import{S as lt,i as ct,s as dt,e as a,k as d,w as S,t as o,M as pt,c as s,d as t,m as p,a as n,x as E,h as i,b as l,G as e,g as _,y as V,L as ht,q as w,o as T,B as O,v as ut}from"../../../chunks/vendor-hf-doc-builder.js";import{D as te}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Pr}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function ft(Fr){let v,xe,b,K,ae,L,Me,se,Ce,$e,x,y,ne,N,Le,oe,Ne,Se,D,Ie,I,Ge,qe,Ee,$,k,ie,G,Be,le,Ue,Ve,c,q,He,ce,Ye,Je,A,Re,B,We,je,U,ze,Qe,u,Q,Xe,Ze,de,er,rr,pe,tr,ar,he,sr,nr,X,or,ir,Z,lr,cr,ee,dr,pr,hr,H,ur,Y,fr,mr,gr,g,J,_r,ue,vr,br,fe,xr,$r,P,R,Sr,me,Er,Vr,F,W,wr,ge,Tr,Or,M,j,Kr,_e,yr,Dr,C,z,kr,ve,Ar,we;return L=new Pr({}),N=new Pr({}),G=new Pr({}),q=new te({props:{name:"class diffusers.KarrasVeScheduler",anchor:"diffusers.KarrasVeScheduler",parameters:[{name:"sigma_min",val:": float = 0.02"},{name:"sigma_max",val:": float = 100"},{name:"s_noise",val:": float = 1.007"},{name:"s_churn",val:": float = 80"},{name:"s_min",val:": float = 0.05"},{name:"s_max",val:": float = 50"}],parametersDescription:[{anchor:"diffusers.KarrasVeScheduler.sigma_min",description:"<strong>sigma_min</strong> (<code>float</code>) &#x2014; minimum noise magnitude",name:"sigma_min"},{anchor:"diffusers.KarrasVeScheduler.sigma_max",description:"<strong>sigma_max</strong> (<code>float</code>) &#x2014; maximum noise magnitude",name:"sigma_max"},{anchor:"diffusers.KarrasVeScheduler.s_noise",description:`<strong>s_noise</strong> (<code>float</code>) &#x2014; the amount of additional noise to counteract loss of detail during sampling.
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A reasonable range is [0.2, 80].`,name:"s_max"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_karras_ve.py#L48"}}),J=new te({props:{name:"add_noise_to_input",anchor:"diffusers.KarrasVeScheduler.add_noise_to_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"sigma",val:": float"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_karras_ve.py#L135"}}),R=new te({props:{name:"scale_model_input",anchor:"diffusers.KarrasVeScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"diffusers.KarrasVeScheduler.scale_model_input.sample",description:"<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014; input sample",name:"sample"},{anchor:"diffusers.KarrasVeScheduler.scale_model_input.timestep",description:"<strong>timestep</strong> (<code>int</code>, optional) &#x2014; current timestep",name:"timestep"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_karras_ve.py#L100",returnDescription:`
<p>scaled input sample</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),W=new te({props:{name:"set_timesteps",anchor:"diffusers.KarrasVeScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.KarrasVeScheduler.set_timesteps.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>) &#x2014;
the number of diffusion steps used when generating samples with a pre-trained model.`,name:"num_inference_steps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_karras_ve.py#L114"}}),j=new te({props:{name:"step",anchor:"diffusers.KarrasVeScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"sigma_hat",val:": float"},{name:"sigma_prev",val:": float"},{name:"sample_hat",val:": FloatTensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KarrasVeScheduler.step.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.KarrasVeScheduler.step.sigma_hat",description:"<strong>sigma_hat</strong> (<code>float</code>) &#x2014; TODO",name:"sigma_hat"},{anchor:"diffusers.KarrasVeScheduler.step.sigma_prev",description:"<strong>sigma_prev</strong> (<code>float</code>) &#x2014; TODO",name:"sigma_prev"},{anchor:"diffusers.KarrasVeScheduler.step.sample_hat",description:"<strong>sample_hat</strong> (<code>torch.FloatTensor</code>) &#x2014; TODO",name:"sample_hat"},{anchor:"diffusers.KarrasVeScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014; option for returning tuple rather than KarrasVeOutput class</p>
<p>KarrasVeOutput &#x2014; updated sample in the diffusion chain and derivative (TODO double check).`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_karras_ve.py#L156",returnDescription:`
<p><code>KarrasVeOutput</code> if <code>return_dict</code> is True, otherwise a <code>tuple</code>. When
returning a tuple, the first element is the sample tensor.</p>
`,returnType:`
<p><code>KarrasVeOutput</code> or <code>tuple</code></p>
`}}),z=new te({props:{name:"step_correct",anchor:"diffusers.KarrasVeScheduler.step_correct",parameters:[{name:"model_output",val:": FloatTensor"},{name:"sigma_hat",val:": float"},{name:"sigma_prev",val:": float"},{name:"sample_hat",val:": FloatTensor"},{name:"sample_prev",val:": FloatTensor"},{name:"derivative",val:": FloatTensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KarrasVeScheduler.step_correct.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.KarrasVeScheduler.step_correct.sigma_hat",description:"<strong>sigma_hat</strong> (<code>float</code>) &#x2014; TODO",name:"sigma_hat"},{anchor:"diffusers.KarrasVeScheduler.step_correct.sigma_prev",description:"<strong>sigma_prev</strong> (<code>float</code>) &#x2014; TODO",name:"sigma_prev"},{anchor:"diffusers.KarrasVeScheduler.step_correct.sample_hat",description:"<strong>sample_hat</strong> (<code>torch.FloatTensor</code>) &#x2014; TODO",name:"sample_hat"},{anchor:"diffusers.KarrasVeScheduler.step_correct.sample_prev",description:"<strong>sample_prev</strong> (<code>torch.FloatTensor</code>) &#x2014; TODO",name:"sample_prev"},{anchor:"diffusers.KarrasVeScheduler.step_correct.derivative",description:"<strong>derivative</strong> (<code>torch.FloatTensor</code>) &#x2014; TODO",name:"derivative"},{anchor:"diffusers.KarrasVeScheduler.step_correct.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>) &#x2014; option for returning tuple rather than KarrasVeOutput class",name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_karras_ve.py#L194",returnDescription:`
<p>updated sample in the diffusion chain. derivative (TODO): TODO</p>
`,returnType:`
<p>prev_sample (TODO)</p>
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