##
LatteTransformer3DModel

A Diffusion Transformer model for 3D data from Latte.

##
LatteTransformer3DModel

###
class diffusers.LatteTransformer3DModel

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

####
forward

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( 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 )

Parameters

- hidden_states 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.

The LatteTransformer3DModel forward method.

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