import{s as lt,n as ct,o as pt}from"../chunks/scheduler.53228c21.js";import{S as ut,i as mt,e as i,s as r,c as p,h as ft,a as d,d as s,b as o,f as b,g as u,j as v,k as _,l as t,m as c,n as m,t as f,o as h,p as g}from"../chunks/index.100fac89.js";import{C as ht}from"../chunks/CopyLLMTxtMenu.b0ff86f3.js";import{D}from"../chunks/Docstring.db856a1e.js";import{H as we,E as gt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.136ef3fd.js";function vt(Ye){let M,ae,ie,le,C,ce,L,pe,O,Je='<code>DPMSolverMultistepInverse</code> is the inverted scheduler from <a href="https://huggingface.co/papers/2206.00927" rel="nofollow">DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps</a> and <a href="https://huggingface.co/papers/2211.01095" rel="nofollow">DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models</a> by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.',ue,E,Ke='The implementation is mostly based on the DDIM inversion definition of <a href="https://huggingface.co/papers/2211.09794" rel="nofollow">Null-text Inversion for Editing Real Images using Guided Diffusion Models</a> and notebook implementation of the <code>DiffEdit</code> latent inversion from <a href="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb" rel="nofollow">Xiang-cd/DiffEdit-stable-diffusion</a>.',me,q,fe,N,Xe=`Dynamic thresholding from <a href="https://huggingface.co/papers/2205.11487" rel="nofollow">Imagen</a> is supported, and for pixel-space
diffusion models, you can set both <code>algorithm_type=&quot;dpmsolver++&quot;</code> and <code>thresholding=True</code> to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
Stable Diffusion.`,he,k,ge,a,H,Ce,J,Ze='<code>DPMSolverMultistepInverseScheduler</code> is the reverse scheduler of <a href="/docs/diffusers/main/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler">DPMSolverMultistepScheduler</a>.',Le,K,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.`,Oe,S,F,Ee,X,et=`Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
integral of the data prediction model.`,qe,A,tt=`<p>&gt; The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both
noise &gt; prediction and data prediction models.</p>`,Ne,$,z,ke,Z,st="One step for the first-order DPMSolver (equivalent to DDIM).",He,y,V,Fe,Q,rt="One step for the second-order multistep DPMSolver.",Ae,T,W,ze,ee,ot="One step for the third-order multistep DPMSolver.",Ve,P,U,We,te,nt=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.`,Ue,I,j,je,se,it="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",Ge,w,G,Be,re,dt=`Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
the multistep DPMSolver.`,ve,B,_e,x,R,Re,oe,at="Base class for the output of a scheduler’s <code>step</code> function.",be,Y,Se,de,Me;return C=new ht({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),L=new we({props:{title:"DPMSolverMultistepInverse",local:"dpmsolvermultistepinverse",headingTag:"h1"}}),q=new we({props:{title:"Tips",local:"tips",headingTag:"h2"}}),k=new we({props:{title:"DPMSolverMultistepInverseScheduler",local:"diffusers.DPMSolverMultistepInverseScheduler",headingTag:"h2"}}),H=new D({props:{name:"class diffusers.DPMSolverMultistepInverseScheduler",anchor:"diffusers.DPMSolverMultistepInverseScheduler",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:": str = 'linear'"},{name:"trained_betas",val:": typing.Union[numpy.ndarray, typing.List[float], NoneType] = None"},{name:"solver_order",val:": int = 2"},{name:"prediction_type",val:": str = 'epsilon'"},{name:"thresholding",val:": bool = False"},{name:"dynamic_thresholding_ratio",val:": float = 0.995"},{name:"sample_max_value",val:": float = 1.0"},{name:"algorithm_type",val:": str = 'dpmsolver++'"},{name:"solver_type",val:": str = 'midpoint'"},{name:"lower_order_final",val:": bool = True"},{name:"euler_at_final",val:": bool = False"},{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:"use_flow_sigmas",val:": typing.Optional[bool] = False"},{name:"flow_shift",val:": typing.Optional[float] = 1.0"},{name:"lambda_min_clipped",val:": float = -inf"},{name:"variance_type",val:": typing.Optional[str] = None"},{name:"timestep_spacing",val:": str = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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>, <code>scaled_linear</code>, or <code>squaredcos_cap_v2</code>.`,name:"beta_schedule"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.solver_order",description:`<strong>solver_order</strong> (<code>int</code>, defaults to 2) &#x2014;
