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Transformer Temporal

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Transformer Temporal

A Transformer model for video-like data.

TransformerTemporalModel

class diffusers.models.transformer_temporal.TransformerTemporalModel

< >

( 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 norm_num_groups: int = 32 cross_attention_dim: typing.Optional[int] = None attention_bias: bool = False sample_size: typing.Optional[int] = None activation_fn: str = 'geglu' norm_elementwise_affine: bool = True double_self_attention: bool = True )

Parameters

  • num_attention_heads (int, optional, defaults to 16) — The number of heads to use for multi-head attention.
  • attention_head_dim (int, optional, defaults to 88) — The number of channels in each head.
  • in_channels (int, optional) — The number of channels in the input and output (specify if the input is continuous).
  • num_layers (int, optional, defaults to 1) — The number of layers of Transformer blocks to use.
  • dropout (float, optional, defaults to 0.0) — The dropout probability to use.
  • cross_attention_dim (int, optional) — The number of encoder_hidden_states dimensions to use.
  • sample_size (int, optional) — The width of the latent images (specify if the input is discrete). This is fixed during training since it is used to learn a number of position embeddings.
  • activation_fn (str, optional, defaults to "geglu") — Activation function to use in feed-forward.
  • attention_bias (bool, optional) — Configure if the TransformerBlock attention should contain a bias parameter.
  • double_self_attention (bool, optional) — Configure if each TransformerBlock should contain two self-attention layers.

A Transformer model for video-like data.

forward

< >

( hidden_states encoder_hidden_states = None timestep = None class_labels = None num_frames = 1 cross_attention_kwargs = None return_dict: bool = True ) → TransformerTemporalModelOutput or tuple

Parameters

  • hidden_states (torch.LongTensor of shape (batch size, num latent pixels) if discrete, torch.FloatTensor of shape (batch size, channel, height, width) if continuous) — Input hidden_states.
  • encoder_hidden_states ( torch.LongTensor of shape (batch size, encoder_hidden_states dim), optional) — Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention.
  • timestep ( torch.long, optional) — Used to indicate denoising step. Optional timestep to be applied as an embedding in AdaLayerNorm.
  • class_labels ( torch.LongTensor of shape (batch size, num classes), optional) — Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in AdaLayerZeroNorm.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a UNet2DConditionOutput instead of a plain tuple.

Returns

TransformerTemporalModelOutput or tuple

If return_dict is True, an TransformerTemporalModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The TransformerTemporal forward method.

TransformerTemporalModelOutput

class diffusers.models.transformer_temporal.TransformerTemporalModelOutput

< >

( sample: FloatTensor )

Parameters

  • sample (torch.FloatTensor of shape (batch_size x num_frames, num_channels, height, width)) — The hidden states output conditioned on encoder_hidden_states input.

The output of TransformerTemporalModel.

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