import{S as At,i as Tt,s as Pt,e as s,k as f,w as L,t as i,M as Ct,c as n,d as r,m as h,a as o,x as I,h as c,b as a,G as e,g,y as U,L as Ot,q,o as V,B,v as Kt}from"../../../chunks/vendor-hf-doc-builder.js";import{D as be}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as nt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Mt(ot){let _,ce,b,$,Y,x,ye,Z,we,de,y,S,ee,D,$e,te,Se,le,v,ke,H,Ee,xe,A,De,He,ue,T,Ae,P,Te,pe,w,k,re,C,Pe,se,Ce,fe,l,O,Oe,z,Ke,K,Me,Ne,d,G,Fe,Le,ne,Ie,Ue,oe,qe,Ve,ae,Be,ze,J,Ge,Je,R,Re,We,W,je,Qe,Xe,j,M,Ye,E,N,Ze,ie,et,tt,Q,F,he;return x=new nt({}),D=new nt({}),C=new nt({}),O=new be({props:{name:"class diffusers.HeunDiscreteScheduler",anchor:"diffusers.HeunDiscreteScheduler",parameters:[{name:"num_train_timesteps",val:": int = 1000"},{name:"beta_start",val:": float = 0.00085"},{name:"beta_end",val:": float = 0.012"},{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'"},{name:"use_karras_sigmas",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.num_train_timesteps",description:"<strong>num_train_timesteps</strong> (<code>int</code>) &#x2014; number of diffusion steps used to train the model. beta_start (<code>float</code>): the",name:"num_train_timesteps"},{anchor:"diffusers.HeunDiscreteScheduler.starting",description:`<strong>starting</strong> <code>beta</code> value of inference. beta_end (<code>float</code>) &#x2014; the final <code>beta</code> value. beta_schedule (<code>str</code>):
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