import{S as B,i as G,s as J,e as l,k as q,w as R,t as p,M as U,c as r,d as o,m as T,a as h,x as j,h as m,b as c,G as t,g as y,y as z,L as Q,q as X,o as Z,B as ee,v as oe}from"../../chunks/vendor-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";function ae(M){let n,E,s,d,_,u,S,g,A,b,a,C,k,D,H,f,O,F,$,v,L,x;return u=new te({}),{c(){n=l("meta"),E=q(),s=l("h1"),d=l("a"),_=l("span"),R(u.$$.fragment),S=q(),g=l("span"),A=p("Overview"),b=q(),a=l("p"),C=p("\u{1F9E8} Diffusers offers many pipelines, models, and schedulers for generative tasks. To make loading these components as simple as possible, we provide a single and unified method - "),k=l("code"),D=p("from_pretrained()"),H=p(" - that loads any of these components from either the Hugging Face "),f=l("a"),O=p("Hub"),F=p(" or your local machine. Whenever you load a pipeline or model, the latest files are automatically downloaded and cached so you can quickly reuse them next time without redownloading the files."),$=q(),v=l("p"),L=p("This section will show you everything you need to know about loading pipelines, how to load different components in a pipeline, how to load checkpoint variants, and how to load community pipelines. You\u2019ll also learn how to load schedulers and compare the speed and quality trade-offs of using different schedulers. Finally, you\u2019ll see how to convert and load KerasCV checkpoints so you can use them in PyTorch with \u{1F9E8} Diffusers."),this.h()},l(e){const i=U('[data-svelte="svelte-1phssyn"]',document.head);n=r(i,"META",{name:!0,content:!0}),i.forEach(o),E=T(e),s=r(e,"H1",{class:!0});var P=h(s);d=r(P,"A",{id:!0,class:!0,href:!0});var N=h(d);_=r(N,"SPAN",{});var I=h(_);j(u.$$.fragment,I),I.forEach(o),N.forEach(o),S=T(P),g=r(P,"SPAN",{});var K=h(g);A=m(K,"Overview"),K.forEach(o),P.forEach(o),b=T(e),a=r(e,"P",{});var w=h(a);C=m(w,"\u{1F9E8} Diffusers offers many pipelines, models, and schedulers for generative tasks. To make loading these components as simple as possible, we provide a single and unified method - "),k=r(w,"CODE",{});var V=h(k);D=m(V,"from_pretrained()"),V.forEach(o),H=m(w," - that loads any of these components from either the Hugging Face "),f=r(w,"A",{href:!0,rel:!0});var W=h(f);O=m(W,"Hub"),W.forEach(o),F=m(w," or your local machine. Whenever you load a pipeline or model, the latest files are automatically downloaded and cached so you can quickly reuse them next time without redownloading the files."),w.forEach(o),$=T(e),v=r(e,"P",{});var Y=h(v);L=m(Y,"This section will show you everything you need to know about loading pipelines, how to load different components in a pipeline, how to load checkpoint variants, and how to load community pipelines. You\u2019ll also learn how to load schedulers and compare the speed and quality trade-offs of using different schedulers. Finally, you\u2019ll see how to convert and load KerasCV checkpoints so you can use them in PyTorch with \u{1F9E8} Diffusers."),Y.forEach(o),this.h()},h(){c(n,"name","hf:doc:metadata"),c(n,"content",JSON.stringify(ne)),c(d,"id","overview"),c(d,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(d,"href","#overview"),c(s,"class","relative group"),c(f,"href","https://huggingface.co/models?library=diffusers&sort=downloads"),c(f,"rel","nofollow")},m(e,i){t(document.head,n),y(e,E,i),y(e,s,i),t(s,d),t(d,_),z(u,_,null),t(s,S),t(s,g),t(g,A),y(e,b,i),y(e,a,i),t(a,C),t(a,k),t(k,D),t(a,H),t(a,f),t(f,O),t(a,F),y(e,$,i),y(e,v,i),t(v,L),x=!0},p:Q,i(e){x||(X(u.$$.fragment,e),x=!0)},o(e){Z(u.$$.fragment,e),x=!1},d(e){o(n),e&&o(E),e&&o(s),ee(u),e&&o(b),e&&o(a),e&&o($),e&&o(v)}}}const ne={local:"overview",title:"Overview"};function se(M){return oe(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class re extends B{constructor(n){super();G(this,n,se,ae,J,{})}}export{re as default,ne as metadata};
