import{s as vt,n as _t,o as Tt}from"../chunks/scheduler.5c93273d.js";import{S as wt,i as Lt,g as a,s as n,r as M,A as xt,h as f,f as l,c as s,j as ht,u as b,x as r,k as nt,y as Ht,a as i,v as E,d as A,t as U,w as P}from"../chunks/index.e43dd92b.js";import{H as V,E as ct}from"../chunks/getInferenceSnippets.7a494bf3.js";function Ct(st){let m,k,y,R,p,q,u,at="<strong>通过混合推理赋能本地 AI 构建者</strong>",z,o,ft=`<p>混合推理是一项<a href="https://huggingface.co/blog/remote_vae" rel="nofollow">实验性功能</a>。
可以在此处提供反馈<a href="https://github.com/huggingface/diffusers/issues/new?template=remote-vae-pilot-feedback.yml" rel="nofollow">此处</a>。</p>`,S,$,j,g,mt="混合推理提供了一种快速简单的方式来卸载本地生成需求。",O,h,rt="<li>🚀 <strong>降低要求：</strong> 无需昂贵硬件即可访问强大模型。</li> <li>💎 <strong>无妥协：</strong> 在不牺牲性能的情况下实现最高质量。</li> <li>💰 <strong>成本效益高：</strong> 它是免费的！🤑</li> <li>🎯 <strong>多样化用例：</strong> 与 Diffusers � 和更广泛的社区完全兼容。</li> <li>🔧 <strong>开发者友好：</strong> 简单请求，快速响应。</li>",D,F,B,v,G,_,ot="<li><strong>VAE 解码 🖼️：</strong> 快速将潜在表示解码为高质量图像，不影响性能或工作流速度。</li> <li><strong>VAE 编码 🔢：</strong> 高效将图像编码为潜在表示，用于生成和训练。</li> <li><strong>文本编码器 📃（即将推出）：</strong> 快速准确地计算提示的文本嵌入，确保流畅高质量的工作流。</li>",K,N,Q,T,J,w,pt='<li><strong><a href="https://github.com/vladmandic/sdnext" rel="nofollow">SD.Next</a>：</strong> 一体化 UI，直接支持混合推理。</li> <li><strong><a href="https://github.com/kijai/ComfyUI-HFRemoteVae" rel="nofollow">ComfyUI-HFRemoteVae</a>：</strong> 用于混合推理的 ComfyUI 节点。</li>',W,L,X,x,ut="<li>2025 年 3 月 10 日：添加了 VAE 编码</li> <li>2025 年 3 月 2 日：初始发布，包含 VAE 解码</li>",Y,H,Z,c,$t="文档分为三个部分：",tt,C,gt="<li><strong>VAE 解码</strong> 学习如何使用混合推理进行 VAE 解码的基础知识。</li> <li><strong>VAE 编码</strong> 学习如何使用混合推理进行 VAE 编码的基础知识。</li> <li><strong>API 参考</strong> 深入了解任务特定设置和参数。</li>",et,d,lt,I,it;return p=new V({props:{title:"混合推理",local:"混合推理",headingTag:"h1"}}),$=new V({props:{title:"为什么使用混合推理？",local:"为什么使用混合推理",headingTag:"h2"}}),v=new V({props:{title:"可用模型",local:"可用模型",headingTag:"h2"}}),T=new V({props:{title:"集成",local:"集成",headingTag:"h2"}}),L=new V({props:{title:"更新日志",local:"更新日志",headingTag:"h2"}}),H=new V({props:{title:"内容",local:"内容",headingTag:"h2"}}),d=new ct({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/zh/hybrid_inference/overview.md"}}),{c(){m=a("meta"),k=n(),y=a("p"),R=n(),M(p.$$.fragment),q=n(),u=a("p"),u.innerHTML=at,z=n(),o=a("blockquote"),o.innerHTML=ft,S=n(),M($.$$.fragment),j=n(),g=a("p"),g.textContent=mt,O=n(),h=a("ul"),h.innerHTML=rt,D=n(),F=a("hr"),B=n(),M(v.$$.fragment),G=n(),_=a("ul"),_.innerHTML=ot,K=n(),N=a("hr"),Q=n(),M(T.$$.fragment),J=n(),w=a("ul"),w.innerHTML=pt,W=n(),M(L.$$.fragment),X=n(),x=a("ul"),x.innerHTML=ut,Y=n(),M(H.$$.fragment),Z=n(),c=a("p"),c.textContent=$t,tt=n(),C=a("ul"),C.innerHTML=gt,et=n(),M(d.