import{S as Kt,i as It,s as Bt,e as s,k as u,w as G,t as a,M as Gt,c as o,d as r,m as f,a as n,x as U,h as i,b as c,G as e,g as _,y as V,L as Ut,q as H,o as R,B as z,v as Vt}from"../../../chunks/vendor-hf-doc-builder.js";import{D as De}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as pt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Ht(ht){let v,ue,b,S,X,T,xe,Z,$e,fe,E,w,ee,C,ke,te,Ae,pe,g,Te,P,Ce,Pe,q,qe,Fe,he,y,D,re,F,Me,se,Ne,me,d,M,Oe,x,Le,N,Ke,Ie,O,Be,Ge,l,J,Ue,Ve,oe,He,Re,ne,ze,Je,ae,We,Ye,W,je,Qe,Y,Xe,Ze,j,et,tt,rt,$,L,st,K,ot,ie,nt,at,it,k,I,ct,ce,lt,dt,A,B,ut,le,ft,ge;return T=new pt({}),C=new pt({}),F=new pt({}),M=new De({props:{name:"class diffusers.EulerDiscreteScheduler",anchor:"diffusers.EulerDiscreteScheduler",parameters:[{name:"num_train_timesteps",val:": int = 1000"},{name:"beta_start",val:": float = 0.0001"},{name:"beta_end",val:": float = 0.02"},{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:"interpolation_type",val:": str = 'linear'"},{name:"use_karras_sigmas",val:": typing.Optional[bool] = False"},{name:"timestep_spacing",val:": str = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.num_train_timesteps",description:"<strong>num_train_timesteps</strong> (<code>int</code>) &#x2014; number of diffusion steps used to train the model.",name:"num_train_timesteps"},{anchor:"diffusers.EulerDiscreteScheduler.beta_start",description:"<strong>beta_start</strong> (<code>float</code>) &#x2014; the starting <code>beta</code> value of inference.",name:"beta_start"},{anchor:"diffusers.EulerDiscreteScheduler.beta_end",description:"<strong>beta_end</strong> (<code>float</code>) &#x2014; the final <code>beta</code> value.",name:"beta_end"},{anchor:"diffusers.EulerDiscreteScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</code>) &#x2014;
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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process), <code>sample</code> (directly predicting the noisy sample<code>) or </code>v_prediction\` (see section 2.4
<a href="https://imagen.research.google/video/paper.pdf" rel="nofollow">https://imagen.research.google/video/paper.pdf</a>)`,name:"prediction_type"},{anchor:"diffusers.EulerDiscreteScheduler.interpolation_type",description:`<strong>interpolation_type</strong> (<code>str</code>, default <code>&quot;linear&quot;</code>, optional) &#x2014;
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This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the
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.EulerDiscreteScheduler.timestep_spacing",description:`<strong>timestep_spacing</strong> (<code>str</code>, default <code>&quot;linspace&quot;</code>) &#x2014;
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Steps are Flawed</a> for more information.`,name:"timestep_spacing"},{anchor:"diffusers.EulerDiscreteScheduler.steps_offset",description:`<strong>steps_offset</strong> (<code>int</code>, default <code>0</code>) &#x2014;
an offset added to the inference steps. You can use a combination of <code>offset=1</code> and
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stable diffusion.`,name:"steps_offset"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L94"}}),L=new De({props:{name:"scale_model_input",anchor:"diffusers.EulerDiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.scale_model_input.sample",description:"<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014; input sample",name:"sample"},{anchor:"diffusers.EulerDiscreteScheduler.scale_model_input.timestep",description:"<strong>timestep</strong> (<code>float</code> or <code>torch.FloatTensor</code>) &#x2014; the current timestep in the diffusion chain",name:"timestep"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L188",returnDescription:`
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<p><code>torch.FloatTensor</code></p>
`}}),I=new De({props:{name:"set_timesteps",anchor:"diffusers.EulerDiscreteScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.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/v0.19.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L211"}}),B=new De({props:{name:"step",anchor:"diffusers.EulerDiscreteScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"},{name:"sample",val:": FloatTensor"},{name:"s_churn",val:": float = 0.0"},{name:"s_tmin",val:": float = 0.0"},{name:"s_tmax",val:": float = inf"},{name:"s_noise",val:": float = 1.0"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.step.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.EulerDiscreteScheduler.step.timestep",description:"<strong>timestep</strong> (<code>float</code>) &#x2014; current timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.EulerDiscreteScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_churn",description:"<strong>s_churn</strong> (<code>float</code>) &#x2014;",name:"s_churn"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_tmin",description:"<strong>s_tmin</strong>  (<code>float</code>) &#x2014;",name:"s_tmin"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_tmax",description:"<strong>s_tmax</strong>  (<code>float</code>) &#x2014;",name:"s_tmax"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_noise",description:"<strong>s_noise</strong> (<code>float</code>) &#x2014;",name:"s_noise"},{anchor:"diffusers.EulerDiscreteScheduler.step.generator",description:"<strong>generator</strong> (<code>torch.Generator</code>, optional) &#x2014; Random number generator.",name:"generator"},{anchor:"diffusers.EulerDiscreteScheduler.step.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>) &#x2014; option for returning tuple rather than EulerDiscreteSchedulerOutput class",name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L307",returnDescription:`
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