import{s as je,n as Ye,o as Je}from"../chunks/scheduler.53228c21.js";import{S as Qe,i as Xe,e as d,s as n,c as p,h as Ze,a,d as s,b as r,f as b,g as u,j as _,k as v,l as t,m as l,n as m,t as f,o as h,p as g}from"../chunks/index.100fac89.js";import{C as et}from"../chunks/CopyLLMTxtMenu.20a72e0e.js";import{D as w}from"../chunks/Docstring.49101787.js";import{H as Ie,E as tt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.4d82655d.js";function st(He){let D,te,Z,se,K,ne,C,re,k,qe='The <code>KDPM2DiscreteScheduler</code> is inspired by the <a href="https://huggingface.co/papers/2206.00364" rel="nofollow">Elucidating the Design Space of Diffusion-Based Generative Models</a> paper, and the scheduler is ported from and created by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>.',ie,L,Fe='The original codebase can be found at <a href="https://github.com/crowsonkb/k-diffusion" rel="nofollow">crowsonkb/k-diffusion</a>.',oe,E,de,o,O,De,B,Ne=`KDPM2DiscreteScheduler is inspired by the DPMSolver2 and 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.`,xe,W,Ve=`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.`,$e,$,I,ye,G,ze="Add noise to the original samples according to the noise schedule at the specified timesteps.",Te,y,H,Se,R,Ue="Find the index of a given timestep in the timestep schedule.",Me,T,q,Pe,j,Ae=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.`,we,S,F,Ke,Y,Be="Sets the begin index for the scheduler. This function should be run from pipeline before the inference.",Ce,M,N,ke,J,We="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",Le,P,V,Ee,Q,Ge=`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,z,ce,x,U,Oe,X,Re="Base class for the output of a scheduler’s <code>step</code> function.",le,A,pe,ee,ue;return K=new et({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),C=new Ie({props:{title:"KDPM2DiscreteScheduler",local:"kdpm2discretescheduler",headingTag:"h1"}}),E=new Ie({props:{title:"KDPM2DiscreteScheduler",local:"diffusers.KDPM2DiscreteScheduler",headingTag:"h2"}}),O=new w({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:"use_karras_sigmas",val:": typing.Optional[bool] = False"},{name:"use_exponential_sigmas",val:": typing.Optional[bool] = False"},{name:"use_beta_sigmas",val:": typing.Optional[bool] = False"},{name:"prediction_type",val:": str = 'epsilon'"},{name:"timestep_spacing",val:": str = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.beta_start",description:`<strong>beta_start</strong> (<code>float</code>, defaults to 0.00085) &#x2014;
The starting <code>beta</code> value of inference.`,name:"beta_start"},{anchor:"diffusers.KDPM2DiscreteScheduler.beta_end",description:`<strong>beta_end</strong> (<code>float</code>, defaults to 0.012) &#x2014;
The final <code>beta</code> value.`,name:"beta_end"},{anchor:"diffusers.KDPM2DiscreteScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</code>, defaults to <code>&quot;linear&quot;</code>) &#x2014;
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
<code>linear</code> or <code>scaled_linear</code>.`,name:"beta_schedule"},{anchor:"diffusers.KDPM2DiscreteScheduler.trained_betas",description:`<strong>trained_betas</strong> (<code>np.ndarray</code>, <em>optional</em>) &#x2014;
Pass an array of betas directly to the constructor to bypass <code>beta_start</code> and <code>beta_end</code>.`,name:"trained_betas"},{anchor:"diffusers.KDPM2DiscreteScheduler.use_karras_sigmas",description:`<strong>use_karras_sigmas</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If <code>True</code>,
the sigmas are determined according to a sequence of noise levels {&#x3C3;i}.`,name:"use_karras_sigmas"},{anchor:"diffusers.KDPM2DiscreteScheduler.use_exponential_sigmas",description:`<strong>use_exponential_sigmas</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.`,name:"use_exponential_sigmas"},{anchor:"diffusers.KDPM2DiscreteScheduler.use_beta_sigmas",description:`<strong>use_beta_sigmas</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to <a href="https://huggingface.co/papers/2407.12173" rel="nofollow">Beta
Sampling is All You Need</a> for more information.`,name:"use_beta_sigmas"},{anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.timestep_spacing",description:`<strong>timestep_spacing</strong> (<code>str</code>, defaults to <code>&quot;linspace&quot;</code>) &#x2014;
The way the timesteps should be scaled. Refer to Table 2 of the <a href="https://huggingface.co/papers/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and
Sample Steps are Flawed</a> for more information.`,name:"timestep_spacing"},{anchor:"diffusers.KDPM2DiscreteScheduler.steps_offset",description:`<strong>steps_offset</strong> (<code>int</code>, defaults to 0) &#x2014;
An offset added to the inference steps, as required by some model families.`,name:"steps_offset"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L96"}}),I=new w({props:{name:"add_noise",anchor:"diffusers.KDPM2DiscreteScheduler.add_noise",parameters:[{name:"original_samples",val:": Tensor"},{name:"noise",val:": Tensor"},{name:"timesteps",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.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_k_dpm_2_discrete.py#L634",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.KDPM2DiscreteScheduler.index_for_timestep",parameters:[{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"schedule_timesteps",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.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_k_dpm_2_discrete.py#L334",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>
`}}),q=new w({props:{name:"scale_model_input",anchor:"diffusers.KDPM2DiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.KDPM2DiscreteScheduler.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_k_dpm_2_discrete.py#L215",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>
`}}),F=new w({props:{name:"set_begin_index",anchor:"diffusers.KDPM2DiscreteScheduler.set_begin_index",parameters:[{name:"begin_index",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.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_k_dpm_2_discrete.py#L205"}}),N=new w({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;
The number of diffusion steps used when generating samples with a pre-trained model.`,name:"num_inference_steps"},{anchor:"diffusers.KDPM2DiscreteScheduler.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L245"}}),V=new w({props:{name:"step",anchor:"diffusers.KDPM2DiscreteScheduler.step",parameters:[{name:"model_output",val:": typing.Union[torch.Tensor, numpy.ndarray]"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"sample",val:": typing.Union[torch.Tensor, numpy.ndarray]"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.KDPM2DiscreteScheduler.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.KDPM2DiscreteScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a <code>KDPM2DiscreteSchedulerOutput</code> or
tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L539",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>, <code>KDPM2DiscreteSchedulerOutput</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>KDPM2DiscreteSchedulerOutput</code> or <code>tuple</code></p>
`}}),z=new Ie({props:{title:"SchedulerOutput",local:"diffusers.schedulers.scheduling_utils.SchedulerOutput",headingTag:"h2"}}),U=new w({props:{name:"class diffusers.schedulers.scheduling_utils.SchedulerOutput",anchor:"diffusers.schedulers.scheduling_utils.SchedulerOutput",parameters:[{name:"prev_sample",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.schedulers.scheduling_utils.SchedulerOutput.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_utils.py#L62"}}),A=new 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