import{S as K,i as Q,s as V,e as a,k as $,w as W,t as p,M as X,c as o,d as t,m as D,a as c,x as Z,h as f,b as u,G as i,g as _,y as ee,L as te,q as ie,o as ne,B as ae,v as oe}from"../../chunks/vendor-hf-doc-builder.js";import{I as se}from"../../chunks/IconCopyLink-hf-doc-builder.js";function re(Y){let s,P,r,d,b,m,q,k,C,S,n,N,y,O,L,v,M,T,g,j,F,A,w,I,E;return m=new se({}),{c(){s=a("meta"),P=$(),r=a("h1"),d=a("a"),b=a("span"),W(m.$$.fragment),q=$(),k=a("span"),C=p("Overview"),S=$(),n=a("p"),N=p("A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like "),y=a("a"),O=p("StableDiffusionPipeline"),L=p(" or "),v=a("a"),M=p("StableDiffusionControlNetPipeline"),T=p(", with specific capabilities. All pipeline types inherit from the base "),g=a("a"),j=p("DiffusionPipeline"),F=p(" class; pass it any checkpoint, and it\u2019ll automatically detect the pipeline type and load the necessary components."),A=$(),w=a("p"),I=p("This section introduces you to some of the tasks supported by our pipelines such as unconditional image generation and different techniques and variations of text-to-image generation. You\u2019ll also learn how to gain more control over the generation process by setting a seed for reproducibility and weighting prompts to adjust the influence certain words in the prompt has over the output. Finally, you\u2019ll see how you can create a community pipeline for a custom task like generating images from speech."),this.h()},l(e){const l=X('[data-svelte="svelte-1phssyn"]',document.head);s=o(l,"META",{name:!0,content:!0}),l.forEach(t),P=D(e),r=o(e,"H1",{class:!0});var x=c(r);d=o(x,"A",{id:!0,class:!0,href:!0});var B=c(d);b=o(B,"SPAN",{});var G=c(b);Z(m.$$.fragment,G),G.forEach(t),B.forEach(t),q=D(x),k=o(x,"SPAN",{});var H=c(k);C=f(H,"Overview"),H.forEach(t),x.forEach(t),S=D(e),n=o(e,"P",{});var h=c(n);N=f(h,"A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like "),y=o(h,"A",{href:!0});var J=c(y);O=f(J,"StableDiffusionPipeline"),J.forEach(t),L=f(h," or "),v=o(h,"A",{href:!0});var R=c(v);M=f(R,"StableDiffusionControlNetPipeline"),R.forEach(t),T=f(h,", with specific capabilities. All pipeline types inherit from the base "),g=o(h,"A",{href:!0});var U=c(g);j=f(U,"DiffusionPipeline"),U.forEach(t),F=f(h," class; pass it any checkpoint, and it\u2019ll automatically detect the pipeline type and load the necessary components."),h.forEach(t),A=D(e),w=o(e,"P",{});var z=c(w);I=f(z,"This section introduces you to some of the tasks supported by our pipelines such as unconditional image generation and different techniques and variations of text-to-image generation. You\u2019ll also learn how to gain more control over the generation process by setting a seed for reproducibility and weighting prompts to adjust the influence certain words in the prompt has over the output. Finally, you\u2019ll see how you can create a community pipeline for a custom task like generating images from speech."),z.forEach(t),this.h()},h(){u(s,"name","hf:doc:metadata"),u(s,"content",JSON.stringify(le)),u(d,"id","overview"),u(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"),u(d,"href","#overview"),u(r,"class","relative group"),u(y,"href","/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline"),u(v,"href","/docs/diffusers/v0.18.0/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline"),u(g,"href","/docs/diffusers/v0.18.0/en/api/diffusion_pipeline#diffusers.DiffusionPipeline")},m(e,l){i(document.head,s),_(e,P,l),_(e,r,l),i(r,d),i(d,b),ee(m,b,null),i(r,q),i(r,k),i(k,C),_(e,S,l),_(e,n,l),i(n,N),i(n,y),i(y,O),i(n,L),i(n,v),i(v,M),i(n,T),i(n,g),i(g,j),i(n,F),_(e,A,l),_(e,w,l),i(w,I),E=!0},p:te,i(e){E||(ie(m.$$.fragment,e),E=!0)},o(e){ne(m.$$.fragment,e),E=!1},d(e){t(s),e&&t(P),e&&t(r),ae(m),e&&t(S),e&&t(n),e&&t(A),e&&t(w)}}}const le={local:"overview",title:"Overview"};function pe(Y){return oe(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ue extends K{constructor(s){super();Q(this,s,pe,re,V,{})}}export{ue as default,le as metadata};
