diffusers/pr_14333/en/api/models/latte_transformer3d.md

LatteTransformer3DModel

A Diffusion Transformer model for 3D data from Latte.

LatteTransformer3DModel[[diffusers.LatteTransformer3DModel]]

diffusers.LatteTransformer3DModel[[diffusers.LatteTransformer3DModel]]

diffusers.LatteTransformer3DModel(num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: int | None = None, out_channels: int | None = None, num_layers: int = 1, dropout: float = 0.0, cross_attention_dim: int | None = None, attention_bias: bool = False, sample_size: int = 64, patch_size: int | None = None, activation_fn: str = 'geglu', num_embeds_ada_norm: int | None = None, norm_type: str = 'layer_norm', norm_elementwise_affine: bool = True, norm_eps: float = 1e-05, caption_channels: int = None, video_length: int = 16)

Source

forward[[diffusers.LatteTransformer3DModel.forward]]

forward(hidden_states: Tensor, timestep: typing.Optional[torch.LongTensor] = None, encoder_hidden_states: typing.Optional[torch.Tensor] = None, encoder_attention_mask: typing.Optional[torch.Tensor] = None, enable_temporal_attentions: bool = True, return_dict: bool = True)

Source

Parameters:

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.

Returns:

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.