MiniMax-H3 is a 33B flow-matching DiT that denoises video and audio in ONE packed
sequence. diffusers ships it as Modular blocks only (MiniMaxH3ModularPipeline,
0.40+) — there is no DiffusionPipeline half, so it fits neither
_detect_pipeline_class() nor this venv's diffusers 0.38. It runs in an isolated
venv, the same way pyannote, NeMo-Canary and vLLM do:

  tools/h3_service.py   generation inside the isolated venv (HTTP)
  codai/api/h3_worker.py venv bootstrap, process lifecycle, eviction handle
  requirements-h3.txt   the venv's pins (diffusers>=0.40, transformers 5.x)

_generate_h3() adapts the request to the checkpoint's contracts instead of
forwarding it, and reports every adaptation in the response warnings: guidance is
baked in (negative_prompt and guidance_scale dropped), 24 fps is fixed and
num_frames snaps up to the next 17n+5 within 5-15s, canvas axes round to 32, and
v2v/interp are rejected. character_profiles become native ref2va references (max
9) so H3 skips the identity-keyframe bridge; an explicit keyframe wins, since
fl2va and ref2va are separate transformer partitions. The worker picks a workflow
per request and never loads without one — that pulls both 61.7GB partitions.

The worker registers as an evictable, VRAM-tracked model; eviction stops the
subprocess, the only way the transformer's pages actually come back.

Character studio: clone the voice off the same footage the face came from
(/v1/audio/voices/extract) with `extract --voice`, the new `voice` subcommand or
the UI checkbox, then `--say` a line that is lip-synced in that cloned voice.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0182DhR1eFNPQyGmDrpHbedY