import{S as jr,i as Qr,s as Xr,e as s,k as l,w as x,t as n,M as Yr,c as a,d as t,m as u,a as o,x as S,h as i,b as d,G as e,g as $,y as V,q as T,o as E,B as K,v as Zr}from"../../../chunks/vendor-hf-doc-builder.js";import{T as et}from"../../../chunks/Tip-hf-doc-builder.js";import{D as ne}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Kr}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function rt(Ve){let h,C,f,b,w,_,U,y;return{c(){h=s("p"),C=n("For more details on the parameters, see "),f=s("a"),b=n("Appendix E"),w=n(`. The grid search values used
to find the optimal `),_=s("code"),U=n("{s_noise, s_churn, s_min, s_max}"),y=n(" for a specific model are described in Table 5 of the paper."),this.h()},l(D){h=a(D,"P",{});var v=o(h);C=i(v,"For more details on the parameters, see "),f=a(v,"A",{href:!0,rel:!0});var g=o(f);b=i(g,"Appendix E"),g.forEach(t),w=i(v,`. The grid search values used
to find the optimal `),_=a(v,"CODE",{});var I=o(_);U=i(I,"{s_noise, s_churn, s_min, s_max}"),I.forEach(t),y=i(v," for a specific model are described in Table 5 of the paper."),v.forEach(t),this.h()},h(){d(f,"href","https://arxiv.org/abs/2206.00364"),d(f,"rel","nofollow")},m(D,v){$(D,h,v),e(h,C),e(h,f),e(f,b),e(h,w),e(h,_),e(_,U),e(h,y)},d(D){D&&t(h)}}}function tt(Ve){let h,C,f,b,w,_,U,y,D,v,g,I,Ne,Le,W,qe,Ge,J,ze,Be,Te,O,M,ue,R,He,he,Ue,Ee,c,j,We,pe,Je,Re,k,je,ie,Qe,Xe,de,Ye,Ze,er,N,rr,L,Q,tr,A,sr,me,ar,or,fe,nr,ir,dr,q,X,cr,ge,lr,ur,G,Y,hr,_e,pr,mr,z,Z,fr,ve,gr,_r,B,ee,vr,re,$r,$e,br,xr,Ke,F,H,be,te,Sr,xe,Vr,we,P,se,Tr,Se,Er,ye;return _=new Kr({}),R=new Kr({}),j=new ne({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>, defaults to 0.02) &#x2014;
The minimum noise magnitude.`,name:"sigma_min"},{anchor:"diffusers.KarrasVeScheduler.sigma_max",description:`<strong>sigma_max</strong> (<code>float</code>, defaults to 100) &#x2014;
The maximum noise magnitude.`,name:"sigma_max"},{anchor:"diffusers.KarrasVeScheduler.s_noise",description:`<strong>s_noise</strong> (<code>float</code>, defaults to 1.007) &#x2014;
The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000,
1.011].`,name:"s_noise"},{anchor:"diffusers.KarrasVeScheduler.s_churn",description:`<strong>s_churn</strong> (<code>float</code>, defaults to 80) &#x2014;
The parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100].`,name:"s_churn"},{anchor:"diffusers.KarrasVeScheduler.s_min",description:`<strong>s_min</strong> (<code>float</code>, defaults to 0.05) &#x2014;
The start value of the sigma range to add noise (enable stochasticity). A reasonable range is [0, 10].`,name:"s_min"},{anchor:"diffusers.KarrasVeScheduler.s_max",description:`<strong>s_max</strong> (<code>float</code>, defaults to 50) &#x2014;
The end value of the sigma range to add noise. A reasonable range is [0.2, 80].`,name:"s_max"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/schedulers/scheduling_karras_ve.py#L48"}}),N=new et({props:{$$slots:{default:[rt]},$$scope:{ctx:Ve}}}),Q=new ne({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"}],parametersDescription:[{anchor:"diffusers.KarrasVeScheduler.add_noise_to_input.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
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`,returnType:`
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`}}),ee=new ne({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;
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`,returnType:`
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Computed sample (x_{t-1}) of previous timestep. <code>prev_sample</code> should be used as next model input in the
denoising loop.`,name:"prev_sample"},{anchor:"diffusers.schedulers.scheduling_karras_ve.KarrasVeOutput.derivative",description:`<strong>derivative</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code> for images) &#x2014;
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The predicted denoised sample (x_{0}) based on the model output from the current timestep.
<code>pred_original_sample</code> can be used to preview progress or for guidance.`,name:"pred_original_sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/schedulers/scheduling_karras_ve.py#L28"}}),{c(){h=s("meta"),C=l(),f=s("h1"),b=s("a"),w=s("span"),x(_.$$.fragment),U=l(),y=s("span"),D=n("KarrasVeScheduler"),v=l(),g=s("p"),I=s("code"),Ne=n("KarrasVeScheduler"),Le=n(" is a stochastic sampler tailored o variance-expanding (VE) models. It is based on the "),W=s("a"),qe=n("Elucidating the Design Space of Diffusion-Based Generative Models"),Ge=n(" and "),J=s("a"),ze=n("Score-based generative modeling through stochastic differential equations"),Be=n(" papers."),Te=l(),O=s("h2"),M=s("a"),ue=s("span"),x(R.$$.fragment),He=l(),he=s("span"),Ue=n("KarrasVeScheduler"),Ee=l(),c=s("div"),x(j.$$.fragment),We=l(),pe=s("p"),Je=n("A stochastic scheduler tailored to variance-expanding models."),Re=l(),k=s("p"),je=n("This model inherits from "),ie=s("a"),Qe=n("SchedulerMixin"),Xe=n(" and "),de=s("a"),Ye=n("ConfigMixin"),Ze=n(`. Check the superclass documentation for the generic
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