import{s as We,n as Je,o as Qe}from"../chunks/scheduler.53228c21.js";import{S as Xe,i as Ye,e as a,s as n,c as u,h as Ze,a as d,d as s,b as r,f as E,g as p,j as _,k as x,l as t,m as c,n as m,t as h,o as f,p as g}from"../chunks/index.100fac89.js";import{C as et}from"../chunks/CopyLLMTxtMenu.999d3d86.js";import{D as w}from"../chunks/Docstring.46d0934e.js";import{H as He,E as tt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d27ae4df.js";function st(Ne){let v,te,Z,se,C,ne,L,re,P,ze='The Karras formulation of the Euler scheduler (Algorithm 2) from the <a href="https://huggingface.co/papers/2206.00364" rel="nofollow">Elucidating the Design Space of Diffusion-Based Generative Models</a> paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original <a href="https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51" rel="nofollow">k-diffusion</a> implementation by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>.',oe,O,ie,o,I,xe,G,Ve="Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1].",ve,B,Ue=`[1] Karras, Tero, et al. “Elucidating the Design Space of Diffusion-Based Generative Models.”
<a href="https://huggingface.co/papers/2206.00364" rel="nofollow">https://huggingface.co/papers/2206.00364</a>`,be,j,qe=`This model inherits from <a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a> and <a href="/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin">ConfigMixin</a>. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.`,$e,$,k,De,F,Ae="Add noise to the original samples according to the noise schedule at the specified timesteps.",Me,D,H,Te,R,Ke="Find the index of a given timestep in the timestep schedule.",Se,M,N,ye,W,Ge=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by <code>(sigma**2 + 1) ** 0.5</code> to match the Euler algorithm.`,we,T,z,Ce,J,Be="Sets the begin index for the scheduler. This function should be run from pipeline before the inference.",Le,S,V,Pe,Q,je="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",Oe,y,U,Ie,X,Fe=`Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).`,ae,q,de,b,A,ke,Y,Re="Output class for the scheduler’s <code>step</code> function output.",le,K,ce,ee,ue;return C=new et({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),L=new He({props:{title:"EDMEulerScheduler",local:"edmeulerscheduler",headingTag:"h1"}}),O=new He({props:{title:"EDMEulerScheduler",local:"diffusers.EDMEulerScheduler",headingTag:"h2"}}),I=new w({props:{name:"class diffusers.EDMEulerScheduler",anchor:"diffusers.EDMEulerScheduler",parameters:[{name:"sigma_min",val:": float = 0.002"},{name:"sigma_max",val:": float = 80.0"},{name:"sigma_data",val:": float = 0.5"},{name:"sigma_schedule",val:": str = 'karras'"},{name:"num_train_timesteps",val:": int = 1000"},{name:"prediction_type",val:": str = 'epsilon'"},{name:"rho",val:": float = 7.0"},{name:"final_sigmas_type",val:": str = 'zero'"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.sigma_min",description:`<strong>sigma_min</strong> (<code>float</code>, <em>optional</em>, defaults to 0.002) &#x2014;
Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable
range is [0, 10].`,name:"sigma_min"},{anchor:"diffusers.EDMEulerScheduler.sigma_max",description:`<strong>sigma_max</strong> (<code>float</code>, <em>optional</em>, defaults to 80.0) &#x2014;
Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable
range is [0.2, 80.0].`,name:"sigma_max"},{anchor:"diffusers.EDMEulerScheduler.sigma_data",description:`<strong>sigma_data</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) &#x2014;
The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].`,name:"sigma_data"},{anchor:"diffusers.EDMEulerScheduler.sigma_schedule",description:`<strong>sigma_schedule</strong> (<code>str</code>, <em>optional</em>, defaults to <code>karras</code>) &#x2014;
Sigma schedule to compute the <code>sigmas</code>. By default, we the schedule introduced in the EDM paper
(<a href="https://huggingface.co/papers/2206.00364" rel="nofollow">https://huggingface.co/papers/2206.00364</a>). Other acceptable value is &#x201C;exponential&#x201D;. The exponential
schedule was incorporated in this model: <a href="https://huggingface.co/stabilityai/cosxl" rel="nofollow">https://huggingface.co/stabilityai/cosxl</a>.`,name:"sigma_schedule"},{anchor:"diffusers.EDMEulerScheduler.num_train_timesteps",description:`<strong>num_train_timesteps</strong> (<code>int</code>, defaults to 1000) &#x2014;
The number of diffusion steps to train the model.`,name:"num_train_timesteps"},{anchor:"diffusers.EDMEulerScheduler.prediction_type",description:`<strong>prediction_type</strong> (<code>str</code>, defaults to <code>epsilon</code>, <em>optional</em>) &#x2014;
Prediction type of the scheduler function; can be <code>epsilon</code> (predicts the noise of the diffusion process),
<code>sample</code> (directly predicts the noisy sample<code>) or </code>v_prediction\` (see section 2.4 of <a href="https://huggingface.co/papers/2210.02303" rel="nofollow">Imagen
Video</a> paper).`,name:"prediction_type"},{anchor:"diffusers.EDMEulerScheduler.rho",description:`<strong>rho</strong> (<code>float</code>, <em>optional</em>, defaults to 7.0) &#x2014;
The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1].`,name:"rho"},{anchor:"diffusers.EDMEulerScheduler.final_sigmas_type",description:`<strong>final_sigmas_type</strong> (<code>str</code>, defaults to <code>&quot;zero&quot;</code>) &#x2014;
The final <code>sigma</code> value for the noise schedule during the sampling process. If <code>&quot;sigma_min&quot;</code>, the final
sigma is the same as the last sigma in the training schedule. If <code>zero</code>, the final sigma is set to 0.`,name:"final_sigmas_type"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L49"}}),k=new w({props:{name:"add_noise",anchor:"diffusers.EDMEulerScheduler.add_noise",parameters:[{name:"original_samples",val:": Tensor"},{name:"noise",val:": Tensor"},{name:"timesteps",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.add_noise.original_samples",description:`<strong>original_samples</strong> (<code>torch.Tensor</code>) &#x2014;
The original samples to which noise will be added.`,name:"original_samples"},{anchor:"diffusers.EDMEulerScheduler.add_noise.noise",description:`<strong>noise</strong> (<code>torch.Tensor</code>) &#x2014;
The noise tensor to add to the original samples.`,name:"noise"},{anchor:"diffusers.EDMEulerScheduler.add_noise.timesteps",description:`<strong>timesteps</strong> (<code>torch.Tensor</code>) &#x2014;
