basic_fast_minimax_h3.py runs FastVideo/FastVideo-Minimax-H3-Preview-v0.1,
the data-free-DMD2 distillation of MiniMax-H3: 4 denoising steps on the
release sampler's shift-12 schedule (vs the base model's 50), synchronized
video+audio in one pipeline call, guidance_scale 1.0.
The script always requests the VSA-H3 attention backend through the typed
boot-time route (pipeline.experimental -> FastVideoArgs.attention_backend):
the student checkpoint carries trained to_gate_compress gates, which only
exist under that backend. --vsa-sparsity defaults to 0.0 (every tile
selected — exactly dense attention); --vsa-tile-size defaults to 64, the
geometry the student was trained with, and is forwarded even at sparsity 0
because the gate-compress branch pools per tile. The HF repo is private
while the MiniMax H3 Community License review completes; --model-path
accepts a local snapshot meanwhile (noted in the script and README).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
