diffusers/pr_13751/en/api/models/transformer_temporal.md

TransformerTemporalModel

A Transformer model for video-like data.

TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

diffusers.TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

Source

A Transformer model for video-like data.

forwarddiffusers.TransformerTemporalModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/transformers/transformer_temporal.py#L123[{"name": "hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": torch.LongTensor | None = None"}, {"name": "timestep", "val": ": torch.LongTensor | None = None"}, {"name": "class_labels", "val": ": LongTensor = None"}, {"name": "num_frames", "val": ": int = 1"}, {"name": "cross_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0TransformerTemporalModelOutput or tupleIf return_dict is True, an TransformerTemporalModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The TransformerTemporal forward method.

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.

Returns:

[TransformerTemporalModelOutput](/docs/diffusers/pr_13751/en/api/models/transformer_temporal#diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput) or tuple``

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

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

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

Source

The output of TransformerTemporalModel.

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.