A Transformer model for video-like data.
( 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
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.
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.
( 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
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.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 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.
( sample: FloatTensor )
The output of TransformerTemporalModel
.