import{s as Jt,n as Qt,o as Yt}from"../chunks/scheduler.182ea377.js";import{S as Zt,i as eo,g as i,s as r,r as d,A as to,h as a,f as o,c as n,j as _,u as c,x as g,k as h,y as m,a as s,v as l,d as f,t as p,w as u}from"../chunks/index.abf12888.js";import{D as A}from"../chunks/Docstring.93f6f462.js";import{H as $}from"../chunks/Heading.16916d63.js";function oo(Ft){let b,De,Ce,Le,K,Te,X,Kt="An attention processor is a class for applying different types of attention mechanisms.",ke,N,ze,P,q,bt,le,Xt="Default processor for performing attention-related computations.",Re,E,Ve,v,I,Pt,fe,Nt="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",Fe,S,Ke,x,W,vt,pe,qt="Processor for implementing the LoRA attention mechanism.",Xe,O,Ne,y,j,xt,ue,Et=`Processor for implementing the LoRA attention mechanism using PyTorch 2.0’s memory-efficient scaled dot-product
attention.`,qe,B,Ee,C,U,yt,_e,It="Processor for implementing attention for the Custom Diffusion method.",Ie,H,Se,w,M,Ct,he,St=`Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled
dot-product attention.`,We,G,Oe,D,J,wt,ge,Wt=`Processor for performing attention-related computations with extra learnable key and value matrices for the text
encoder.`,je,Q,Be,L,Y,Dt,$e,Ot=`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.`,Ue,Z,He,T,ee,Lt,Ae,jt=`Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text
encoder.`,Me,te,Ge,k,oe,Tt,be,Bt="Processor for implementing memory efficient attention using xFormers.",Je,se,Qe,z,re,kt,Pe,Ut="Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.",Ye,ne,Ze,R,ie,zt,ve,Ht="Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.",et,ae,tt,V,de,Rt,xe,Mt="Processor for implementing sliced attention.",ot,ce,st,F,me,Vt,ye,Gt="Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.",rt,we,nt;return K=new $({props:{title:"Attention Processor",local:"attention-processor",headingTag:"h1"}}),N=new $({props:{title:"AttnProcessor",local:"diffusers.models.attention_processor.AttnProcessor",headingTag:"h2"}}),q=new A({props:{name:"class diffusers.models.attention_processor.AttnProcessor",anchor:"diffusers.models.attention_processor.AttnProcessor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L532"}}),E=new $({props:{title:"AttnProcessor2_0",local:"diffusers.models.attention_processor.AttnProcessor2_0",headingTag:"h2"}}),I=new A({props:{name:"class diffusers.models.attention_processor.AttnProcessor2_0",anchor:"diffusers.models.attention_processor.AttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L968"}}),S=new $({props:{title:"LoRAAttnProcessor",local:"diffusers.models.attention_processor.LoRAAttnProcessor",headingTag:"h2"}}),W=new A({props:{name:"class diffusers.models.attention_processor.LoRAAttnProcessor",anchor:"diffusers.models.attention_processor.LoRAAttnProcessor",parameters:[{name:"hidden_size",val:""},{name:"cross_attention_dim",val:" = None"},{name:"rank",val:" = 4"},{name:"network_alpha",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.models.attention_processor.LoRAAttnProcessor.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
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The dimension of the LoRA update matrices.`,name:"rank"},{anchor:"diffusers.models.attention_processor.LoRAAttnProcessor2_0.network_alpha",description:`<strong>network_alpha</strong> (<code>int</code>, <em>optional</em>) &#x2014;
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The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor.out_bias",description:`<strong>out_bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to include the bias parameter in <code>train_q_out</code>.`,name:"out_bias"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
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The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.out_bias",description:`<strong>out_bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to include the bias parameter in <code>train_q_out</code>.`,name:"out_bias"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability to use.`,name:"dropout"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L1162"}}),G=new $({props:{title:"AttnAddedKVProcessor",local:"diffusers.models.attention_processor.AttnAddedKVProcessor",headingTag:"h2"}}),J=new A({props:{name:"class diffusers.models.attention_processor.AttnAddedKVProcessor",anchor:"diffusers.models.attention_processor.AttnAddedKVProcessor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L697"}}),Q=new $({props:{title:"AttnAddedKVProcessor2_0",local:"diffusers.models.attention_processor.AttnAddedKVProcessor2_0",headingTag:"h2"}}),Y=new A({props:{name:"class diffusers.models.attention_processor.AttnAddedKVProcessor2_0",anchor:"diffusers.models.attention_processor.AttnAddedKVProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L751"}}),Z=new $({props:{title:"LoRAAttnAddedKVProcessor",local:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor",headingTag:"h2"}}),ee=new A({props:{name:"class diffusers.models.attention_processor.LoRAAttnAddedKVProcessor",anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor",parameters:[{name:"hidden_size",val:""},{name:"cross_attention_dim",val:" = None"},{name:"rank",val:" = 4"},{name:"network_alpha",val:" = None"}],parametersDescription:[{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The hidden size of the attention layer.`,name:"hidden_size"},{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.rank",description:`<strong>rank</strong> (<code>int</code>, defaults to 4) &#x2014;
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<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase" rel="nofollow">operator</a> to
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The dimension of the LoRA update matrices.`,name:"rank"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The base
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase" rel="nofollow">operator</a> to
use as the attention operator. It is recommended to set to <code>None</code>, and allow xFormers to choose the best
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