import{S as Wt,i as jt,s as zt,e as s,k as l,w as V,t as i,M as Bt,c as r,d as o,m as c,a as n,x as H,h as a,b as d,G as e,g as u,y as J,L as Kt,q as U,o as G,B as R,v as Qt}from"../../../chunks/vendor-hf-doc-builder.js";import{D as $e}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as vt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Xt(bt){let _,pe,v,y,Y,E,Ee,Z,Ae,he,b,P,ee,A,Te,te,Ne,fe,T,N,Oe,Ie,ue,W,Le,me,j,Ce,ge,x,Fe,O,ke,qe,_e,D,w,oe,I,Ve,se,He,ve,p,L,Je,re,Ue,Ge,f,z,Re,We,ne,je,ze,ie,Be,Ke,ae,Qe,Xe,B,Ye,Ze,K,et,tt,Q,ot,st,rt,X,nt,C,it,at,M,F,dt,de,lt,ct,S,k,pt,le,ht,ft,$,q,ut,ce,mt,be;return E=new vt({}),A=new vt({}),I=new vt({}),L=new $e({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"}],parametersDescription:[{anchor:"diffusers.DDPMScheduler.num_train_timesteps",description:"<strong>num_train_timesteps</strong> (<code>int</code>) &#x2014; number of diffusion steps used to train the model.",name:"num_train_timesteps"},{anchor:"diffusers.DDPMScheduler.beta_start",description:"<strong>beta_start</strong> (<code>float</code>) &#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>) &#x2014; the final <code>beta</code> value.",name:"beta_end"},{anchor:"diffusers.DDPMScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</code>) &#x2014;
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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prediction type of the scheduler function, one of <code>epsilon</code> (predicting the noise of the diffusion
process), <code>sample</code> (directly predicting the noisy sample<code>) or </code>v_prediction\` (see section 2.4
<a href="https://imagen.research.google/video/paper.pdf" rel="nofollow">https://imagen.research.google/video/paper.pdf</a>)`,name:"prediction_type"},{anchor:"diffusers.DDPMScheduler.thresholding",description:`<strong>thresholding</strong> (<code>bool</code>, default <code>False</code>) &#x2014;
whether to use the &#x201C;dynamic thresholding&#x201D; method (introduced by Imagen, <a href="https://arxiv.org/abs/2205.11487" rel="nofollow">https://arxiv.org/abs/2205.11487</a>).
Note that the thresholding method is unsuitable for latent-space diffusion models (such as
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the ratio for the dynamic thresholding method. Default is <code>0.995</code>, the same as Imagen
(<a href="https://arxiv.org/abs/2205.11487" rel="nofollow">https://arxiv.org/abs/2205.11487</a>). 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>, default <code>1.0</code>) &#x2014;
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<p>scaled input sample</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),k=new $e({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>Optional[int]</code>) &#x2014;
the number of diffusion steps used when generating samples with a pre-trained model. If passed, then
<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>, optional) &#x2014;
the device to which the timesteps are moved to.`,name:"device"},{anchor:"diffusers.DDPMScheduler.set_timesteps.custom_timesteps",description:`<strong>custom_timesteps</strong> (<code>List[int]</code>, optional) &#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 passed, <code>num_inference_steps</code>
must be <code>None</code>.`,name:"custom_timesteps"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.1/src/diffusers/schedulers/scheduling_ddpm.py#L185"}}),q=new $e({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; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.DDPMScheduler.step.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DDPMScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.
generator &#x2014; random number generator.`,name:"sample"},{anchor:"diffusers.DDPMScheduler.step.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>) &#x2014; option for returning tuple rather than DDPMSchedulerOutput class",name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.1/src/diffusers/schedulers/scheduling_ddpm.py#L312",returnDescription:`
<p><code>~schedulers.scheduling_utils.DDPMSchedulerOutput</code> if <code>return_dict</code> is True, otherwise a <code>tuple</code>. When
returning a tuple, the first element is the sample tensor.</p>
`,returnType:`
<p><code>~schedulers.scheduling_utils.DDPMSchedulerOutput</code> or <code>tuple</code></p>
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