import{S as sr,i as or,s as ar,e as n,k as l,w as E,t as a,M as ir,c as s,d as r,m as p,a as o,x as w,h as i,b as d,G as e,g as _,y,L as dr,q as V,o as D,B as T,v as cr}from"../../../chunks/vendor-hf-doc-builder.js";import{D as te}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Ot}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function lr(kt){let v,Se,S,P,re,q,Ce,ne,Ne,be,b,O,se,L,Fe,oe,Me,$e,k,qe,I,Le,Ie,xe,$,A,ae,G,Ge,ie,He,Ee,c,H,Ue,de,We,Be,z,Je,U,Re,je,u,K,ze,Ke,ce,Qe,Xe,le,Ye,Ze,pe,et,tt,Q,rt,nt,X,st,ot,Y,at,it,dt,C,W,ct,ue,lt,pt,g,B,ut,fe,ft,ht,x,mt,he,gt,_t,me,vt,St,bt,N,J,$t,ge,xt,Et,F,R,wt,_e,yt,Vt,M,j,Dt,ve,Tt,we;return q=new Ot({}),L=new Ot({}),G=new Ot({}),H=new te({props:{name:"class diffusers.ScoreSdeVeScheduler",anchor:"diffusers.ScoreSdeVeScheduler",parameters:[{name:"num_train_timesteps",val:": int = 2000"},{name:"snr",val:": float = 0.15"},{name:"sigma_min",val:": float = 0.01"},{name:"sigma_max",val:": float = 1348.0"},{name:"sampling_eps",val:": float = 1e-05"},{name:"correct_steps",val:": int = 1"}],parametersDescription:[{anchor:"diffusers.ScoreSdeVeScheduler.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.ScoreSdeVeScheduler.snr",description:`<strong>snr</strong> (<code>float</code>) &#x2014;
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current instance of sample being created by diffusion process.
generator &#x2014; random number generator.`,name:"sample"},{anchor:"diffusers.ScoreSdeVeScheduler.step_correct.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>) &#x2014; option for returning tuple rather than SchedulerOutput class",name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_sde_ve.py#L216",returnDescription:`
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current instance of sample being created by diffusion process.
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