import{S as Ft,i as It,s as Ut,e as s,k as p,w as q,t as a,M as qt,c as o,d as r,m as u,a as n,x as V,h as d,b as i,G as e,g as _,y as H,L as Vt,q as B,o as G,B as J,v as Ht}from"../../../chunks/vendor-hf-doc-builder.js";import{D as we}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as lt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Bt(pt){let g,pe,b,w,Z,E,Se,ee,$e,ue,y,S,te,M,ke,re,Pe,fe,v,Ee,x,Me,xe,K,Ke,Ae,he,A,Te,T,Ce,me,D,$,se,C,Oe,oe,Ne,_e,c,O,Le,k,Fe,N,Ie,Ue,L,qe,Ve,ne,He,Be,l,R,Ge,Je,ie,Re,We,ae,je,ze,de,Qe,Xe,W,Ye,Ze,j,et,tt,z,rt,st,ot,Q,F,nt,P,I,it,ce,at,dt,X,U,ve;return E=new lt({}),M=new lt({}),C=new lt({}),O=new we({props:{name:"class diffusers.KDPM2DiscreteScheduler",anchor:"diffusers.KDPM2DiscreteScheduler",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.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.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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options to clip the variance used when adding noise to the denoised sample. Choose from <code>fixed_small</code>,
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prediction type of the scheduler function, one of <code>epsilon</code> (predicting the noise of the diffusion
process), <code>sample</code> (directly predicting the noisy sample<code>) or </code>v_prediction\` (see section 2.4
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sample (<code>torch.FloatTensor</code>): input sample timestep (<code>int</code>, optional): current timestep`,name:"current"}],source:"https://github.com/huggingface/diffusers/blob/v0.11.0/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L91",returnDescription:`
<p>scaled input sample</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),I=new we({props:{name:"set_timesteps",anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.set_timesteps.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>) &#x2014;
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the device to which the timesteps should be moved to. If <code>None</code>, the timesteps are not moved.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/v0.11.0/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L114"}}),U=new we({props:{name:"step",anchor:"diffusers.KDPM2DiscreteScheduler.step",parameters:[{name:"model_output",val:": typing.Union[torch.FloatTensor, numpy.ndarray]"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"},{name:"sample",val:": typing.Union[torch.FloatTensor, numpy.ndarray]"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.step.Predict",description:"<strong>Predict</strong> the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion &#x2014;",name:"Predict"},{anchor:"diffusers.KDPM2DiscreteScheduler.step.process",description:`<strong>process</strong> from the learned model outputs (most often the predicted noise). &#x2014;
model_output (<code>torch.FloatTensor</code> or <code>np.ndarray</code>): direct output from learned diffusion model. timestep
(<code>int</code>): current discrete timestep in the diffusion chain. sample (<code>torch.FloatTensor</code> or <code>np.ndarray</code>):
current instance of sample being created by diffusion process.
return_dict (<code>bool</code>): option for returning tuple rather than SchedulerOutput class`,name:"process"}],source:"https://github.com/huggingface/diffusers/blob/v0.11.0/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L195",returnDescription:`
<p><a
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returning a tuple, the first element is the sample tensor.</p>
`,returnType:`
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