An attention processor is a class for applying different types of attention mechanisms.
Default processor for performing attention-related computations.
Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).
Processor for performing attention-related computations with extra learnable key and value matrices for the text encoder.
Processor for performing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0), with extra learnable key and value matrices for the text encoder.
( batch_size = 2 )
Cross frame attention processor. Each frame attends the first frame.
( train_kv: bool = True train_q_out: bool = True hidden_size: Optional = None cross_attention_dim: Optional = None out_bias: bool = True dropout: float = 0.0 )
Parameters
bool, defaults to True) —
Whether to newly train the key and value matrices corresponding to the text features. bool, defaults to True) —
Whether to newly train query matrices corresponding to the latent image features. int, optional, defaults to None) —
The hidden size of the attention layer. int, optional, defaults to None) —
The number of channels in the encoder_hidden_states. bool, defaults to True) —
Whether to include the bias parameter in train_q_out. float, optional, defaults to 0.0) —
The dropout probability to use. Processor for implementing attention for the Custom Diffusion method.
( train_kv: bool = True train_q_out: bool = True hidden_size: Optional = None cross_attention_dim: Optional = None out_bias: bool = True dropout: float = 0.0 )
Parameters
bool, defaults to True) —
Whether to newly train the key and value matrices corresponding to the text features. bool, defaults to True) —
Whether to newly train query matrices corresponding to the latent image features. int, optional, defaults to None) —
The hidden size of the attention layer. int, optional, defaults to None) —
The number of channels in the encoder_hidden_states. bool, defaults to True) —
Whether to include the bias parameter in train_q_out. float, optional, defaults to 0.0) —
The dropout probability to use. Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled dot-product attention.
( train_kv: bool = True train_q_out: bool = False hidden_size: Optional = None cross_attention_dim: Optional = None out_bias: bool = True dropout: float = 0.0 attention_op: Optional = None )
Parameters
bool, defaults to True) —
Whether to newly train the key and value matrices corresponding to the text features. bool, defaults to True) —
Whether to newly train query matrices corresponding to the latent image features. int, optional, defaults to None) —
The hidden size of the attention layer. int, optional, defaults to None) —
The number of channels in the encoder_hidden_states. bool, defaults to True) —
Whether to include the bias parameter in train_q_out. float, optional, defaults to 0.0) —
The dropout probability to use. Callable, optional, defaults to None) —
The base
operator to use
as the attention operator. It is recommended to set to None, and allow xFormers to choose the best operator. Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.
Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). It uses fused projection layers. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
This API is currently 🧪 experimental in nature and can change in future.
( hidden_size: int cross_attention_dim: Optional = None rank: int = 4 network_alpha: Optional = None )
Parameters
int, optional) —
The hidden size of the attention layer. int, optional, defaults to None) —
The number of channels in the encoder_hidden_states. int, defaults to 4) —
The dimension of the LoRA update matrices. int, optional) —
Equivalent to alpha but it’s usage is specific to Kohya (A1111) style LoRAs. dict) —
Additional keyword arguments to pass to the LoRALinearLayer layers. Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text encoder.
( hidden_size: int cross_attention_dim: int rank: int = 4 attention_op: Optional = None network_alpha: Optional = None **kwargs )
Parameters
int, optional) —
The hidden size of the attention layer. int, optional) —
The number of channels in the encoder_hidden_states. int, defaults to 4) —
The dimension of the LoRA update matrices. Callable, optional, defaults to None) —
The base
operator to
use as the attention operator. It is recommended to set to None, and allow xFormers to choose the best
operator. int, optional) —
Equivalent to alpha but it’s usage is specific to Kohya (A1111) style LoRAs. dict) —
Additional keyword arguments to pass to the LoRALinearLayer layers. Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.
( slice_size: int )
Processor for implementing sliced attention.
( slice_size )
Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.
( attention_op: Optional = None )
Parameters
Callable, optional, defaults to None) —
The base
operator to
use as the attention operator. It is recommended to set to None, and allow xFormers to choose the best
operator. Processor for implementing memory efficient attention using xFormers.