A Transformer model for video-like data.
( 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
int
, optional, defaults to 16) — The number of heads to use for multi-head attention. int
, optional, defaults to 88) — The number of channels in each head. int
, optional) —
The number of channels in the input and output (specify if the input is continuous). int
, optional, defaults to 1) — The number of layers of Transformer blocks to use. float
, optional, defaults to 0.0) — The dropout probability to use. int
, optional) — The number of encoder_hidden_states
dimensions to use. bool
, optional) —
Configure if the TransformerBlock
attention should contain a bias parameter. 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. str
, optional, defaults to "geglu"
) —
Activation function to use in feed-forward. See diffusers.models.activations.get_activation
for supported
activation functions. bool
, optional) —
Configure if the TransformerBlock
should use learnable elementwise affine parameters for normalization. bool
, optional) —
Configure if each TransformerBlock
should contain two self-attention layers. str
, optional):
The type of positional embeddings to apply to the sequence input before passing use. int
, optional):
The maximum length of the sequence over which to apply positional embeddings. A Transformer model for video-like data.
( 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: typing.Optional[typing.Dict[str, typing.Any]] = None return_dict: bool = True ) → TransformerTemporalModelOutput or tuple
Parameters
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. 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. torch.LongTensor
, optional) —
Used to indicate denoising step. Optional timestep to be applied as an embedding in AdaLayerNorm
. 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
. int
, optional, defaults to 1) —
The number of frames to be processed per batch. This is used to reshape the hidden states. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.models.attention_processor. bool
, optional, defaults to True
) —
Whether or not to return a TransformerTemporalModelOutput
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.
( sample: Tensor )
The output of TransformerTemporalModel
.