#
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: Optional = None out_channels: Optional = None num_layers: int = 1 dropout: float = 0.0 norm_num_groups: int = 32 cross_attention_dim: Optional = None attention_bias: bool = False sample_size: Optional = None activation_fn: str = 'geglu' norm_elementwise_affine: bool = True double_self_attention: bool = True positional_embeddings: Optional = None num_positional_embeddings: Optional = None )

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

< source >

( hidden_states: FloatTensor encoder_hidden_states: Optional = None timestep: Optional = None class_labels: LongTensor = None num_frames: int = 1 cross_attention_kwargs: Optional = None return_dict: bool = True ) →
`~models.transformer_temporal.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.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 UNet2DConditionOutput instead of a plain tuple.

Returns

`~models.transformer_temporal.TransformerTemporalModelOutput` or `tuple`

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

The `TransformerTemporal` forward method.

##
TransformerTemporalModelOutput

###
class diffusers.models.transformers.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`.

< > Update on GitHub