import{S as kt,i as Mt,s as Tt,e as n,k as u,w as V,t as o,M as At,c as a,d as r,m as h,a as s,x as J,h as i,b as d,G as e,g as v,y as U,L as Ct,q as W,o as j,B as z,v as Ft}from"../../../chunks/vendor-hf-doc-builder.js";import{D as nt}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as at}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Ot(st){let _,ie,b,w,K,E,_e,Q,be,de,P,$,X,D,Pe,Y,ye,le,g,we,N,$e,xe,k,Se,Re,M,Ee,ce,y,x,Z,T,De,ee,ke,pe,c,A,Me,te,Te,Ae,l,L,Ce,Fe,re,Oe,Ie,ne,Ne,Le,ae,qe,Be,q,Ge,He,B,Ve,Je,G,Ue,We,je,H,ze,C,Ke,Qe,S,F,Xe,se,Ye,Ze,R,O,et,oe,tt,ue;return E=new at({}),D=new at({}),T=new at({}),A=new nt({props:{name:"class diffusers.RePaintScheduler",anchor:"diffusers.RePaintScheduler",parameters:[{name:"num_train_timesteps",val:": int = 1000"},{name:"beta_start",val:": float = 0.0001"},{name:"beta_end",val:": float = 0.02"},{name:"beta_schedule",val:": str = 'linear'"},{name:"eta",val:": float = 0.0"},{name:"trained_betas",val:": typing.Optional[numpy.ndarray] = None"},{name:"clip_sample",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.RePaintScheduler.num_train_timesteps",description:"<strong>num_train_timesteps</strong> (<code>int</code>) &#x2014; number of diffusion steps used to train the model.",name:"num_train_timesteps"},{anchor:"diffusers.RePaintScheduler.beta_start",description:"<strong>beta_start</strong> (<code>float</code>) &#x2014; the starting <code>beta</code> value of inference.",name:"beta_start"},{anchor:"diffusers.RePaintScheduler.beta_end",description:"<strong>beta_end</strong> (<code>float</code>) &#x2014; the final <code>beta</code> value.",name:"beta_end"},{anchor:"diffusers.RePaintScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</code>) &#x2014;
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current instance of sample being created by diffusion process.`,name:"sample"},{anchor:"diffusers.RePaintScheduler.step.original_image",description:`<strong>original_image</strong> (<code>torch.FloatTensor</code>) &#x2014;
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<p><code>~schedulers.scheduling_utils.RePaintSchedulerOutput</code> if <code>return_dict</code> is True, otherwise a <code>tuple</code>. When
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`,returnType:`
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