The DPMSolver order which can be <code>1</code> or <code>2</code> or <code>3</code>. It is recommended to use <code>solver_order=2</code> for guided
sampling, and <code>solver_order=3</code> for unconditional sampling.`,name:"solver_order"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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://imagen.research.google/video/paper.pdf" rel="nofollow">Imagen
Video</a> paper).`,name:"prediction_type"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.thresholding",description:`<strong>thresholding</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to use the &#x201C;dynamic thresholding&#x201D; method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion.`,name:"thresholding"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.dynamic_thresholding_ratio",description:`<strong>dynamic_thresholding_ratio</strong> (<code>float</code>, defaults to 0.995) &#x2014;
The ratio for the dynamic thresholding method. Valid only when <code>thresholding=True</code>.`,name:"dynamic_thresholding_ratio"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.sample_max_value",description:`<strong>sample_max_value</strong> (<code>float</code>, defaults to 1.0) &#x2014;
The threshold value for dynamic thresholding. Valid only when <code>thresholding=True</code> and
<code>algorithm_type=&quot;dpmsolver++&quot;</code>.`,name:"sample_max_value"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.algorithm_type",description:`<strong>algorithm_type</strong> (<code>str</code>, defaults to <code>dpmsolver++</code>) &#x2014;
Algorithm type for the solver; can be <code>dpmsolver</code>, <code>dpmsolver++</code>, <code>sde-dpmsolver</code> or <code>sde-dpmsolver++</code>. The
<code>dpmsolver</code> type implements the algorithms in the <a href="https://huggingface.co/papers/2206.00927" rel="nofollow">DPMSolver</a>
paper, and the <code>dpmsolver++</code> type implements the algorithms in the
<a href="https://huggingface.co/papers/2211.01095" rel="nofollow">DPMSolver++</a> paper. It is recommended to use <code>dpmsolver++</code> or
<code>sde-dpmsolver++</code> with <code>solver_order=2</code> for guided sampling like in Stable Diffusion.`,name:"algorithm_type"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.solver_type",description:`<strong>solver_type</strong> (<code>str</code>, defaults to <code>midpoint</code>) &#x2014;
Solver type for the second-order solver; can be <code>midpoint</code> or <code>heun</code>. The solver type slightly affects the
sample quality, especially for a small number of steps. It is recommended to use <code>midpoint</code> solvers.`,name:"solver_type"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.lower_order_final",description:`<strong>lower_order_final</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to use lower-order solvers in the final steps. Only valid for &lt; 15 inference steps. This can
stabilize the sampling of DPMSolver for steps &lt; 15, especially for steps &lt;= 10.`,name:"lower_order_final"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.euler_at_final",description:`<strong>euler_at_final</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to use Euler&#x2019;s method in the final step. It is a trade-off between numerical stability and detail
richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference
steps, but sometimes may result in blurring.`,name:"euler_at_final"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.lambda_min_clipped",description:`<strong>lambda_min_clipped</strong> (<code>float</code>, defaults to <code>-inf</code>) &#x2014;
Clipping threshold for the minimum value of <code>lambda(t)</code> for numerical stability. This is critical for the
