#
MochiTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in Mochi-1 Preview by Genmo.

The model can be loaded with the following code snippet.

    from diffusers import MochiTransformer3DModel

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

##
MochiTransformer3DModel

###
class diffusers.MochiTransformer3DModel

< source >

( 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: typing.Optional[int] = 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 )

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.

##
Transformer2DModelOutput

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class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( sample: torch.Tensor )

Parameters

- sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if 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.

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