#
Attention Processor

An attention processor is a class for applying different types of attention mechanisms.

##
AttnProcessor

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class diffusers.models.attention_processor.AttnProcessor

< source >

( )

Default processor for performing attention-related computations.

##
AttnProcessor2_0

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class diffusers.models.attention_processor.AttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).

##
AttnAddedKVProcessor

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class diffusers.models.attention_processor.AttnAddedKVProcessor

< source >

( )

Processor for performing attention-related computations with extra learnable key and value matrices for the text encoder.

##
AttnAddedKVProcessor2_0

###
class diffusers.models.attention_processor.AttnAddedKVProcessor2_0

< source >

( )

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.

##
CrossFrameAttnProcessor

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class diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor

< source >

( batch_size = 2 )

Cross frame attention processor. Each frame attends the first frame.

##
CustomDiffusionAttnProcessor

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class diffusers.models.attention_processor.CustomDiffusionAttnProcessor

< source >

( 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

- train_kv (`bool`, defaults to `True`) — Whether to newly train the key and value matrices corresponding to the text features.
- train_q_out (`bool`, defaults to `True`) — Whether to newly train query matrices corresponding to the latent image features.
- hidden_size (`int`, optional, defaults to `None`) — The hidden size of the attention layer.
- cross_attention_dim (`int`, optional, defaults to `None`) — The number of channels in the `encoder_hidden_states`.
- out_bias (`bool`, defaults to `True`) — Whether to include the bias parameter in `train_q_out`.
- dropout (`float`, optional, defaults to 0.0) — The dropout probability to use.

Processor for implementing attention for the Custom Diffusion method.

##
CustomDiffusionAttnProcessor2_0

###
class diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0

< source >

( 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

- train_kv (`bool`, defaults to `True`) — Whether to newly train the key and value matrices corresponding to the text features.
- train_q_out (`bool`, defaults to `True`) — Whether to newly train query matrices corresponding to the latent image features.
- hidden_size (`int`, optional, defaults to `None`) — The hidden size of the attention layer.
- cross_attention_dim (`int`, optional, defaults to `None`) — The number of channels in the `encoder_hidden_states`.
- out_bias (`bool`, defaults to `True`) — Whether to include the bias parameter in `train_q_out`.
- dropout (`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.

##
CustomDiffusionXFormersAttnProcessor

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class diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor

< source >

( 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

- train_kv (`bool`, defaults to `True`) — Whether to newly train the key and value matrices corresponding to the text features.
- train_q_out (`bool`, defaults to `True`) — Whether to newly train query matrices corresponding to the latent image features.
- hidden_size (`int`, optional, defaults to `None`) — The hidden size of the attention layer.
- cross_attention_dim (`int`, optional, defaults to `None`) — The number of channels in the `encoder_hidden_states`.
- out_bias (`bool`, defaults to `True`) — Whether to include the bias parameter in `train_q_out`.
- dropout (`float`, optional, defaults to 0.0) — The dropout probability to use.
- attention_op (`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.

##
FusedAttnProcessor2_0

###
class diffusers.models.attention_processor.FusedAttnProcessor2_0

< source >

( )

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.

##
LoRAAttnAddedKVProcessor

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class diffusers.models.attention_processor.LoRAAttnAddedKVProcessor

< source >

( hidden_size: int cross_attention_dim: Optional = None rank: int = 4 network_alpha: Optional = None )

Parameters

- hidden_size (`int`, optional) — The hidden size of the attention layer.
- cross_attention_dim (`int`, optional, defaults to `None`) — The number of channels in the `encoder_hidden_states`.
- rank (`int`, defaults to 4) — The dimension of the LoRA update matrices.
- network_alpha (`int`, optional) — Equivalent to `alpha` but it’s usage is specific to Kohya (A1111) style LoRAs.
- kwargs (`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.

##
LoRAXFormersAttnProcessor

###
class diffusers.models.attention_processor.LoRAXFormersAttnProcessor

< source >

( hidden_size: int cross_attention_dim: int rank: int = 4 attention_op: Optional = None network_alpha: Optional = None **kwargs )

Parameters

- hidden_size (`int`, optional) — The hidden size of the attention layer.
- cross_attention_dim (`int`, optional) — The number of channels in the `encoder_hidden_states`.
- rank (`int`, defaults to 4) — The dimension of the LoRA update matrices.
- attention_op (`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.
- network_alpha (`int`, optional) — Equivalent to `alpha` but it’s usage is specific to Kohya (A1111) style LoRAs.
- kwargs (`dict`) — Additional keyword arguments to pass to the `LoRALinearLayer` layers.

Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.

##
SlicedAttnProcessor

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class diffusers.models.attention_processor.SlicedAttnProcessor

< source >

( slice_size: int )

Parameters

- slice_size (`int`, optional) — The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and `attention_head_dim` must be a multiple of the `slice_size`.

Processor for implementing sliced attention.

##
SlicedAttnAddedKVProcessor

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class diffusers.models.attention_processor.SlicedAttnAddedKVProcessor

< source >

( slice_size )

Parameters

- slice_size (`int`, optional) — The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and `attention_head_dim` must be a multiple of the `slice_size`.

Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.

##
XFormersAttnProcessor

###
class diffusers.models.attention_processor.XFormersAttnProcessor

< source >

( attention_op: Optional = None )

Parameters

- attention_op (`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.