cosine (<code>squaredcos_cap_v2</code>) noise schedule.`,name:"lambda_min_clipped"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.variance_type",description:`<strong>variance_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Set to &#x201C;learned&#x201D; or &#x201C;learned_range&#x201D; for diffusion models that predict variance. If set, the model&#x2019;s output
contains the predicted Gaussian variance.`,name:"variance_type"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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_dpmsolver_multistep_inverse.py#L78"}}),F=new D({props:{name:"convert_model_output",anchor:"diffusers.DPMSolverMultistepInverseScheduler.convert_model_output",parameters:[{name:"model_output",val:": Tensor"},{name:"*args",val:""},{name:"sample",val:": Tensor = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.convert_model_output.model_output",description:`<strong>model_output</strong> (<code>torch.Tensor</code>) &#x2014;
The direct output from the learned diffusion model.`,name:"model_output"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.convert_model_output.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
A current instance of a sample created by the diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L482",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The converted model output.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),z=new D({props:{name:"dpm_solver_first_order_update",anchor:"diffusers.DPMSolverMultistepInverseScheduler.dpm_solver_first_order_update",parameters:[{name:"model_output",val:": Tensor"},{name:"*args",val:""},{name:"sample",val:": Tensor = None"},{name:"noise",val:": typing.Optional[torch.Tensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.dpm_solver_first_order_update.model_output",description:`<strong>model_output</strong> (<code>torch.Tensor</code>) &#x2014;
The direct output from the learned diffusion model.`,name:"model_output"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.dpm_solver_first_order_update.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
A current instance of a sample created by the diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L581",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The sample tensor at the previous timestep.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),V=new D({props:{name:"multistep_dpm_solver_second_order_update",anchor:"diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_second_order_update",parameters:[{name:"model_output_list",val:": typing.List[torch.Tensor]"},{name:"*args",val:""},{name:"sample",val:": Tensor = None"},{name:"noise",val:": typing.Optional[torch.Tensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_second_order_update.model_output_list",description:`<strong>model_output_list</strong> (<code>List[torch.Tensor]</code>) &#x2014;
The direct outputs from learned diffusion model at current and latter timesteps.`,name:"model_output_list"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_second_order_update.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
A current instance of a sample created by the diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L651",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The sample tensor at the previous timestep.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),W=new D({props:{name:"multistep_dpm_solver_third_order_update",anchor:"diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_third_order_update",parameters:[{name:"model_output_list",val:": typing.List[torch.Tensor]"},{name:"*args",val:""},{name:"sample",val:": Tensor = None"},{name:"noise",val:": typing.Optional[torch.Tensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_third_order_update.model_output_list",description:`<strong>model_output_list</strong> (<code>List[torch.Tensor]</code>) &#x2014;
The direct outputs from learned diffusion model at current and latter timesteps.`,name:"model_output_list"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.multistep_dpm_solver_third_order_update.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
