diffusers/main/en/api/models/allegro_transformer3d.md

AllegroTransformer3DModel

A Diffusion Transformer model for 3D data from Allegro was introduced in Allegro: Open the Black Box of Commercial-Level Video Generation Model by RhymesAI.

The model can be loaded with the following code snippet.

from diffusers import AllegroTransformer3DModel

transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")

AllegroTransformer3DModel[[diffusers.AllegroTransformer3DModel]]

diffusers.AllegroTransformer3DModel[[diffusers.AllegroTransformer3DModel]]

Source

forwarddiffusers.AllegroTransformer3DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_allegro.py#L305[{"name": "hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": LongTensor"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "encoder_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "image_rotary_emb", "val": ": tuple[torch.Tensor, torch.Tensor] | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.
• attention_mask (torch.Tensor, optional) -- Self-attention mask applied to hidden_states.
• encoder_attention_mask (torch.Tensor, optional) -- Cross-attention mask applied to encoder_hidden_states.
• image_rotary_emb (tuple of torch.Tensor, optional) -- Pre-computed rotary positional embeddings.
• 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 AllegroTransformer3DModel forward method.

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.

attention_mask (torch.Tensor, optional) : Self-attention mask applied to hidden_states.

encoder_attention_mask (torch.Tensor, optional) : Cross-attention mask applied to encoder_hidden_states.

image_rotary_emb (tuple of torch.Tensor, optional) : Pre-computed rotary positional embeddings.

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

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

The output of 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 is discrete) : The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.