# WanTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in [Wan 2.1](https://github.com/Wan-Video/Wan2.1) by the Alibaba Wan Team.

The model can be loaded with the following code snippet.

```python
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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).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](/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.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](/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).