A current instance of a sample created by diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L775",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The sample tensor at the previous timestep.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),U=new D({props:{name:"scale_model_input",anchor:"diffusers.DPMSolverMultistepInverseScheduler.scale_model_input",parameters:[{name:"sample",val:": Tensor"},{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014;
The input sample.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L972",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>
`}}),j=new D({props:{name:"set_timesteps",anchor:"diffusers.DPMSolverMultistepInverseScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int = None"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.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_dpmsolver_multistep_inverse.py#L245"}}),G=new D({props:{name:"step",anchor:"diffusers.DPMSolverMultistepInverseScheduler.step",parameters:[{name:"model_output",val:": Tensor"},{name:"timestep",val:": typing.Union[int, torch.Tensor]"},{name:"sample",val:": Tensor"},{name:"generator",val:" = None"},{name:"variance_noise",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.step.timestep",description:`<strong>timestep</strong> (<code>int</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.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.DPMSolverMultistepInverseScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.step.variance_noise",description:`<strong>variance_noise</strong> (<code>torch.Tensor</code>) &#x2014;
Alternative to generating noise with <code>generator</code> by directly providing the noise for the variance
itself. Useful for methods such as <code>CycleDiffusion</code>.`,name:"variance_noise"},{anchor:"diffusers.DPMSolverMultistepInverseScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/main/en/api/schedulers/edm_multistep_dpm_solver#diffusers.schedulers.scheduling_utils.SchedulerOutput">SchedulerOutput</a> or <code>tuple</code>.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py#L889",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/edm_multistep_dpm_solver#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/edm_multistep_dpm_solver#diffusers.schedulers.scheduling_utils.SchedulerOutput"
>SchedulerOutput</a> or <code>tuple</code></p>
`}}),B=new we({props:{title:"SchedulerOutput",local:"diffusers.schedulers.scheduling_utils.SchedulerOutput",headingTag:"h2"}}),R=new D({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"}}),Y=new gt({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md"}}),{c(){M=i("meta"),ae=r(),ie=i("p"),le=r(),p(C.$$.fragment),ce=r(),p(L.$$.fragment),pe=r(),O=i("p"),O.innerHTML=Je,ue=r(),E=i("p"),E.innerHTML=Ke,me=r(),p(q.$$.fragment),fe=r(),N=i("p"),N.innerHTML=Xe,he=r(),p(k.$$.fragment),ge=r(),a=i("div"),p(H.$$.fragment),Ce=r(),J=i("p"),J.innerHTML=Ze,Le=r(),K=i("p"),K.innerHTML=Qe,Oe=r(),S=i("div"),p(F.$$.fragment),Ee=r(),X=i("p"),X.textContent=et,qe=r(),A=i("blockquote"),A.innerHTML=tt,Ne=r(),$=i("div"),p(z.$$.fragment),ke=r(),Z=i("p"),Z.textContent=st,He=r(),y=i("div"),p(V.$$.fragment),Fe=r(),Q=i("p"),Q.textContent=rt,Ae=r(),T=i("div"),p(W.$$.fragment),ze=r(),ee=i("p"),ee.textContent=ot,Ve=r(),P=i("div"),p(U.$$.fragment),We=r(),te=i("p"),te.textContent=nt,Ue=r(),I=i("div"),p(j.$$.fragment),je=r(),se=i("p"),se.textContent=it,Ge=r(),w=i("div"),p(G.$$.fragment),Be=r(),re=i("p"),re.textContent=dt,ve=r(),p(B.$$.fragment),_e=r(),x=i("div"),p(R.$$.fragment),Re=r(),oe=i("p"),oe.innerHTML=at,be=r(),p(Y.$$.fragment),Se=r(),de=i("p"),this.h()},l(e){const n=ft("svelte-u9bgzb",document.head);M=d(n,"META",{name:!0,content:!0}),n.forEach(s),ae=o(e),ie=d(e,"P",{}),b(ie).forEach(s),le=o(e),u(C.$$.fragment,e),ce=o(e),u(L.$$.fragment,e),pe=o(e),O=d(e,"P",{"data-svelte-h":!0}),v(O)!=="svelte-9vmlu9"&&(O.innerHTML=Je),ue=o(e),E=d(e,"P",{"data-svelte-h":!0}),v(E)!=="svelte-jb4p1n"&&(E.innerHTML=Ke),me=o(e),u(q.$$.fragment,e),fe=o(e),N=d(e,"P",{"data-svelte-h":!0}),v(N)!=="svelte-1ng0gyl"&&(N.innerHTML=Xe),he=o(e),u(k.