import{S as Tt,i as Et,s as Ot,e as o,k as i,w,t as d,M as At,c as r,d as s,m as l,a as n,x as T,h as c,b as a,G as t,g as h,y as E,L as Nt,q as O,o as A,B as N,v as It}from"../../../chunks/vendor-hf-doc-builder.js";import{D as de}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as at}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Ct(it){let m,le,g,y,K,I,$e,Q,we,ce,C,F,Te,Ee,pe,G,Oe,ue,R,X,Ae,he,_,P,Z,q,Ne,ee,Ie,fe,u,L,Ce,W,te,Fe,qe,Le,v,ke,j,Ve,He,B,Je,Ue,ze,S,k,Ge,se,Re,We,M,V,je,oe,Be,Ye,x,H,Ke,re,Qe,me,D,$,ne,J,Xe,ae,Ze,ge,b,U,et,z,tt,ie,st,ot,_e;return I=new at({}),q=new at({}),L=new de({props:{name:"class diffusers.DDPMScheduler",anchor:"diffusers.DDPMScheduler",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:"variance_type",val:": str = 'fixed_small'"},{name:"clip_sample",val:": bool = True"},{name:"prediction_type",val:": str = 'epsilon'"},{name:"thresholding",val:": bool = False"},{name:"dynamic_thresholding_ratio",val:": float = 0.995"},{name:"clip_sample_range",val:": float = 1.0"},{name:"sample_max_value",val:": float = 1.0"},{name:"timestep_spacing",val:": str = 'leading'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.variance_type",description:`<strong>variance_type</strong> (<code>str</code>, defaults to <code>&quot;fixed_small&quot;</code>) &#x2014;
Clip the variance when adding noise to the denoised sample. Choose from <code>fixed_small</code>, <code>fixed_small_log</code>,
<code>fixed_large</code>, <code>fixed_large_log</code>, <code>learned</code> or <code>learned_range</code>.`,name:"variance_type"},{anchor:"diffusers.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.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.DDPMScheduler.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>.`,name:"sample_max_value"},{anchor:"diffusers.DDPMScheduler.timestep_spacing",description:`<strong>timestep_spacing</strong> (<code>str</code>, defaults to <code>&quot;leading&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.DDPMScheduler.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_ddpm.py#L91"}}),k=new de({props:{name:"scale_model_input",anchor:"diffusers.DDPMScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"diffusers.DDPMScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.DDPMScheduler.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_ddpm.py#L189",returnDescription:`
<p>A scaled input sample.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),V=new de({props:{name:"set_timesteps",anchor:"diffusers.DDPMScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": typing.Optional[int] = None"},{name:"device",val:": typing.Union[str, torch.device] = None"},{name:"timesteps",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"diffusers.DDPMScheduler.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. If used,
<code>timesteps</code> must be <code>None</code>.`,name:"num_inference_steps"},{anchor:"diffusers.DDPMScheduler.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"},{anchor:"diffusers.DDPMScheduler.set_timesteps.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Custom timesteps used to support arbitrary spacing between timesteps. If <code>None</code>, then the default
timestep spacing strategy of equal spacing between timesteps is used. If <code>timesteps</code> is passed,
<code>num_inference_steps</code> must be <code>None</code>.`,name:"timesteps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py#L206"}}),H=new de({props:{name:"step",anchor:"diffusers.DDPMScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": int"},{name:"sample",val:": FloatTensor"},{name:"generator",val:" = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DDPMScheduler.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.DDPMScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.DDPMScheduler.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.DDPMScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.DDPMScheduler.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/ddpm#diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput">DDPMSchedulerOutput</a> or <code>tuple</code>.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py#L355",returnDescription:`
<p>If return_dict is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/schedulers/ddpm#diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"
>DDPMSchedulerOutput</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/ddpm#diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"
>DDPMSchedulerOutput</a> or <code>tuple</code></p>
`}}),J=new at({}),U=new de({props:{name:"class diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput",anchor:"diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput",parameters:[{name:"prev_sample",val:": FloatTensor"},{name:"pred_original_sample",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput.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"},{anchor:"diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput.pred_original_sample",description:`<strong>pred_original_sample</strong> (<code>torch.FloatTensor</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_ddpm.py#L30"}}),{c(){m=o("meta"),le=i(),g=o("h1"),y=o("a"),K=o("span"),w(I.$$.fragment),$e=i(),Q=o("span"),we=d("DDPMScheduler"),ce=i(),C=o("p"),F=o("a"),Te=d("Denoising Diffusion Probabilistic Models"),Ee=d(" (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the \u{1F917} Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline."),pe=i(),G=o("p"),Oe=d("The abstract from the paper is:"),ue=i(),R=o("p"),X=o("em"),Ae=d("We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN."),he=i(),_=o("h2"),P=o("a"),Z=o("span"),w(q.$$.fragment),Ne=i(),ee=o("span"),Ie=d("DDPMScheduler"),fe=i(),u=o("div"),w(L.$$.fragment),Ce=i(),W=o("p"),te=o("code"),Fe=d("DDPMScheduler"),qe=d(" explores the connections between denoising score matching and Langevin dynamics sampling."),Le=i(),v=o("p"),ke=d("This model inherits from "),j=o("a"),Ve=d("SchedulerMixin"),He=d(" and "),B=o("a"),Je=d("ConfigMixin"),Ue=d(`. Check the superclass documentation for the generic
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