diffusers/v0.39.0/en/api/models/easyanimate_transformer3d.md

# EasyAnimateTransformer3DModel

A Diffusion Transformer model for 3D data from [EasyAnimate](https://github.com/aigc-apps/EasyAnimate) was introduced by Alibaba PAI.

The model can be loaded with the following code snippet.

```python
from diffusers import EasyAnimateTransformer3DModel

transformer = EasyAnimateTransformer3DModel.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
```

## EasyAnimateTransformer3DModel[[diffusers.EasyAnimateTransformer3DModel]]

#### diffusers.EasyAnimateTransformer3DModel[[diffusers.EasyAnimateTransformer3DModel]]

[Source](https://github.com/huggingface/diffusers/blob/v0.39.0/src/diffusers/models/transformers/transformer_easyanimate.py#L316)

A Transformer model for video-like data in [EasyAnimate](https://github.com/aigc-apps/EasyAnimate).

forwarddiffusers.EasyAnimateTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/v0.39.0/src/diffusers/models/transformers/transformer_easyanimate.py#L461[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": Tensor"}, {"name": "timestep_cond", "val": ": torch.Tensor | None = None"}, {"name": "encoder_hidden_states", "val": ": torch.Tensor | None = None"}, {"name": "encoder_hidden_states_t5", "val": ": torch.Tensor | None = None"}, {"name": "inpaint_latents", "val": ": torch.Tensor | None = None"}, {"name": "control_latents", "val": ": torch.Tensor | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- **hidden_states** (`torch.Tensor` of shape `(batch_size, channels, num_frames, height, width)`) --
  Input `hidden_states`.
- **timestep** (`torch.LongTensor`) --
  Used to indicate denoising step.
- **timestep_cond** (`torch.Tensor`, *optional*) --
  Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
  through the `self.time_embedding` layer to obtain the final timestep embeddings.
- **encoder_hidden_states** (`torch.Tensor`, *optional*) --
  Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
- **encoder_hidden_states_t5** (`torch.Tensor`, *optional*) --
  Additional conditional embeddings computed from a T5 text encoder.
- **inpaint_latents** (`torch.Tensor`, *optional*) --
  Latents concatenated to `hidden_states` for inpainting variants of the model.
- **control_latents** (`torch.Tensor`, *optional*) --
  Latents concatenated to `hidden_states` for control variants of the model.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.0If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The [EasyAnimateTransformer3DModel](/docs/diffusers/v0.39.0/en/api/models/easyanimate_transformer3d#diffusers.EasyAnimateTransformer3DModel) forward method.

**Parameters:**

num_attention_heads (`int`, defaults to `48`) : The number of heads to use for multi-head attention.

attention_head_dim (`int`, defaults to `64`) : The number of channels in each head.

in_channels (`int`, defaults to `16`) : The number of channels in the input.

out_channels (`int`, *optional*, defaults to `16`) : The number of channels in the output.

patch_size (`int`, defaults to `2`) : The size of the patches to use in the patch embedding layer.

sample_width (`int`, defaults to `90`) : The width of the input latents.

sample_height (`int`, defaults to `60`) : The height of the input latents.

activation_fn (`str`, defaults to `"gelu-approximate"`) : Activation function to use in feed-forward.

timestep_activation_fn (`str`, defaults to `"silu"`) : Activation function to use when generating the timestep embeddings.

num_layers (`int`, defaults to `30`) : The number of layers of Transformer blocks to use.

mmdit_layers (`int`, defaults to `1000`) : The number of layers of Multi Modal Transformer blocks to use.

dropout (`float`, defaults to `0.0`) : The dropout probability to use.

time_embed_dim (`int`, defaults to `512`) : Output dimension of timestep embeddings.

text_embed_dim (`int`, defaults to `4096`) : Input dimension of text embeddings from the text encoder.

norm_eps (`float`, defaults to `1e-5`) : The epsilon value to use in normalization layers.

norm_elementwise_affine (`bool`, defaults to `True`) : Whether to use elementwise affine in normalization layers.

flip_sin_to_cos (`bool`, defaults to `True`) : Whether to flip the sin to cos in the time embedding.

time_position_encoding_type (`str`, defaults to `3d_rope`) : Type of time position encoding.

after_norm (`bool`, defaults to `False`) : Flag to apply normalization after.

resize_inpaint_mask_directly (`bool`, defaults to `True`) : Flag to resize inpaint mask directly.

enable_text_attention_mask (`bool`, defaults to `True`) : Flag to enable text attention mask.

add_noise_in_inpaint_model (`bool`, defaults to `False`) : Flag to add noise in inpaint model.

**Returns:**

If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

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

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

[Source](https://github.com/huggingface/diffusers/blob/v0.39.0/src/diffusers/models/modeling_outputs.py#L21)

The output of [Transformer2DModel](/docs/diffusers/v0.39.0/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/v0.39.0/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.