import{S as kt,i as Tt,s as At,e as s,k as u,w as L,t as i,M as Ct,c as o,d as r,m as h,a as n,x as U,h as l,b as a,G as e,g as v,y as q,L as Nt,q as V,o as B,B as H,v as Ot}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Se}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as ot}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Ft(nt){let _,le,S,y,X,P,be,Z,De,de,b,E,ee,x,ye,te,Ee,ce,g,we,M,$e,Pe,k,xe,Me,pe,T,ke,A,Te,fe,D,w,re,C,Ae,se,Ce,ue,c,N,Ne,R,Oe,O,Fe,Ie,d,z,Ke,Le,oe,Ue,qe,ne,Ve,Be,ae,He,Re,G,ze,Ge,J,Je,We,W,Ye,je,Qe,Y,F,Xe,$,I,Ze,ie,et,tt,j,K,he;return P=new ot({}),x=new ot({}),C=new ot({}),N=new Se({props:{name:"class diffusers.DPMSolverSDEScheduler",anchor:"diffusers.DPMSolverSDEScheduler",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"},{name:"noise_sampler_seed",val:": typing.Optional[int] = None"},{name:"timestep_spacing",val:": str = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.DPMSolverSDEScheduler.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.DPMSolverSDEScheduler.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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process), <code>sample</code> (directly predicting the noisy sample<code>) or </code>v_prediction\` (see section 2.4
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noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence
of noise levels {&#x3C3;i} as defined in Equation (5) of the paper <a href="https://arxiv.org/pdf/2206.00364.pdf" rel="nofollow">https://arxiv.org/pdf/2206.00364.pdf</a>.`,name:"use_karras_sigmas"},{anchor:"diffusers.DPMSolverSDEScheduler.noise_sampler_seed",description:`<strong>noise_sampler_seed</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
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The way the timesteps should be scaled. Refer to Table 2. of <a href="https://arxiv.org/abs/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and Sample
Steps are Flawed</a> for more information.`,name:"timestep_spacing"},{anchor:"diffusers.DPMSolverSDEScheduler.steps_offset",description:`<strong>steps_offset</strong> (<code>int</code>, default <code>0</code>) &#x2014;
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`}}),I=new Se({props:{name:"set_timesteps",anchor:"diffusers.DPMSolverSDEScheduler.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.DPMSolverSDEScheduler.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"},{anchor:"diffusers.DPMSolverSDEScheduler.set_timesteps.device",description:`<strong>device</strong> (<code>str</code> or <code>torch.device</code>, optional) &#x2014;
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/v0.19.2/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L249"}}),K=new Se({props:{name:"step",anchor:"diffusers.DPMSolverSDEScheduler.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"},{name:"s_noise",val:": float = 1.0"}],parametersDescription:[{anchor:"diffusers.DPMSolverSDEScheduler.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.DPMSolverSDEScheduler.step.process",description:"<strong>process</strong> from the learned model outputs (most often the predicted noise). &#x2014;",name:"process"},{anchor:"diffusers.DPMSolverSDEScheduler.step.model_output",description:"<strong>model_output</strong> (Union[torch.FloatTensor, np.ndarray]) &#x2014; Direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.DPMSolverSDEScheduler.step.timestep",description:"<strong>timestep</strong> (Union[float, torch.FloatTensor]) &#x2014; Current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DPMSolverSDEScheduler.step.sample",description:"<strong>sample</strong> (Union[torch.FloatTensor, np.ndarray]) &#x2014; Current instance of sample being created by diffusion process.",name:"sample"},{anchor:"diffusers.DPMSolverSDEScheduler.step.return_dict",description:"<strong>return_dict</strong> (bool, optional) &#x2014; Option for returning tuple rather than SchedulerOutput class. Defaults to True.",name:"return_dict"},{anchor:"diffusers.DPMSolverSDEScheduler.step.s_noise",description:"<strong>s_noise</strong> (float, optional) &#x2014; Scaling factor for the noise added to the sample. Defaults to 1.0.",name:"s_noise"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_dpmsolver_sde.py#L378",returnDescription:`
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