#
Attention Processor

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

##
AttnProcessor

###
class diffusers.models.attention_processor.AttnProcessor

< source >

( )

Default processor for performing attention-related computations.

###
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).

###
class diffusers.models.attention_processor.AttnAddedKVProcessor

< source >

( )

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

###
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.

###
class diffusers.models.attention_processor.AttnProcessorNPU

< source >

( )

Processor for implementing flash attention using torch_npu. Torch_npu supports only fp16 and bf16 data types. If fp32 is used, F.scaled_dot_product_attention will be used for computation, but the acceleration effect on NPU is not significant.

###
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.

##
Allegro

###
class diffusers.models.attention_processor.AllegroAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is used in the Allegro model. It applies a normalization layer and rotary embedding on the query and key vector.

##
AuraFlow

###
class diffusers.models.attention_processor.AuraFlowAttnProcessor2_0

< source >

( )

Attention processor used typically in processing Aura Flow.

###
class diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0

< source >

( )

Attention processor used typically in processing Aura Flow with fused projections.

##
CogVideoX

###
class diffusers.models.attention_processor.CogVideoXAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors, but does not include spatial normalization.

###
class diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on query and key vectors, but does not include spatial normalization.

##
CrossFrameAttnProcessor

###
class diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor

< source >

( batch_size = 2 )

Parameters

- batch_size — The number that represents actual batch size, other than the frames. For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to 2, due to classifier-free guidance.

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

##
Custom Diffusion

###
class diffusers.models.attention_processor.CustomDiffusionAttnProcessor

< source >

( 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.

###
class diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0

< source >

( 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.

###
class diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor

< source >

( 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.

##
Flux

###
class diffusers.models.attention_processor.FluxAttnProcessor2_0

< source >

( )

Attention processor used typically in processing the SD3-like self-attention projections.

###
class diffusers.models.attention_processor.FusedFluxAttnProcessor2_0

< source >

( )

Attention processor used typically in processing the SD3-like self-attention projections.

###
class diffusers.models.attention_processor.FluxSingleAttnProcessor2_0

< source >

( )

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

##
Hunyuan

###
class diffusers.models.attention_processor.HunyuanAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.

###
class diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0) with fused projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.

###
class diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is used in the HunyuanDiT model. It applies a normalization layer and rotary embedding on query and key vector. This variant of the processor employs Pertubed Attention Guidance.

###
class diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is used in the HunyuanDiT model. It applies a normalization layer and rotary embedding on query and key vector. This variant of the processor employs Pertubed Attention Guidance.

##
IdentitySelfAttnProcessor2_0

###
class diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0

< source >

( )

Processor for implementing PAG using scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). PAG reference: https://arxiv.org/abs/2403.17377

###
class diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0

< source >

( )

Processor for implementing PAG using scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). PAG reference: https://arxiv.org/abs/2403.17377

##
IP-Adapter

###
class diffusers.models.attention_processor.IPAdapterAttnProcessor

< source >

( hidden_size cross_attention_dim = None num_tokens = (4,) scale = 1.0 )

Parameters

- hidden_size (`int`) — The hidden size of the attention layer.
- cross_attention_dim (`int`) — The number of channels in the `encoder_hidden_states`.
- num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`) — The context length of the image features.
- scale (`float` or List`float`, defaults to 1.0) — the weight scale of image prompt.

Attention processor for Multiple IP-Adapters.

###
class diffusers.models.attention_processor.IPAdapterAttnProcessor2_0

< source >

( hidden_size cross_attention_dim = None num_tokens = (4,) scale = 1.0 )

Parameters

- hidden_size (`int`) — The hidden size of the attention layer.
- cross_attention_dim (`int`) — The number of channels in the `encoder_hidden_states`.
- num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`) — The context length of the image features.
- scale (`float` or `List[float]`, defaults to 1.0) — the weight scale of image prompt.

Attention processor for IP-Adapter for PyTorch 2.0.

##
JointAttnProcessor2_0

###
class diffusers.models.attention_processor.JointAttnProcessor2_0

< source >

( )

Attention processor used typically in processing the SD3-like self-attention projections.

###
class diffusers.models.attention_processor.PAGJointAttnProcessor2_0

< source >

( )

Attention processor used typically in processing the SD3-like self-attention projections.

###
class diffusers.models.attention_processor.PAGCFGJointAttnProcessor2_0

< source >

( )

Attention processor used typically in processing the SD3-like self-attention projections.

###
class diffusers.models.attention_processor.FusedJointAttnProcessor2_0

< source >

( )

Attention processor used typically in processing the SD3-like self-attention projections.

##
LoRA

###
class diffusers.models.attention_processor.LoRAAttnProcessor

< source >

( )

Processor for implementing attention with LoRA.

###
class diffusers.models.attention_processor.LoRAAttnProcessor2_0

< source >

( )

Processor for implementing attention with LoRA (enabled by default if you’re using PyTorch 2.0).

###
class diffusers.models.attention_processor.LoRAAttnAddedKVProcessor

< source >

( )

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

###
class diffusers.models.attention_processor.LoRAXFormersAttnProcessor

< source >

( )

Processor for implementing attention with LoRA using xFormers.

##
Lumina-T2X

###
class diffusers.models.attention_processor.LuminaAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is used in the LuminaNextDiT model. It applies a s normalization layer and rotary embedding on query and key vector.

##
Mochi

###
class diffusers.models.attention_processor.MochiAttnProcessor2_0

< source >

( )

Attention processor used in Mochi.

###
class diffusers.models.attention_processor.MochiVaeAttnProcessor2_0

< source >

( )

Attention processor used in Mochi VAE.

##
Sana

###
class diffusers.models.attention_processor.SanaLinearAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product linear attention.

###
class diffusers.models.attention_processor.SanaMultiscaleAttnProcessor2_0

< source >

( )

Processor for implementing multiscale quadratic attention.

###
class diffusers.models.attention_processor.PAGCFGSanaLinearAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product linear attention.

###
class diffusers.models.attention_processor.PAGIdentitySanaLinearAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product linear attention.

##
Stable Audio

###
class diffusers.models.attention_processor.StableAudioAttnProcessor2_0

< source >

( )

Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is used in the Stable Audio model. It applies rotary embedding on query and key vector, and allows MHA, GQA or MQA.

##
SlicedAttnProcessor

###
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.

###
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: 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.

###
class diffusers.models.attention_processor.XFormersAttnAddedKVProcessor

< source >

( 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.

##
XLAFlashAttnProcessor2_0

###
class diffusers.models.attention_processor.XLAFlashAttnProcessor2_0

< source >

( partition_spec: typing.Optional[typing.Tuple[typing.Optional[str], ...]] = None )

Processor for implementing scaled dot-product attention with pallas flash attention kernel if using `torch_xla`.

< > Update on GitHub