diffusers/pr_14234/en/api/models/transformer_temporal.md

TransformerTemporalModel

A Transformer model for video-like data.

TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

diffusers.TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

diffusers.TransformerTemporalModel(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, norm_num_groups: int = 32, cross_attention_dim: int | None = None, attention_bias: bool = False, sample_size: int | None = None, activation_fn: str = 'geglu', norm_elementwise_affine: bool = True, double_self_attention: bool = True, positional_embeddings: str | None = None, num_positional_embeddings: int | None = None)

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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.

attention_bias (bool, optional) : Configure if the TransformerBlock attention should contain a bias parameter.

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. See diffusers.models.activations.get_activation for supported activation functions.

norm_elementwise_affine (bool, optional) : Configure if the TransformerBlock should use learnable elementwise affine parameters for normalization.

double_self_attention (bool, optional) : Configure if each TransformerBlock should contain two self-attention layers.

positional_embeddings : (str, optional): The type of positional embeddings to apply to the sequence input before passing use.

num_positional_embeddings : (int, optional): The maximum length of the sequence over which to apply positional embeddings.

A Transformer model for video-like data.

forward[[diffusers.TransformerTemporalModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: typing.Optional[torch.LongTensor] = None, timestep: typing.Optional[torch.LongTensor] = None, class_labels: LongTensor = None, num_frames: int = 1, cross_attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True)

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Parameters:

hidden_states (torch.LongTensor of shape (batch size, num latent pixels) if discrete, torch.Tensor 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.LongTensor, 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.

num_frames (int, optional, defaults to 1) : The number of frames to be processed per batch. This is used to reshape the hidden states.

cross_attention_kwargs (dict, optional) : A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.

return_dict (bool, optional, defaults to True) : Whether or not to return a TransformerTemporalModelOutput 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[[diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput]]

diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput[[diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput]]

diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput(sample: Tensor)

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Parameters:

sample (torch.Tensor 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.