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AnyFlowFARTransformer3DModel

docs/source/en/api/models/anyflow_far_transformer3d.md

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AnyFlowFARTransformer3DModel

The causal (FAR) 3D Transformer used by AnyFlowFARPipeline — the FAR variant of AnyFlow. See the AnyFlowFARPipeline page for paper, authors, and released checkpoints. It extends the v0.35.1 Wan2.1 backbone with three additions:

  1. FAR causal block-mask via torch.nn.attention.flex_attention, supporting chunk-wise autoregressive generation as introduced in FAR.
  2. Compressed-frame patch embedding (far_patch_embedding) for context (already-generated) frames, warm-started from the full-resolution patch_embedding at construction time via trilinear interpolation.
  3. Dual-timestep flow-map embedding (same as AnyFlowTransformer3DModel) — every forward call conditions on both the source timestep t and the target timestep r.

The default chunk schedule (chunk_partition) is stored in the model config; the released NVIDIA AnyFlow-FAR checkpoints use [1, 3, 3, 3, 3, 3, 3, 2] for the canonical 81-frame setting. forward accepts a per-call chunk_partition override, so the same checkpoint also handles other num_frames configurations without retraining.

python
from diffusers import AnyFlowFARTransformer3DModel

# Causal AnyFlow checkpoint (FAR):
transformer = AnyFlowFARTransformer3DModel.from_pretrained(
    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", subfolder="transformer"
)

AnyFlowFARTransformer3DModel

[[autodoc]] AnyFlowFARTransformer3DModel

AnyFlowFARTransformerOutput

[[autodoc]] models.transformers.transformer_anyflow_far.AnyFlowFARTransformerOutput