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 )
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. 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. bool
, optional) —
Configure if the TransformerBlock
attention should contain a bias parameter. bool
, optional) —
Configure if each TransformerBlock
should contain two self-attention layers. A Transformer model for video-like data.
( 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
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. 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.long
, 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 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.
( sample: FloatTensor )
The output of TransformerTemporalModel
.