import{s as mo,o as fo,n as po}from"../chunks/scheduler.182ea377.js";import{S as uo,i as _o,g as i,s as r,r as d,A as go,h as a,f as o,c as n,j as _,u as c,x as b,k as g,y as u,a as s,v as l,d as m,t as f,w as p}from"../chunks/index.abf12888.js";import{T as ho}from"../chunks/Tip.230e2334.js";import{D as A}from"../chunks/Docstring.93f6f462.js";import{H as $}from"../chunks/Heading.16916d63.js";function $o(qe){let h,N="This API is currently 🧪 experimental in nature and can change in future.";return{c(){h=i("p"),h.textContent=N},l(v){h=a(v,"P",{"data-svelte-h":!0}),b(h)!=="svelte-2dfli5"&&(h.textContent=N)},m(v,ve){s(v,h,ve)},p:po,d(v){v&&o(h)}}}function Ao(qe){let h,N,v,ve,q,Ee,E,Ht="An attention processor is a class for applying different types of attention mechanisms.",Ie,I,Oe,x,O,zt,Pe,Mt="Default processor for performing attention-related computations.",Se,S,We,y,W,Vt,xe,Gt="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",je,j,Be,C,B,Rt,ye,Jt=`Processor for performing attention-related computations with extra learnable key and value matrices for the text
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dot-product attention.`,et,ee,tt,k,te,Et,Te,to="Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.",ot,oe,st,P,se,It,ke,oo=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).
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The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.out_bias",description:`<strong>out_bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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The dropout probability to use.`,name:"dropout"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The base
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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;
The dimension of the LoRA update matrices.`,name:"rank"},{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.network_alpha",description:`<strong>network_alpha</strong> (<code>int</code>, <em>optional</em>) &#x2014;
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Additional keyword arguments to pass to the <code>LoRALinearLayer</code> layers.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L2012"}}),fe=new $({props:{title:"LoRAXFormersAttnProcessor",local:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor",headingTag:"h2"}}),pe=new A({props:{name:"class diffusers.models.attention_processor.LoRAXFormersAttnProcessor",anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor",parameters:[{name:"hidden_size",val:": int"},{name:"cross_attention_dim",val:": int"},{name:"rank",val:": int = 4"},{name:"attention_op",val:": Optional = None"},{name:"network_alpha",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.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.LoRAXFormersAttnProcessor.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.rank",description:`<strong>rank</strong> (<code>int</code>, defaults to 4) &#x2014;
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
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