# TransformerTemporalModel

A Transformer model for video-like data.

## TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

- **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.

- **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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a [TransformerTemporalModelOutput](/docs/diffusers/main/en/api/models/transformer_temporal#diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput)
  instead of a plain tuple.[TransformerTemporalModelOutput](/docs/diffusers/main/en/api/models/transformer_temporal#diffusers.models.transformers.transformer_temporal.TransformerTemporalModelOutput) or `tuple`If `return_dict` is True, an
[TransformerTemporalModelOutput](/docs/diffusers/main/en/api/models/transformer_temporal#diffusers.models.transformers.transformer_temporal.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]]

- **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](/docs/diffusers/main/en/api/models/transformer_temporal#diffusers.TransformerTemporalModel).