import{s as Be,n as Ke,o as Re}from"../chunks/scheduler.53228c21.js";import{S as Ge,i as Ye,e as d,s as n,c as u,h as Je,a,d as s,b as r,f as _,g as p,j as b,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 Qe}from"../chunks/CopyLLMTxtMenu.999d3d86.js";import{D as C}from"../chunks/Docstring.46d0934e.js";import{H as Ne,E as Xe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d27ae4df.js";function Ze(Ee){let x,ee,X,te,q,se,L,ne,O,ke='The Heun scheduler (Algorithm 1) is 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. The scheduler is ported from the <a href="https://github.com/crowsonkb/k-diffusion" rel="nofollow">k-diffusion</a> library and created by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>.',re,P,oe,o,N,be,W,Ie="Scheduler with Heun steps for discrete beta schedules.",ve,j,Me=`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.`,xe,y,E,$e,B,Fe="Add noise to the original samples according to the noise schedule at the specified timesteps.",ye,D,k,De,K,Ve="Find the index of a given timestep in the timestep schedule.",Te,T,I,Se,R,ze=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.`,He,S,M,we,G,Ue="Sets the begin index for the scheduler. This function should be run from pipeline before the inference.",Ce,H,F,qe,Y,Ae="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",Le,w,V,Oe,J,We=`Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).`,ie,z,de,$,U,Pe,Q,je="Base class for the output of a scheduler’s <code>step</code> function.",ae,A,ce,Z,le;return q=new Qe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),L=new Ne({props:{title:"HeunDiscreteScheduler",local:"heundiscretescheduler",headingTag:"h1"}}),P=new Ne({props:{title:"HeunDiscreteScheduler",local:"diffusers.HeunDiscreteScheduler",headingTag:"h2"}}),N=new C({props:{name:"class diffusers.HeunDiscreteScheduler",anchor:"diffusers.HeunDiscreteScheduler",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:": typing.Literal['linear', 'scaled_linear', 'squaredcos_cap_v2', 'exp'] = 'linear'"},{name:"trained_betas",val:": typing.Union[numpy.ndarray, typing.List[float], NoneType] = None"},{name:"prediction_type",val:": typing.Literal['epsilon', 'sample', 'v_prediction'] = 'epsilon'"},{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:"clip_sample",val:": typing.Optional[bool] = False"},{name:"clip_sample_range",val:": float = 1.0"},{name:"timestep_spacing",val:": typing.Literal['linspace', 'leading', 'trailing'] = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.beta_start",description:`<strong>beta_start</strong> (<code>float</code>, defaults to 0.0001) &#x2014;
The starting <code>beta</code> value of inference.`,name:"beta_start"},{anchor:"diffusers.HeunDiscreteScheduler.beta_end",description:`<strong>beta_end</strong> (<code>float</code>, defaults to 0.02) &#x2014;
The final <code>beta</code> value.`,name:"beta_end"},{anchor:"diffusers.HeunDiscreteScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>&quot;linear&quot;</code>, <code>&quot;scaled_linear&quot;</code>, <code>&quot;squaredcos_cap_v2&quot;</code>, or <code>&quot;exp&quot;</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>, <code>scaled_linear</code>, <code>squaredcos_cap_v2</code>, or <code>exp</code>.`,name:"beta_schedule"},{anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.prediction_type",description:`<strong>prediction_type</strong> (<code>&quot;epsilon&quot;</code>, <code>&quot;sample&quot;</code>, or <code>&quot;v_prediction&quot;</code>, defaults to <code>&quot;epsilon&quot;</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.HeunDiscreteScheduler.clip_sample",description:`<strong>clip_sample</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Clip the predicted sample for numerical stability.`,name:"clip_sample"},{anchor:"diffusers.HeunDiscreteScheduler.clip_sample_range",description:`<strong>clip_sample_range</strong> (<code>float</code>, defaults to 1.0) &#x2014;
The maximum magnitude for sample clipping. Valid only when <code>clip_sample=True</code>.`,name:"clip_sample_range"},{anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.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.HeunDiscreteScheduler.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.HeunDiscreteScheduler.timestep_spacing",description:`<strong>timestep_spacing</strong> (<code>&quot;linspace&quot;</code>, <code>&quot;leading&quot;</code>, or <code>&quot;trailing&quot;</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.HeunDiscreteScheduler.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_heun_discrete.py#L96"}}),E=new C({props:{name:"add_noise",anchor:"diffusers.HeunDiscreteScheduler.add_noise",parameters:[{name:"original_samples",val:": Tensor"},{name:"noise",val:": Tensor"},{name:"timesteps",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.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.HeunDiscreteScheduler.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_heun_discrete.py#L655",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>
