# TransformerTemporalModel

A Transformer model for video-like data.

## TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

#### diffusers.TransformerTemporalModel[[diffusers.TransformerTemporalModel]]

```python
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)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_temporal.py#L41)

**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]]

```python
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)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_temporal.py#L123)

**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](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.

**Returns:** [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]]

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

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

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_temporal.py#L29)

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