diffusers/main/en/api/models/mochi_transformer3d.md

# MochiTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in [Mochi-1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Genmo.

The model can be loaded with the following code snippet.

```python
from diffusers import MochiTransformer3DModel

transformer = MochiTransformer3DModel.from_pretrained("genmo/mochi-1-preview", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
```

## MochiTransformer3DModel[[diffusers.MochiTransformer3DModel]]

- **patch_size** (`int`, defaults to `2`) --
  The size of the patches to use in the patch embedding layer.
- **num_attention_heads** (`int`, defaults to `24`) --
  The number of heads to use for multi-head attention.
- **attention_head_dim** (`int`, defaults to `128`) --
  The number of channels in each head.
- **num_layers** (`int`, defaults to `48`) --
  The number of layers of Transformer blocks to use.
- **in_channels** (`int`, defaults to `12`) --
  The number of channels in the input.
- **out_channels** (`int`, *optional*, defaults to `None`) --
  The number of channels in the output.
- **qk_norm** (`str`, defaults to `"rms_norm"`) --
  The normalization layer to use.
- **text_embed_dim** (`int`, defaults to `4096`) --
  Input dimension of text embeddings from the text encoder.
- **time_embed_dim** (`int`, defaults to `256`) --
  Output dimension of timestep embeddings.
- **activation_fn** (`str`, defaults to `"swiglu"`) --
  Activation function to use in feed-forward.
- **max_sequence_length** (`int`, defaults to `256`) --
  The maximum sequence length of text embeddings supported.

A Transformer model for video-like data introduced in [Mochi](https://huggingface.co/genmo/mochi-1-preview).

- **hidden_states** (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`) --
  Input `hidden_states`.
- **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.
- **timestep** (`torch.LongTensor`) --
  Used to indicate denoising step.
- **encoder_attention_mask** (`torch.Tensor`) --
  Mask applied to `encoder_hidden_states` during attention.
- **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).
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.`torch.Tensor`The denoised output tensor of shape `(batch_size, out_channels, num_frames, height, width)`.

The [MochiTransformer3DModel](/docs/diffusers/main/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel) 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).