$$.fragment,e),ge=o(e),a=d(e,"DIV",{class:!0});var l=b(a);u(H.$$.fragment,l),Ce=o(l),J=d(l,"P",{"data-svelte-h":!0}),v(J)!=="svelte-25li7n"&&(J.innerHTML=Ze),Le=o(l),K=d(l,"P",{"data-svelte-h":!0}),v(K)!=="svelte-linuuh"&&(K.innerHTML=Qe),Oe=o(l),S=d(l,"DIV",{class:!0});var ne=b(S);u(F.$$.fragment,ne),Ee=o(ne),X=d(ne,"P",{"data-svelte-h":!0}),v(X)!=="svelte-1st02i8"&&(X.textContent=et),qe=o(ne),A=d(ne,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),v(A)!=="svelte-lwd2ft"&&(A.innerHTML=tt),ne.forEach(s),Ne=o(l),$=d(l,"DIV",{class:!0});var xe=b($);u(z.$$.fragment,xe),ke=o(xe),Z=d(xe,"P",{"data-svelte-h":!0}),v(Z)!=="svelte-4nw43d"&&(Z.textContent=st),xe.forEach(s),He=o(l),y=d(l,"DIV",{class:!0});var De=b(y);u(V.$$.fragment,De),Fe=o(De),Q=d(De,"P",{"data-svelte-h":!0}),v(Q)!=="svelte-1d6o6zu"&&(Q.textContent=rt),De.forEach(s),Ae=o(l),T=d(l,"DIV",{class:!0});var $e=b(T);u(W.$$.fragment,$e),ze=o($e),ee=d($e,"P",{"data-svelte-h":!0}),v(ee)!=="svelte-17nne8p"&&(ee.textContent=ot),$e.forEach(s),Ve=o(l),P=d(l,"DIV",{class:!0});var ye=b(P);u(U.$$.fragment,ye),We=o(ye),te=d(ye,"P",{"data-svelte-h":!0}),v(te)!=="svelte-1rkfgpx"&&(te.textContent=nt),ye.forEach(s),Ue=o(l),I=d(l,"DIV",{class:!0});var Te=b(I);u(j.$$.fragment,Te),je=o(Te),se=d(Te,"P",{"data-svelte-h":!0}),v(se)!=="svelte-1vzm9q"&&(se.textContent=it),Te.forEach(s),Ge=o(l),w=d(l,"DIV",{class:!0});var Pe=b(w);u(G.$$.fragment,Pe),Be=o(Pe),re=d(Pe,"P",{"data-svelte-h":!0}),v(re)!=="svelte-7jbjgx"&&(re.textContent=dt),Pe.forEach(s),l.forEach(s),ve=o(e),u(B.$$.fragment,e),_e=o(e),x=d(e,"DIV",{class:!0});var Ie=b(x);u(R.$$.fragment,Ie),Re=o(Ie),oe=d(Ie,"P",{"data-svelte-h":!0}),v(oe)!=="svelte-6ojmkw"&&(oe.innerHTML=at),Ie.forEach(s),be=o(e),u(Y.$$.fragment,e),Se=o(e),de=d(e,"P",{}),b(de).forEach(s),this.h()},h(){_(M,"name","hf:doc:metadata"),_(M,"content",_t),_(A,"class","tip"),_(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(a,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),_(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,n){t(document.head,M),c(e,ae,n),c(e,ie,n),c(e,le,n),m(C,e,n),c(e,ce,n),m(L,e,n),c(e,pe,n),c(e,O,n),c(e,ue,n),c(e,E,n),c(e,me,n),m(q,e,n),c(e,fe,n),c(e,N,n),c(e,he,n),m(k,e,n),c(e,ge,n),c(e,a,n),m(H,a,null),t(a,Ce),t(a,J),t(a,Le),t(a,K),t(a,Oe),t(a,S),m(F,S,null),t(S,Ee),t(S,X),t(S,qe),t(S,A),t(a,Ne),t(a,$),m(z,$,null),t($,ke),t($,Z),t(a,He),t(a,y),m(V,y,null),t(y,Fe),t(y,Q),t(a,Ae),t(a,T),m(W,T,null),t(T,ze),t(T,ee),t(a,Ve),t(a,P),m(U,P,null),t(P,We),t(P,te),t(a,Ue),t(a,I),m(j,I,null),t(I,je),t(I,se),t(a,Ge),t(a,w),m(G,w,null),t(w,Be),t(w,re),c(e,ve,n),m(B,e,n),c(e,_e,n),c(e,x,n),m(R,x,null),t(x,Re),t(x,oe),c(e,be,n),m(Y,e,n),c(e,Se,n),c(e,de,n),Me=!0},p:ct,i(e){Me||(f(C.$$.fragment,e),f(L.$$.fragment,e),f(q.$$.fragment,e),f(k.$$.fragment,e),f(H.$$.fragment,e),f(F.$$.fragment,e),f(z.$$.fragment,e),f(V.$$.fragment,e),f(W.$$.fragment,e),f(U.$$.fragment,e),f(j.$$.fragment,e),f(G.$$.fragment,e),f(B.$$.fragment,e),f(R.$$.fragment,e),f(Y.$$.fragment,e),Me=!0)},o(e){h(C.$$.fragment,e),h(L.$$.fragment,e),h(q.$$.fragment,e),h(k.$$.fragment,e),h(H.$$.fragment,e),h(F.$$.fragment,e),h(z.$$.fragment,e),h(V.$$.fragment,e),h(W.$$.fragment,e),h(U.$$.fragment,e),h(j.$$.fragment,e),h(G.$$.fragment,e),h(B.$$.fragment,e),h(R.$$.fragment,e),h(Y.$$.fragment,e),Me=!1},d(e){e&&(s(ae),s(ie),s(le),s(ce),s(pe),s(O),s(ue),s(E),s(me),s(fe),s(N),s(he),s(ge),s(a),s(ve),s(_e),s(x),s(be),s(Se),s(de)),s(M),g(C,e),g(L,e),g(q,e),g(k,e),g(H),g(F),g(z),g(V),g(W),g(U),g(j),g(G),g(B,e),g(R),g(Y,e)}}}const _t='{"title":"DPMSolverMultistepInverse","local":"dpmsolvermultistepinverse","sections":[{"title":"Tips","local":"tips","sections":[],"depth":2},{"title":"DPMSolverMultistepInverseScheduler","local":"diffusers.DPMSolverMultistepInverseScheduler","sections":[],"depth":2},{"title":"SchedulerOutput","local":"diffusers.schedulers.scheduling_utils.SchedulerOutput","sections":[],"depth":2}],"depth":1}';function bt(Ye){return pt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class yt extends ut{constructor(M){super(),mt(this,M,bt,vt,lt,{})}}export{yt as component};