`}}),k=new C({props:{name:"index_for_timestep",anchor:"diffusers.HeunDiscreteScheduler.index_for_timestep",parameters:[{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"schedule_timesteps",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.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_heun_discrete.py#L191",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>
`}}),I=new C({props:{name:"scale_model_input",anchor:"diffusers.HeunDiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.HeunDiscreteScheduler.scale_model_input.timestep",description:`<strong>timestep</strong> (<code>float</code> or <code>torch.Tensor</code>) &#x2014;
The current timestep in the diffusion chain.`,name:"timestep"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_heun_discrete.py#L254",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>
`}}),M=new C({props:{name:"set_begin_index",anchor:"diffusers.HeunDiscreteScheduler.set_begin_index",parameters:[{name:"begin_index",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.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_heun_discrete.py#L244"}}),F=new C({props:{name:"set_timesteps",anchor:"diffusers.HeunDiscreteScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": typing.Optional[int] = None"},{name:"device",val:": typing.Union[str, torch.device] = None"},{name:"num_train_timesteps",val:": typing.Optional[int] = None"},{name:"timesteps",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"diffusers.HeunDiscreteScheduler.set_timesteps.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of diffusion steps used when generating samples with a pre-trained model.`,name:"num_inference_steps"},{anchor:"diffusers.HeunDiscreteScheduler.set_timesteps.device",description:`<strong>device</strong> (<code>str</code>, <code>torch.device</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The device to which the timesteps should be moved to. If <code>None</code>, the timesteps are not moved.`,name:"device"},{anchor:"diffusers.HeunDiscreteScheduler.set_timesteps.num_train_timesteps",description:`<strong>num_train_timesteps</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of diffusion steps used when training the model. If <code>None</code>, the default
<code>num_train_timesteps</code> attribute is used.`,name:"num_train_timesteps"},{anchor:"diffusers.HeunDiscreteScheduler.set_timesteps.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Custom timesteps used to support arbitrary spacing between timesteps. If <code>None</code>, timesteps will be
generated based on the <code>timestep_spacing</code> attribute. If <code>timesteps</code> is passed, <code>num_inference_steps</code>
must be <code>None</code>, and <code>timestep_spacing</code> attribute will be ignored.`,name:"timesteps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_heun_discrete.py#L280"}}),V=new C({props:{name:"step",anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.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.HeunDiscreteScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.HeunDiscreteScheduler.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.HeunDiscreteScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a <code>HeunDiscreteSchedulerOutput</code> or
tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_heun_discrete.py#L551",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>, <code>HeunDiscreteSchedulerOutput</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>HeunDiscreteSchedulerOutput</code> or <code>tuple</code></p>
`}}),z=new Ne({props:{title:"SchedulerOutput",local:"diffusers.schedulers.scheduling_utils.SchedulerOutput",headingTag:"h2"}}),U=new C({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 Xe({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/schedulers/heun.md"}}),{c(){x=d("meta"),ee=n(),X=d("p"),te=n(),u(q.$$.fragment),se=n(),u(L.$$.fragment),ne=n(),O=d("p"),O.innerHTML=ke,re=n(),u(P.$$.fragment),oe=n(),o=d("div"),u(N.$$.fragment),be=n(),W=d("p"),W.textContent=Ie,ve=n(),j=d("p"),j.innerHTML=Me,xe=n(),y=d("div"),u(E.$$.fragment),$e=n(),B=d("p"),B.textContent=Fe,ye=n(),D=d("div"),u(k.$$.fragment),De=n(),K=d("p"),K.textContent=Ve,Te=n(),T=d("div"),u(I.$$.fragment),Se=n(),R=d("p"),R.textContent=ze,He=n(),S=d("div"),u(M.$$.fragment),we=n(),G=d("p"),G.textContent=Ue,Ce=n(),H=d("div"),u(F.$$.fragment),qe=n(),Y=d("p"),Y.textContent=Ae,Le=n(),w=d("div"),u(V.$$.fragment),Oe=n(),J=d("p"),J.textContent=We,ie=n(),u(z.$$.fragment),de=n(),$=d("div"),u(U.$$.fragment),Pe=n(),Q=d("p"),Q.innerHTML=je,ae=n(),u(A.$$.fragment),ce=n(),Z=d("p"),this.h()},l(e){const 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