diffusers/pr_13966/en/api/models/wan_transformer_3d.md

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[[diffusers.WanTransformer3DModel]]

• 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.

• hidden_states (torch.Tensor of shape (batch_size, num_channels, num_frames, height, width)) -- Input hidden_states.
• timestep (torch.LongTensor) -- Used to indicate denoising step.
• encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) -- Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
• encoder_hidden_states_image (torch.Tensor, optional) -- Conditional image embeddings for image-conditioned generation.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.
• attention_kwargs (dict, optional) -- A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The WanTransformer3DModel forward method.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

• 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.