# 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]]

#### diffusers.WanAnimateTransformer3DModel[[diffusers.WanAnimateTransformer3DModel]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_wan_animate.py#L971)

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

forwarddiffusers.WanAnimateTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_wan_animate.py#L1138[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": LongTensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states_image", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pose_hidden_states", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "face_pixel_values", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "motion_encode_batch_size", "val": ": typing.Optional[int] = None"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}]- **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.0

Forward pass of Wan2.2-Animate transformer model.

**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.

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.

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

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

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/modeling_outputs.py#L21)

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).

**Parameters:**

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.

