import{S as Ze,i as et,s as tt,e as r,k as c,w as G,t as f,M as at,c as i,d as t,m as d,a as s,x as C,h,b as o,G as a,g as p,y as U,q as z,o as F,B,v as rt}from"../../../chunks/vendor-hf-doc-builder.js";import{T as it}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Oe}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Ne}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function st(ie){let l,T,u,_,b,m,K,P;return{c(){l=r("p"),T=f("Make sure to check out the Schedulers "),u=r("a"),_=f("guide"),b=f(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),m=r("a"),K=f("reuse components across pipelines"),P=f(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(E){l=i(E,"P",{});var g=s(l);T=h(g,"Make sure to check out the Schedulers "),u=i(g,"A",{href:!0});var y=s(u);_=h(y,"guide"),y.forEach(t),b=h(g," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),m=i(g,"A",{href:!0});var $=s(m);K=h($,"reuse components across pipelines"),$.forEach(t),P=h(g," section to learn how to efficiently load the same components into multiple pipelines."),g.forEach(t),this.h()},h(){o(u,"href","/using-diffusers/schedulers"),o(m,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(E,g){p(E,l,g),a(l,T),a(l,u),a(u,_),a(l,b),a(l,m),a(m,K),a(l,P)},d(E){E&&t(l)}}}function nt(ie){let l,T,u,_,b,m,K,P,E,g,y,$,ge,_e,se,H,ve,ne,R,j,ye,oe,V,le,k,S,Q,D,$e,X,we,pe,v,O,be,Y,Pe,Ee,w,N,ke,Z,Ie,xe,ee,Te,ce,I,A,te,L,Ve,ae,Se,de,x,M,Ae,re,Ke,ue;return m=new Ne({}),V=new it({props:{$$slots:{default:[st]},$$scope:{ctx:ie}}}),D=new Ne({}),O=new Oe({props:{name:"class diffusers.KarrasVePipeline",anchor:"diffusers.KarrasVePipeline",parameters:[{name:"unet",val:": UNet2DModel"},{name:"scheduler",val:": KarrasVeScheduler"}],parametersDescription:[{anchor:"diffusers.KarrasVePipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
A <code>UNet2DModel</code> to denoise the encoded image.`,name:"unet"},{anchor:"diffusers.KarrasVePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/stochastic_karras_ve#diffusers.KarrasVeScheduler">KarrasVeScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py#L25"}}),N=new Oe({props:{name:"__call__",anchor:"diffusers.KarrasVePipeline.__call__",parameters:[{name:"batch_size",val:": int = 1"},{name:"num_inference_steps",val:": int = 50"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.KarrasVePipeline.__call__.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
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generation deterministic.`,name:"generator"},{anchor:"diffusers.KarrasVePipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
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expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.KarrasVePipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
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<p>If <code>return_dict</code> is <code>True</code>, <a
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