import{s as je,n as Ye,o as Je}from"../chunks/scheduler.53228c21.js";import{S as Qe,i as Xe,e as a,s as n,c as p,h as Ze,a as d,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 A}from"../chunks/Docstring.49101787.js";import{H as Oe,E as tt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.4d82655d.js";function st(Ie){let D,te,Z,se,K,ne,w,re,C,He='The <code>KDPM2DiscreteScheduler</code> with ancestral sampling 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>.',oe,k,Ne='The original codebase can be found at <a href="https://github.com/crowsonkb/k-diffusion" rel="nofollow">crowsonkb/k-diffusion</a>.',ie,L,ae,i,E,De,G,qe=`KDPM2DiscreteScheduler with ancestral sampling 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,B,ze=`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.`,$e,$,O,ye,W,Fe="Add noise to the original samples according to the noise schedule at the specified timesteps.",Te,y,I,Se,R,Ve="Find the index of a given timestep in the timestep schedule.",Me,T,H,Pe,j,Ue=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.`,Ae,S,N,Ke,Y,Ge="Sets the begin index for the scheduler. This function should be run from pipeline before the inference.",we,M,q,Ce,J,Be="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",ke,P,z,Le,Q,We=`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).`,de,F,ce,x,V,Ee,X,Re="Base class for the output of a scheduler’s <code>step</code> function.",le,U,pe,ee,ue;return K=new et({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new Oe({props:{title:"KDPM2AncestralDiscreteScheduler",local:"kdpm2ancestraldiscretescheduler",headingTag:"h1"}}),L=new Oe({props:{title:"KDPM2AncestralDiscreteScheduler",local:"diffusers.KDPM2AncestralDiscreteScheduler",headingTag:"h2"}}),E=new A({props:{name:"class diffusers.KDPM2AncestralDiscreteScheduler",anchor:"diffusers.KDPM2AncestralDiscreteScheduler",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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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_ancestral_discrete.py#L97"}}),O=new A({props:{name:"add_noise",anchor:"diffusers.KDPM2AncestralDiscreteScheduler.add_noise",parameters:[{name:"original_samples",val:": Tensor"},{name:"noise",val:": Tensor"},{name:"timesteps",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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_ancestral_discrete.py#L662",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>
`}}),I=new A({props:{name:"index_for_timestep",anchor:"diffusers.KDPM2AncestralDiscreteScheduler.index_for_timestep",parameters:[{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"schedule_timesteps",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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_ancestral_discrete.py#L508",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>
`}}),H=new A({props:{name:"scale_model_input",anchor:"diffusers.KDPM2AncestralDiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"}],parametersDescription:[{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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_ancestral_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>
