# 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", dtype=torch.float16).to("cuda")
```

## MochiTransformer3DModel[[diffusers.MochiTransformer3DModel]]

#### diffusers.MochiTransformer3DModel[[diffusers.MochiTransformer3DModel]]

```python
diffusers.MochiTransformer3DModel(patch_size: int = 2, num_attention_heads: int = 24, attention_head_dim: int = 128, num_layers: int = 48, pooled_projection_dim: int = 1536, in_channels: int = 12, out_channels: int | None = None, qk_norm: str = 'rms_norm', text_embed_dim: int = 4096, time_embed_dim: int = 256, activation_fn: str = 'swiglu', max_sequence_length: int = 256)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_mochi.py#L309)

**Parameters:**

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).

#### forward[[diffusers.MochiTransformer3DModel.forward]]

```python
forward(hidden_states: Tensor, encoder_hidden_states: Tensor, timestep: LongTensor, encoder_attention_mask: Tensor, attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_mochi.py#L407)

**Parameters:**

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.

**Returns:** `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]]

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

```python
diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)
```

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

**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.

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