Attention Processor

译者：片刻小哥哥

项目地址：https://huggingface.apachecn.org/docs/diffusers/api/attnprocessor

原始地址：https://huggingface.co/docs/diffusers/api/attnprocessor

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

AttnProcessor

class

diffusers.models.attention_processor.

AttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L694)

(

)

Default processor for performing attention-related computations.

AttnProcessor2_0

class

diffusers.models.attention_processor.

AttnProcessor2_0

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1166)

(

)

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

LoRAAttnProcessor

class

diffusers.models.attention_processor.

LoRAAttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1693)

(

hidden_size

: int

cross_attention_dim

: typing.Optional[int] = None

rank

: int = 4

network_alpha

: typing.Optional[int] = 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.
• 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.

LoRAAttnProcessor2_0

class

diffusers.models.attention_processor.

LoRAAttnProcessor2_0

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1765)

(

hidden_size

: int

cross_attention_dim

: typing.Optional[int] = None

rank

: int = 4

network_alpha

: typing.Optional[int] = None

**kwargs

)

Parameters

• hidden_size ( int ) — 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.
• 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 using PyTorch 2.0’s memory-efficient scaled dot-product attention.

CustomDiffusionAttnProcessor

class

diffusers.models.attention_processor.

CustomDiffusionAttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L763)

(

train_kv

: bool = True

train_q_out

: bool = True

hidden_size

: typing.Optional[int] = None

cross_attention_dim

: typing.Optional[int] = 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

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1370)

(

train_kv

: bool = True

train_q_out

: bool = True

hidden_size

: typing.Optional[int] = None

cross_attention_dim

: typing.Optional[int] = 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.

AttnAddedKVProcessor

class

diffusers.models.attention_processor.

AttnAddedKVProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L867)

(

)

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

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L931)

(

)

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.

LoRAAttnAddedKVProcessor

class

diffusers.models.attention_processor.

LoRAAttnAddedKVProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1919)

(

hidden_size

: int

cross_attention_dim

: typing.Optional[int] = None

rank

: int = 4

network_alpha

: typing.Optional[int] = 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.

XFormersAttnProcessor

class

diffusers.models.attention_processor.

XFormersAttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1075)

(

attention_op

: typing.Optional[typing.Callable] = 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.

LoRAXFormersAttnProcessor

class

diffusers.models.attention_processor.

LoRAXFormersAttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1840)

(

hidden_size

: int

cross_attention_dim

: int

rank

: int = 4

attention_op

: typing.Optional[typing.Callable] = None

network_alpha

: typing.Optional[int] = 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.

CustomDiffusionXFormersAttnProcessor

class

diffusers.models.attention_processor.

CustomDiffusionXFormersAttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1254)

(

train_kv

: bool = True

train_q_out

: bool = False

hidden_size

: typing.Optional[int] = None

cross_attention_dim

: typing.Optional[int] = None

out_bias

: bool = True

dropout

: float = 0.0

attention_op

: typing.Optional[typing.Callable] = 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.

SlicedAttnProcessor

class

diffusers.models.attention_processor.

SlicedAttnProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1484)

(

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

class

diffusers.models.attention_processor.

SlicedAttnAddedKVProcessor

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/models/attention_processor.py#L1571)

(

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.

---

我们一直在努力

apachecn/AiLearning

---