`}}),N=new A({props:{name:"set_begin_index",anchor:"diffusers.KDPM2AncestralDiscreteScheduler.set_begin_index",parameters:[{name:"begin_index",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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_ancestral_discrete.py#L205"}}),q=new A({props:{name:"set_timesteps",anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.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_ancestral_discrete.py#L245"}}),z=new A({props:{name:"step",anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.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.KDPM2AncestralDiscreteScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.KDPM2AncestralDiscreteScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a
<code>KDPM2AncestralDiscreteSchedulerOutput</code> or tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py#L554",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>,
<code>KDPM2AncestralDiscreteSchedulerOutput</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>KDPM2AncestralDiscreteSchedulerOutput</code> or <code>tuple</code></p>
`}}),F=new Oe({props:{title:"SchedulerOutput",local:"diffusers.schedulers.scheduling_utils.SchedulerOutput",headingTag:"h2"}}),V=new A({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"}}),U=new tt({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/schedulers/dpm_discrete_ancestral.md"}}),{c(){D=a("meta"),te=n(),Z=a("p"),se=n(),p(K.$$.fragment),ne=n(),p(w.$$.fragment),re=n(),C=a("p"),C.innerHTML=He,oe=n(),k=a("p"),k.innerHTML=Ne,ie=n(),p(L.$$.fragment),ae=n(),i=a("div"),p(E.$$.fragment),De=n(),G=a("p"),G.innerHTML=qe,xe=n(),B=a("p"),B.innerHTML=ze,$e=n(),$=a("div"),p(O.$$.fragment),ye=n(),W=a("p"),W.textContent=Fe,Te=n(),y=a("div"),p(I.$$.fragment),Se=n(),R=a("p"),R.textContent=Ve,Me=n(),T=a("div"),p(H.$$.fragment),Pe=n(),j=a("p"),j.textContent=Ue,Ae=n(),S=a("div"),p(N.$$.fragment),Ke=n(),Y=a("p"),Y.textContent=Ge,we=n(),M=a("div"),p(q.$$.fragment),Ce=n(),J=a("p"),J.textContent=Be,ke=n(),P=a("div"),p(z.$$.fragment),Le=n(),Q=a("p"),Q.textContent=We,de=n(),p(F.$$.fragment),ce=n(),x=a("div"),p(V.$$.fragment),Ee=n(),X=a("p"),X.innerHTML=Re,le=n(),p(U.$$.fragment),pe=n(),ee=a("p"),this.h()},l(e){const o=Ze("svelte-u9bgzb",document.head);D=d(o,"META",{name:!0,content:!0}),o.forEach(s),te=r(e),Z=d(e,"P",{}),b(Z).forEach(s),se=r(e),u(K.$$.fragment,e),ne=r(e),u(w.$$.fragment,e),re=r(e),C=d(e,"P",{"data-svelte-h":!0}),_(C)!=="svelte-1xsfzct"&&(C.innerHTML=He),oe=r(e),k=d(e,"P",{"data-svelte-h":!0}),_(k)!=="svelte-loovl7"&&(k.innerHTML=Ne),ie=r(e),u(L.$$.fragment,e),ae=r(e),i=d(e,"DIV",{class:!0});var c=b(i);u(E.$$.fragment,c),De=r(c),G=d(c,"P",{"data-svelte-h":!0}),_(G)!=="svelte-lafslh"&&(G.innerHTML=qe),xe=r(c),B=d(c,"P",{"data-svelte-h":!0}),_(B)!=="svelte-linuuh"&&(B.innerHTML=ze),$e=r(c),$=d(c,"DIV",{class:!0});var me=b($);u(O.$$.fragment,me),ye=r(me),W=d(me,"P",{"data-svelte-h":!0}),_(W)!