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WanTransformer3DModel

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WanTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in Wan 2.1 by the Alibaba Wan Team.

The model can be loaded with the following code snippet.

from diffusers import WanTransformer3DModel

transformer = WanTransformer3DModel.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)

WanTransformer3DModel

class diffusers.WanTransformer3DModel

< >

( patch_size: typing.Tuple[int] = (1, 2, 2) num_attention_heads: int = 40 attention_head_dim: int = 128 in_channels: int = 16 out_channels: int = 16 text_dim: int = 4096 freq_dim: int = 256 ffn_dim: int = 13824 num_layers: int = 40 cross_attn_norm: bool = True qk_norm: typing.Optional[str] = 'rms_norm_across_heads' eps: float = 1e-06 image_dim: typing.Optional[int] = None added_kv_proj_dim: typing.Optional[int] = None rope_max_seq_len: int = 1024 pos_embed_seq_len: typing.Optional[int] = None )

Parameters

  • patch_size (Tuple[int], defaults to (1, 2, 2)) — 3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
  • num_attention_heads (int, defaults to 40) — Fixed length for text embeddings.
  • attention_head_dim (int, defaults to 128) — The number of channels in each head.
  • in_channels (int, defaults to 16) — The number of channels in the input.
  • out_channels (int, defaults to 16) — The number of channels in the output.
  • text_dim (int, defaults to 512) — Input dimension for text embeddings.
  • freq_dim (int, defaults to 256) — Dimension for sinusoidal time embeddings.
  • ffn_dim (int, defaults to 13824) — Intermediate dimension in feed-forward network.
  • num_layers (int, defaults to 40) — The number of layers of transformer blocks to use.
  • window_size (Tuple[int], defaults to (-1, -1)) — Window size for local attention (-1 indicates global attention).
  • cross_attn_norm (bool, defaults to True) — Enable cross-attention normalization.
  • qk_norm (bool, defaults to True) — Enable query/key normalization.
  • eps (float, defaults to 1e-6) — Epsilon value for normalization layers.
  • add_img_emb (bool, defaults to False) — Whether to use img_emb.
  • added_kv_proj_dim (int, optional, defaults to None) — The number of channels to use for the added key and value projections. If None, no projection is used.

A Transformer model for video-like data used in the Wan model.

Transformer2DModelOutput

class diffusers.models.modeling_outputs.Transformer2DModelOutput

< >

( sample: torch.Tensor )

Parameters

  • sample (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch size, num_vector_embeds - 1, num_latent_pixels) if Transformer2DModel is discrete) — The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.

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