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LatteTransformer3DModel

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LatteTransformer3DModel

A Diffusion Transformer model for 3D data from Latte.

LatteTransformer3DModel

class diffusers.LatteTransformer3DModel

< >

( num_attention_heads: int = 16attention_head_dim: int = 88in_channels: typing.Optional[int] = Noneout_channels: typing.Optional[int] = Nonenum_layers: int = 1dropout: float = 0.0cross_attention_dim: typing.Optional[int] = Noneattention_bias: bool = Falsesample_size: int = 64patch_size: typing.Optional[int] = Noneactivation_fn: str = 'geglu'num_embeds_ada_norm: typing.Optional[int] = Nonenorm_type: str = 'layer_norm'norm_elementwise_affine: bool = Truenorm_eps: float = 1e-05caption_channels: int = Nonevideo_length: int = 16 )

forward

< >

( hidden_states: Tensortimestep: typing.Optional[torch.LongTensor] = Noneencoder_hidden_states: typing.Optional[torch.Tensor] = Noneencoder_attention_mask: typing.Optional[torch.Tensor] = Noneenable_temporal_attentions: bool = Truereturn_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.

< > Update on GitHub

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