=="svelte-rz9b5o"&&(W.textContent=Fe),me.forEach(s),Te=r(c),y=d(c,"DIV",{class:!0});var fe=b(y);u(I.$$.fragment,fe),Se=r(fe),R=d(fe,"P",{"data-svelte-h":!0}),_(R)!=="svelte-1xn85hs"&&(R.textContent=Ve),fe.forEach(s),Me=r(c),T=d(c,"DIV",{class:!0});var he=b(T);u(H.$$.fragment,he),Pe=r(he),j=d(he,"P",{"data-svelte-h":!0}),_(j)!=="svelte-1rkfgpx"&&(j.textContent=Ue),he.forEach(s),Ae=r(c),S=d(c,"DIV",{class:!0});var ge=b(S);u(N.$$.fragment,ge),Ke=r(ge),Y=d(ge,"P",{"data-svelte-h":!0}),_(Y)!=="svelte-1k141rk"&&(Y.textContent=Ge),ge.forEach(s),we=r(c),M=d(c,"DIV",{class:!0});var _e=b(M);u(q.$$.fragment,_e),Ce=r(_e),J=d(_e,"P",{"data-svelte-h":!0}),_(J)!=="svelte-1vzm9q"&&(J.textContent=Be),_e.forEach(s),ke=r(c),P=d(c,"DIV",{class:!0});var be=b(P);u(z.$$.fragment,be),Le=r(be),Q=d(be,"P",{"data-svelte-h":!0}),_(Q)!=="svelte-hi84tp"&&(Q.textContent=We),be.forEach(s),c.forEach(s),de=r(e),u(F.$$.fragment,e),ce=r(e),x=d(e,"DIV",{class:!0});var ve=b(x);u(V.$$.fragment,ve),Ee=r(ve),X=d(ve,"P",{"data-svelte-h":!0}),_(X)!=="svelte-6ojmkw"&&(X.innerHTML=Re),ve.forEach(s),le=r(e),u(U.$$.fragment,e),pe=r(e),ee=d(e,"P",{}),b(ee).forEach(s),this.h()},h(){v(D,"name","hf:doc:metadata"),v(D,"content",nt),v($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(i,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,o){t(document.head,D),l(e,te,o),l(e,Z,o),l(e,se,o),m(K,e,o),l(e,ne,o),m(w,e,o),l(e,re,o),l(e,C,o),l(e,oe,o),l(e,k,o),l(e,ie,o),m(L,e,o),l(e,ae,o),l(e,i,o),m(E,i,null),t(i,De),t(i,G),t(i,xe),t(i,B),t(i,$e),t(i,$),m(O,$,null),t($,ye),t($,W),t(i,Te),t(i,y),m(I,y,null),t(y,Se),t(y,R),t(i,Me),t(i,T),m(H,T,null),t(T,Pe),t(T,j),t(i,Ae),t(i,S),m(N,S,null),t(S,Ke),t(S,Y),t(i,we),t(i,M),m(q,M,null),t(M,Ce),t(M,J),t(i,ke),t(i,P),m(z,P,null),t(P,Le),t(P,Q),l(e,de,o),m(F,e,o),l(e,ce,o),l(e,x,o),m(V,x,null),t(x,Ee),t(x,X),l(e,le,o),m(U,e,o),l(e,pe,o),l(e,ee,o),ue=!0},p:Ye,i(e){ue||(f(K.$$.fragment,e),f(w.$$.fragment,e),f(L.$$.fragment,e),f(E.$$.fragment,e),f(O.$$.fragment,e),f(I.$$.fragment,e),f(H.$$.fragment,e),f(N.$$.fragment,e),f(q.$$.fragment,e),f(z.$$.fragment,e),f(F.$$.fragment,e),f(V.$$.fragment,e),f(U.$$.fragment,e),ue=!0)},o(e){h(K.$$.fragment,e),h(w.$$.fragment,e),h(L.$$.fragment,e),h(E.$$.fragment,e),h(O.$$.fragment,e),h(I.$$.fragment,e),h(H.$$.fragment,e),h(N.$$.fragment,e),h(q.$$.fragment,e),h(z.$$.fragment,e),h(F.$$.fragment,e),h(V.$$.fragment,e),h(U.$$.fragment,e),ue=!1},d(e){e&&(s(te),s(Z),s(se),s(ne),s(re),s(C),s(oe),s(k),s(ie),s(ae),s(i),s(de),s(ce),s(x),s(le),s(pe),s(ee)),s(D),g(K,e),g(w,e),g(L,e),g(E),g(O),g(I),g(H),g(N),g(q),g(z),g(F,e),g(V),g(U,e)}}}const nt='{"title":"KDPM2AncestralDiscreteScheduler","local":"kdpm2ancestraldiscretescheduler","sections":[{"title":"KDPM2AncestralDiscreteScheduler","local":"diffusers.KDPM2AncestralDiscreteScheduler","sections":[],"depth":2},{"title":"SchedulerOutput","local":"diffusers.schedulers.scheduling_utils.SchedulerOutput","sections":[],"depth":2}],"depth":1}';function rt(Ie){return Je(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class lt extends Qe{constructor(D){super(),Xe(this,D,rt,st,je,{})}}export{lt as component};
