import{S as At,i as Pt,s as Tt,e as s,k as p,w as F,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 Mt,q,o as V,B,v as Ot}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 Nt(ot){let _,ce,b,$,Y,x,ye,Z,we,de,y,S,ee,D,$e,te,Se,le,v,Ee,H,ke,xe,A,De,He,ue,P,Ae,T,Pe,fe,w,E,re,C,Te,se,Ce,pe,l,M,Me,G,Oe,O,Ne,Ke,d,J,Le,Fe,ne,Ie,Ue,oe,qe,Ve,ae,Be,Ge,R,Je,Re,W,We,je,j,ze,Qe,Xe,z,N,Ye,k,K,Ze,ie,et,tt,Q,L,he;return x=new nt({}),D=new nt({}),C=new nt({}),M=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'"}],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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`}}),K=new be({props:{name:"set_timesteps",anchor:"diffusers.HeunDiscreteScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"},{name:"num_train_timesteps",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.set_timesteps.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>) &#x2014;
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