, including exactly one
text_embeds backend carrying default: true. The client sends dataset_config only
when it has one; the server has always accepted the field and the in-process Wan
path ignores it.

Checked against the real township render: 151 sources (102 clips + 49 outcomes),
19 usable for the busiest fighter at 93 frames. 82 tests.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0182DhR1eFNPQyGmDrpHbedY