The timesteps at which to add noise, determining the noise level from the schedule.`,name:"timesteps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L433",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The noisy samples with added noise scaled according to the timestep schedule.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),H=new w({props:{name:"index_for_timestep",anchor:"diffusers.EDMEulerScheduler.index_for_timestep",parameters:[{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"schedule_timesteps",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.index_for_timestep.timestep",description:`<strong>timestep</strong> (<code>float</code> or <code>torch.Tensor</code>) &#x2014;
The timestep value to find in the schedule.`,name:"timestep"},{anchor:"diffusers.EDMEulerScheduler.index_for_timestep.schedule_timesteps",description:`<strong>schedule_timesteps</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
The timestep schedule to search in. If <code>None</code>, uses <code>self.timesteps</code>.`,name:"schedule_timesteps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L287",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The index of the timestep in the schedule. For the very first step, returns the second index if
multiple matches exist to avoid skipping a sigma when starting mid-schedule (e.g., for image-to-image).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>int</code></p>
`}}),N=new w({props:{name:"scale_model_input",anchor:"diffusers.EDMEulerScheduler.scale_model_input",parameters:[{name:"sample",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.EDMEulerScheduler.scale_model_input.timestep",description:`<strong>timestep</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The current timestep in the diffusion chain.`,name:"timestep"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L191",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A scaled input sample.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),z=new w({props:{name:"set_begin_index",anchor:"diffusers.EDMEulerScheduler.set_begin_index",parameters:[{name:"begin_index",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.set_begin_index.begin_index",description:`<strong>begin_index</strong> (<code>int</code>, defaults to <code>0</code>) &#x2014;
The begin index for the scheduler.`,name:"begin_index"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L153"}}),V=new w({props:{name:"set_timesteps",anchor:"diffusers.EDMEulerScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int = None"},{name:"device",val:": typing.Union[str, torch.device] = None"},{name:"sigmas",val:": typing.Union[torch.Tensor, typing.List[float], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.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.EDMEulerScheduler.set_timesteps.device",description:`<strong>device</strong> (<code>str</code> or <code>torch.device</code>, <em>optional</em>) &#x2014;
The device to which the timesteps should be moved to. If <code>None</code>, the timesteps are not moved.`,name:"device"},{anchor:"diffusers.EDMEulerScheduler.set_timesteps.sigmas",description:`<strong>sigmas</strong> (<code>Union[torch.Tensor, List[float]]</code>, <em>optional</em>) &#x2014;
Custom sigmas to use for the denoising process. If not defined, the default behavior when
<code>num_inference_steps</code> is passed will be used.`,name:"sigmas"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L215"}}),U=new w({props:{name:"step",anchor:"diffusers.EDMEulerScheduler.step",parameters:[{name:"model_output",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"sample",val:": Tensor"},{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"},{name:"pred_original_sample",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.EDMEulerScheduler.step.model_output",description:`<strong>model_output</strong> (<code>torch.Tensor</code>) &#x2014;
The direct output from learned diffusion model.`,name:"model_output"},{anchor:"diffusers.EDMEulerScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.EDMEulerScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
A current instance of a sample created by the diffusion process.`,name:"sample"},{anchor:"diffusers.EDMEulerScheduler.step.s_churn",description:"<strong>s_churn</strong> (<code>float</code>) &#x2014;",name:"s_churn"},{anchor:"diffusers.EDMEulerScheduler.step.s_tmin",description:"<strong>s_tmin</strong>  (<code>float</code>) &#x2014;",name:"s_tmin"},{anchor:"diffusers.EDMEulerScheduler.step.s_tmax",description:"<strong>s_tmax</strong>  (<code>float</code>) &#x2014;",name:"s_tmax"},{anchor:"diffusers.EDMEulerScheduler.step.s_noise",description:`<strong>s_noise</strong> (<code>float</code>, defaults to 1.0) &#x2014;
Scaling factor for noise added to the sample.`,name:"s_noise"},{anchor:"diffusers.EDMEulerScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.EDMEulerScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a <code>~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput</code> or tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_edm_euler.py#L333",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>, <code>~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput</code> is
returned, otherwise a tuple is returned where the first element is the sample tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput</code> or <code>tuple</code></p>
`}}),q=new He({props:{title:"EDMEulerSchedulerOutput",local:"diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput",headingTag:"h2"}}),A=new w({props:{name:"class diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput",anchor:"diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput",parameters:[{name:"prev_sample",val:": Tensor"},{name:"pred_original_sample",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput.prev_sample",description:`<strong>prev_sample</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code> for images) &#x2014;
Computed sample <code>(x_{t-1})</code> 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_edm_euler.EDMEulerSchedulerOutput.pred_original_sample",description:`<strong>pred_original_sample</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code> for images) &#x2014;
The predicted denoised sample <code>(x_{0})</code> 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/main/src/diffusers/schedulers/scheduling_edm_euler.py#L32"}}),K=new 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