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

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[[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.

• 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.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.torch.TensorThe denoised output tensor of shape (batch_size, out_channels, num_frames, height, width).

The 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 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.