$$.fragment),lt=n(),I=a("p"),this.h()},l(t){const e=xt("svelte-u9bgzb",document.head);m=f(e,"META",{name:!0,content:!0}),e.forEach(l),k=s(t),y=f(t,"P",{}),ht(y).forEach(l),R=s(t),b(p.$$.fragment,t),q=s(t),u=f(t,"P",{"data-svelte-h":!0}),r(u)!=="svelte-1qp520s"&&(u.innerHTML=at),z=s(t),o=f(t,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),r(o)!=="svelte-1fuuwwh"&&(o.innerHTML=ft),S=s(t),b($.$$.fragment,t),j=s(t),g=f(t,"P",{"data-svelte-h":!0}),r(g)!=="svelte-pie3xf"&&(g.textContent=mt),O=s(t),h=f(t,"UL",{"data-svelte-h":!0}),r(h)!=="svelte-1icnlgm"&&(h.innerHTML=rt),D=s(t),F=f(t,"HR",{}),B=s(t),b(v.$$.fragment,t),G=s(t),_=f(t,"UL",{"data-svelte-h":!0}),r(_)!=="svelte-dmne87"&&(_.innerHTML=ot),K=s(t),N=f(t,"HR",{}),Q=s(t),b(T.$$.fragment,t),J=s(t),w=f(t,"UL",{"data-svelte-h":!0}),r(w)!=="svelte-n12d76"&&(w.innerHTML=pt),W=s(t),b(L.$$.fragment,t),X=s(t),x=f(t,"UL",{"data-svelte-h":!0}),r(x)!=="svelte-5ojv2r"&&(x.innerHTML=ut),Y=s(t),b(H.$$.fragment,t),Z=s(t),c=f(t,"P",{"data-svelte-h":!0}),r(c)!=="svelte-7wavoz"&&(c.textContent=$t),tt=s(t),C=f(t,"UL",{"data-svelte-h":!0}),r(C)!=="svelte-2isdzp"&&(C.innerHTML=gt),et=s(t),b(d.$$.fragment,t),lt=s(t),I=f(t,"P",{}),ht(I).forEach(l),this.h()},h(){nt(m,"name","hf:doc:metadata"),nt(m,"content",dt),nt(o,"class","tip")},m(t,e){Ht(document.head,m),i(t,k,e),i(t,y,e),i(t,R,e),E(p,t,e),i(t,q,e),i(t,u,e),i(t,z,e),i(t,o,e),i(t,S,e),E($,t,e),i(t,j,e),i(t,g,e),i(t,O,e),i(t,h,e),i(t,D,e),i(t,F,e),i(t,B,e),E(v,t,e),i(t,G,e),i(t,_,e),i(t,K,e),i(t,N,e),i(t,Q,e),E(T,t,e),i(t,J,e),i(t,w,e),i(t,W,e),E(L,t,e),i(t,X,e),i(t,x,e),i(t,Y,e),E(H,t,e),i(t,Z,e),i(t,c,e),i(t,tt,e),i(t,C,e),i(t,et,e),E(d,t,e),i(t,lt,e),i(t,I,e),it=!0},p:_t,i(t){it||(A(p.$$.fragment,t),A($.$$.fragment,t),A(v.$$.fragment,t),A(T.$$.fragment,t),A(L.$$.fragment,t),A(H.$$.fragment,t),A(d.$$.fragment,t),it=!0)},o(t){U(p.$$.fragment,t),U($.$$.fragment,t),U(v.$$.fragment,t),U(T.$$.fragment,t),U(L.$$.fragment,t),U(H.$$.fragment,t),U(d.$$.fragment,t),it=!1},d(t){t&&(l(k),l(y),l(R),l(q),l(u),l(z),l(o),l(S),l(j),l(g),l(O),l(h),l(D),l(F),l(B),l(G),l(_),l(K),l(N),l(Q),l(J),l(w),l(W),l(X),l(x),l(Y),l(Z),l(c),l(tt),l(C),l(et),l(lt),l(I)),l(m),P(p,t),P($,t),P(v,t),P(T,t),P(L,t),P(H,t),P(d,t)}}}const dt='{"title":"混合推理","local":"混合推理","sections":[{"title":"为什么使用混合推理？","local":"为什么使用混合推理","sections":[],"depth":2},{"title":"可用模型","local":"可用模型","sections":[],"depth":2},{"title":"集成","local":"集成","sections":[],"depth":2},{"title":"更新日志","local":"更新日志","sections":[],"depth":2},{"title":"内容","local":"内容","sections":[],"depth":2}],"depth":1}';function Mt(st){return Tt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ut extends wt{constructor(m){super(),Lt(this,m,Mt,Ct,vt,{})}}export{Ut as component};
