import{S as Dt,i as xt,s as kt,e as s,k as u,w as O,t as a,M as Tt,c as n,d as r,m as p,a as o,x as I,h as i,b as c,G as e,g,y as B,L as Ct,q as U,o as q,B as K,v as Pt}from"../../../chunks/vendor-hf-doc-builder.js";import{D as be}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as st}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Lt(nt){let _,ae,v,y,j,x,Ee,z,ye,ie,b,w,Q,k,we,X,$e,ce,V,Se,le,E,$,Y,T,Ae,Z,De,de,d,C,xe,G,ke,P,Te,Ce,l,H,Pe,Le,ee,Me,Fe,te,Ne,Oe,re,Ie,Be,R,Ue,qe,J,Ke,Ve,W,Ge,He,Re,S,L,Je,M,We,se,je,ze,Qe,A,F,Xe,ne,Ye,Ze,D,N,et,oe,tt,ue;return x=new st({}),k=new st({}),T=new st({}),C=new be({props:{name:"class diffusers.EulerAncestralDiscreteScheduler",anchor:"diffusers.EulerAncestralDiscreteScheduler",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:"trained_betas",val:": typing.Union[numpy.ndarray, typing.List[float], NoneType] = None"},{name:"prediction_type",val:": str = 'epsilon'"}],parametersDescription:[{anchor:"diffusers.EulerAncestralDiscreteScheduler.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.EulerAncestralDiscreteScheduler.beta_start",description:"<strong>beta_start</strong> (<code>float</code>) &#x2014; the starting <code>beta</code> value of inference.",name:"beta_start"},{anchor:"diffusers.EulerAncestralDiscreteScheduler.beta_end",description:"<strong>beta_end</strong> (<code>float</code>) &#x2014; the final <code>beta</code> value.",name:"beta_end"},{anchor:"diffusers.EulerAncestralDiscreteScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</code>) &#x2014;
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`,returnType:`
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