# WanAnimateTransformer3DModel A Diffusion Transformer model for 3D video-like data was introduced in [Wan Animate](https://github.com/Wan-Video/Wan2.2) by the Alibaba Wan Team. The model can be loaded with the following code snippet. ```python from diffusers import WanAnimateTransformer3DModel transformer = WanAnimateTransformer3DModel.from_pretrained("Wan-AI/Wan2.2-Animate-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16) ``` ## WanAnimateTransformer3DModel[[diffusers.WanAnimateTransformer3DModel]] - **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. - **image_dim** (`int`, *optional*, defaults to `1280`) -- The number of channels to use for the image embedding. If `None`, no projection is used. - **added_kv_proj_dim** (`int`, *optional*, defaults to `5120`) -- 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 WanAnimate model. - **hidden_states** (`torch.Tensor` of shape `(B, 2C + 4, T + 1, H, W)`) -- Input noisy video latents of shape `(B, 2C + 4, T + 1, H, W)`, where B is the batch size, C is the number of latent channels (16 for Wan VAE), T is the number of latent frames in an inference segment, H is the latent height, and W is the latent width. - **timestep** -- (`torch.LongTensor`): The current timestep in the denoising loop. - **encoder_hidden_states** (`torch.Tensor`) -- Text embeddings from the text encoder (umT5 for Wan Animate). - **encoder_hidden_states_image** (`torch.Tensor`) -- CLIP visual features of the reference (character) image. - **pose_hidden_states** (`torch.Tensor` of shape `(B, C, T, H, W)`) -- Pose video latents. TODO: description - **face_pixel_values** (`torch.Tensor` of shape `(B, C', S, H', W')`) -- Face video in pixel space (not latent space). Typically C' = 3 and H' and W' are the height/width of the face video in pixels. Here S is the inference segment length, usually set to 77. - **motion_encode_batch_size** (`int`, *optional*) -- The batch size for batched encoding of the face video via the motion encoder. Will default to `self.config.motion_encoder_batch_size` if not set. - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether to return the output as a dict or 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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).`~models.transformer_2d.Transformer2DModelOutput` or `tuple`If `return_dict` is True, a `~models.transformer_2d.Transformer2DModelOutput` whose `sample` is the denoised video latent is returned, otherwise a plain `tuple` whose first element is that tensor is returned. Forward pass of Wan2.2-Animate transformer model. ## 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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).