Transformer Temporal

译者：片刻小哥哥

项目地址：https://huggingface.apachecn.org/docs/diffusers/api/models/transformer_temporal

原始地址：https://huggingface.co/docs/diffusers/api/models/transformer_temporal

A Transformer model for video-like data.

TransformerTemporalModel

class

diffusers.models.

TransformerTemporalModel

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/transformer_temporal.py#L39)

(

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

positional_embeddings

: typing.Optional[str] = None

num_positional_embeddings

: typing.Optional[int] = 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

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/transformer_temporal.py#L119)

(

hidden_states

: FloatTensor

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

: typing.Union[typing.Dict[str, typing.Any], NoneType] = None

return_dict

: bool = True

)

→

export const metadata = 'undefined';

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

export const metadata = 'undefined';

TransformerTemporalModelOutput or tuple

export const metadata = 'undefined';

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

The TransformerTemporal forward method.