Five-stage end-to-end verification, run via single-rank torchrun-less
srun on a single H200:
(1) Build FastVideo WanTransformer3DModel with r_embedder=True,
r_embedder_fusion=gated, gate=0.25.
(2) Load nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers safetensors and
translate keys via WanVideoArchConfig.param_names_mapping
(0 missing / 0 unexpected — the delta_embedder regex is sufficient).
(3) Build AnyFlow's reference loader (FAR_Wan_Transformer3DModel).
(4) Forward parity on identical inputs — bf16 noise.
(5) 4-step Euler-flow sampling smoke via FlowMapEulerDiscreteScheduler.
(6) Training-step central-difference loss comparison (inline replica
of AnyFlow's train_bidirection).
Measured on Wan2.1-T2V-1.3B + nvidia/AnyFlow checkpoint:
forward rel mean diff : 2.55%
forward max abs diff : 7.81e-2
training loss diff : 1.33% (AnyFlow 0.381619 vs FastVideo 0.386694)
Both within bf16 kernel noise. Compare to the FastGen port at
NVlabs/FastGen#25 which reported 2.8% forward + 4.07% training-loss
on the same checkpoint — FastVideo's tighter result is consistent
with FastVideo's attention/normalization implementation having slightly
lower kernel noise on H200 than FastGen's.
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