#
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

< source >

( 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 )

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

< source >

( 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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