import{S as Nt,i as It,s as Lt,e as r,k as c,w as K,t as a,M as qt,c as o,d as s,m as l,a as n,x as k,h as d,b as i,G as e,g as h,y as A,L as Bt,q as O,o as C,B as F,v as Ut}from"../../../chunks/vendor-hf-doc-builder.js";import{D as le}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as ft}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Vt(mt){let g,ue,_,y,Z,N,Te,ee,Ke,pe,f,ke,te,Ae,Oe,I,Ce,Fe,L,Ne,Ie,he,$,Le,q,qe,Be,fe,v,P,se,B,Ue,re,Ve,me,u,U,Ge,V,He,G,Re,ze,Je,b,We,j,Ye,je,Q,Qe,Xe,Ze,w,H,et,oe,tt,st,M,R,rt,ne,ot,nt,E,z,it,ie,at,ge,D,x,ae,J,dt,de,ct,_e,S,W,lt,Y,ut,ce,pt,ht,ve;return N=new ft({}),B=new ft({}),U=new le({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:"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.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.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. You can use a combination of <code>offset=1</code> and
<code>set_alpha_to_one=False</code> to make the last step use step 0 for the previous alpha product like in Stable
Diffusion.`,name:"steps_offset"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L71"}}),H=new le({props:{name:"scale_model_input",anchor:"diffusers.KDPM2DiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</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#L167",returnDescription:`
<p>A scaled input sample.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),R=new le({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#L196"}}),z=new le({props:{name:"step",anchor:"diffusers.KDPM2DiscreteScheduler.step",parameters:[{name:"model_output",val:": typing.Union[torch.FloatTensor, numpy.ndarray]"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"},{name:"sample",val:": typing.Union[torch.FloatTensor, numpy.ndarray]"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KDPM2DiscreteScheduler.step.model_output",description:`<strong>model_output</strong> (<code>torch.FloatTensor</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.FloatTensor</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 <a href="/docs/diffusers/main/en/api/schedulers/dpm_discrete#diffusers.schedulers.scheduling_utils.SchedulerOutput">SchedulerOutput</a> or tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py#L295",returnDescription:`
<p>If return_dict is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/schedulers/dpm_discrete#diffusers.schedulers.scheduling_utils.SchedulerOutput"
>SchedulerOutput</a> is returned, otherwise a
tuple is returned where the first element is the sample tensor.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/main/en/api/schedulers/dpm_discrete#diffusers.schedulers.scheduling_utils.SchedulerOutput"
>SchedulerOutput</a> or <code>tuple</code></p>
`}}),J=new ft({}),W=new le({props:{name:"class diffusers.schedulers.scheduling_utils.SchedulerOutput",anchor:"diffusers.schedulers.scheduling_utils.SchedulerOutput",parameters:[{name:"prev_sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.schedulers.scheduling_utils.SchedulerOutput.prev_sample",description:`<strong>prev_sample</strong> (<code>torch.FloatTensor</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#L50"}}),{c(){g=r("meta"),ue=c(),_=r("h1"),y=r("a"),Z=r("span"),K(N.$$.fragment),Te=c(),ee=r("span"),Ke=a("KDPM2DiscreteScheduler"),pe=c(),f=r("p"),ke=a("The "),te=r("code"),Ae=a("KDPM2DiscreteScheduler"),Oe=a(" is inspired by the "),I=r("a"),Ce=a("Elucidating the Design Space of Diffusion-Based Generative Models"),Fe=a(" paper, and the scheduler is ported from and created by "),L=r("a"),Ne=a("Katherine Crowson"),Ie=a("."),he=c(),$=r("p"),Le=a("The original codebase can be found at "),q=r("a"),qe=a("crowsonkb/k-diffusion"),Be=a("."),fe=c(),v=r("h2"),P=r("a"),se=r("span"),K(B.$$.fragment),Ue=c(),re=r("span"),Ve=a("KDPM2DiscreteScheduler"),me=c(),u=r("div"),K(U.$$.fragment),Ge=c(),V=r("p"),He=a("KDPM2DiscreteScheduler is inspired by the DPMSolver2 and Algorithm 2 from the "),G=r("a"),Re=a(`Elucidating the Design Space of
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