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LatteTransformer3DModel

A Diffusion Transformer model for 3D data from Latte.

LatteTransformer3DModel

class diffusers.LatteTransformer3DModel

< source >

( num_attention_heads: int = 16 attention_head_dim: int = 88 in_channels: Optional = None out_channels: Optional = None num_layers: int = 1 dropout: float = 0.0 cross_attention_dim: Optional = None attention_bias: bool = False sample_size: int = 64 patch_size: Optional = None activation_fn: str = 'geglu' num_embeds_ada_norm: Optional = 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

< source >

( hidden_states: Tensor timestep: Optional = None encoder_hidden_states: Optional = None encoder_attention_mask: Optional = 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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