#
Transformer Temporal

A Transformer model for video-like data.

##
TransformerTemporalModel

###
class diffusers.models.TransformerTemporalModel

< source >

( 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

< source >

( 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

< source >

( 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`.