Files
hao-ai-lab-FastVideo/v2/platform
SolitaryThinker 7fd58f26ea [fix] correct GPU adapters against real fastvideo API (cross-check findings)
Adversarial cross-check of the written-not-run torch adapters against the real
fastvideo source confirmed the interface contracts (DiT returns bare velocity
tensor; timestep=sigma*1000; encode().mode() + bare decode; .last_hidden_state;
no fused solver kernel) but caught a wrong construction layer. Fixed in code:

- Construction: WanTransformer3DModel / AutoencoderKLWan have NO from_pretrained.
  Replace it with the real FastVideo loaders (TransformerLoader / VAELoader /
  TextEncoderLoader + TokenizerLoader, each load(model_path, fastvideo_args)).
  The loader resolves the class from the checkpoint config — so UMT5-vs-T5 is
  chosen correctly instead of hardcoded (was BLOCKER #1/#4/#5).
- Text encoder: wrap the forward in set_forward_context(...) — the (U)MT5
  attention reads global state via get_forward_context(); a bare call mis-encodes
  (was BLOCKER #2). Drop the wrong padding="max_length".
- VAE: apply latent normalization the DiT expects — (z-mean)*inv_std on encode,
  inverse on decode, with latents_std stored as its reciprocal; shift_factor
  before decode (was BLOCKER #3). Skipping it yields washed-out video, not error.

The remaining unknowns are genuinely box-dependent (FastVideoArgs fields,
shift_factor placement, exact tokenizer kwargs, FSDP) — GPU_BRINGUP.md reconciled
to mark what's now fixed-in-code vs what still needs the box. 204 CPU tests pass.
2026-06-18 09:41:15 +00:00
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