diffusers/pr_13966/en/api/models/latte_transformer3d.md

LatteTransformer3DModel

A Diffusion Transformer model for 3D data from Latte.

LatteTransformer3DModel[[diffusers.LatteTransformer3DModel]]

• hidden_states (torch.Tensor of shape (batch size, channel, num_frame, height, width)) -- Input hidden_states.

• timestep ( torch.LongTensor, optional) -- Used to indicate denoising step. Optional timestep to be applied as an embedding in AdaLayerNorm.

• encoder_hidden_states ( torch.FloatTensor of shape (batch size, sequence len, embed dims), optional) -- Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention.

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

  • Mask (batcheight, sequence_length) True = keep, False = discard.
  • Bias (batcheight, 1, sequence_length) 0 = keep, -10000 = discard.

  If ndim == 2: will be interpreted as a mask, then converted into a bias consistent with the format above. This bias will be added to the cross-attention scores.

• enable_temporal_attentions -- (bool, optional, defaults to True): Whether to enable temporal attentions.

• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~models.unet_2d_condition.UNet2DConditionOutput instead of a plain tuple.If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The LatteTransformer3DModel forward method.