import{s as Xe,n as Ze,o as et}from"../chunks/scheduler.53228c21.js";import{S as tt,i as st,e as d,s as r,c as u,h as rt,a as c,d as s,b as n,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 nt}from"../chunks/CopyLLMTxtMenu.999d3d86.js";import{D as $}from"../chunks/Docstring.46d0934e.js";import{H as Fe,E as ot}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d27ae4df.js";function it(He){let x,re,te,ne,q,oe,O,ie,P,Ae='<code>LMSDiscreteScheduler</code> is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>, and the original implementation can be found at <a href="https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181" rel="nofollow">crowsonkb/k-diffusion</a>.',de,k,ce,o,E,$e,j,Ue="A linear multistep scheduler for discrete beta schedules.",De,K,We=`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.`,Te,D,I,Le,B,Re="Add noise to the original samples according to the noise schedule at the specified timesteps.",ye,T,N,Me,G,je="Compute the linear multistep coefficient.",we,L,V,Ce,Y,Ke="Find the index of a given timestep in the timestep schedule.",qe,y,z,Oe,J,Be=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.`,Pe,M,F,ke,Q,Ge="Sets the begin index for the scheduler. This function should be run from pipeline before the inference.",Ee,w,H,Ie,X,Ye="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",Ne,C,A,Ve,Z,Je=`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,U,le,S,W,ze,ee,Qe="Output class for the scheduler’s <code>step</code> function output.",ue,R,pe,se,me;return q=new nt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),O=new Fe({props:{title:"LMSDiscreteScheduler",local:"lmsdiscretescheduler",headingTag:"h1"}}),k=new Fe({props:{title:"LMSDiscreteScheduler",local:"diffusers.LMSDiscreteScheduler",headingTag:"h2"}}),E=new $({props:{name:"class diffusers.LMSDiscreteScheduler",anchor:"diffusers.LMSDiscreteScheduler",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:": typing.Literal['linear', 'scaled_linear', 'squaredcos_cap_v2'] = '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:": typing.Literal['epsilon', 'sample', 'v_prediction'] = 'epsilon'"},{name:"timestep_spacing",val:": typing.Literal['linspace', 'leading', 'trailing'] = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.num_train_timesteps",description:`<strong>num_train_timesteps</strong> (<code>int</code>, defaults to <code>1000</code>) &#x2014;
The number of diffusion steps to train the model.`,name:"num_train_timesteps"},{anchor:"diffusers.LMSDiscreteScheduler.beta_start",description:`<strong>beta_start</strong> (<code>float</code>, defaults to <code>0.0001</code>) &#x2014;
The starting <code>beta</code> value of inference.`,name:"beta_start"},{anchor:"diffusers.LMSDiscreteScheduler.beta_end",description:`<strong>beta_end</strong> (<code>float</code>, defaults to <code>0.02</code>) &#x2014;
The final <code>beta</code> value.`,name:"beta_end"},{anchor:"diffusers.LMSDiscreteScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>&quot;linear&quot;</code>, <code>&quot;scaled_linear&quot;</code>, or <code>&quot;squaredcos_cap_v2&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.`,name:"beta_schedule"},{anchor:"diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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>) &#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.LMSDiscreteScheduler.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.LMSDiscreteScheduler.steps_offset",description:`<strong>steps_offset</strong> (<code>int</code>, defaults to <code>0</code>) &#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_lms_discrete.py#L94"}}),I=new $({props:{name:"add_noise",anchor:"diffusers.LMSDiscreteScheduler.add_noise",parameters:[{name:"original_samples",val:": Tensor"},{name:"noise",val:": Tensor"},{name:"timesteps",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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_lms_discrete.py#L616",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>
`}}),N=new $({props:{name:"get_lms_coefficient",anchor:"diffusers.LMSDiscreteScheduler.get_lms_coefficient",parameters:[{name:"order",val:": int"},{name:"t",val:": int"},{name:"current_order",val:": int"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.get_lms_coefficient.order",description:`<strong>order</strong> (<code>int</code>) &#x2014;
The order of the linear multistep method.`,name:"order"},{anchor:"diffusers.LMSDiscreteScheduler.get_lms_coefficient.t",description:`<strong>t</strong> (<code>int</code>) &#x2014;
The current timestep index.`,name:"t"},{anchor:"diffusers.LMSDiscreteScheduler.get_lms_coefficient.current_order",description:`<strong>current_order</strong> (<code>int</code>) &#x2014;
The current order for which to compute the coefficient.`,name:"current_order"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py#L257",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The computed linear multistep coefficient.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>float</code></p>
`}}),V=new $({props:{name:"index_for_timestep",anchor:"diffusers.LMSDiscreteScheduler.index_for_timestep",parameters:[{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"schedule_timesteps",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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_lms_discrete.py#L345",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>
`}}),z=new $({props:{name:"scale_model_input",anchor:"diffusers.LMSDiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.LMSDiscreteScheduler.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_lms_discrete.py#L233",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 $({props:{name:"set_begin_index",anchor:"diffusers.LMSDiscreteScheduler.set_begin_index",parameters:[{name:"begin_index",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.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_lms_discrete.py#L223"}}),H=new $({props:{name:"set_timesteps",anchor:"diffusers.LMSDiscreteScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device, NoneType] = None"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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_lms_discrete.py#L286"}}),A=new $({props:{name:"step",anchor:"diffusers.LMSDiscreteScheduler.step",parameters:[{name:"model_output",val:": Tensor"},{name:"timestep",val:": typing.Union[float, torch.Tensor]"},{name:"sample",val:": Tensor"},{name:"order",val:": int = 4"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code> or <code>torch.Tensor</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.step.order",description:`<strong>order</strong> (<code>int</code>, defaults to 4) &#x2014;
The order of the linear multistep method.`,name:"order"},{anchor:"diffusers.LMSDiscreteScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.schedulers.scheduling_utils.SchedulerOutput">SchedulerOutput</a> or tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py#L535",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.schedulers.scheduling_utils.SchedulerOutput"
>SchedulerOutput</a> 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><a
  href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.schedulers.scheduling_utils.SchedulerOutput"
>SchedulerOutput</a> or <code>tuple</code></p>
`}}),U=new Fe({props:{title:"LMSDiscreteSchedulerOutput",local:"diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput",headingTag:"h2"}}),W=new $({props:{name:"class diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput",anchor:"diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput",parameters:[{name:"prev_sample",val:": Tensor"},{name:"pred_original_sample",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput.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_lms_discrete.LMSDiscreteSchedulerOutput.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_lms_discrete.py#L31"}}),R=new 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