import{S as _t,i as bt,s as vt,e as n,k as h,w as S,t as d,M as Pt,c as a,d as t,m as g,a as i,x as k,h as c,b as p,G as s,g as f,y as T,q as j,o as A,B as O,v as yt,L as $t}from"../../../chunks/vendor-hf-doc-builder.js";import{T as Dt}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Qe}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as wt}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ye}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Mt}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Et(X){let l,y,u,m,_,r,b,M;return{c(){l=n("p"),y=d("Make sure to check out the Schedulers "),u=n("a"),m=d("guide"),_=d(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),r=n("a"),b=d("reuse components across pipelines"),M=d(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(E){l=a(E,"P",{});var v=i(l);y=c(v,"Make sure to check out the Schedulers "),u=a(v,"A",{href:!0});var $=i(u);m=c($,"guide"),$.forEach(t),_=c(v," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),r=a(v,"A",{href:!0});var D=i(r);b=c(D,"reuse components across pipelines"),D.forEach(t),M=c(v," section to learn how to efficiently load the same components into multiple pipelines."),v.forEach(t),this.h()},h(){p(u,"href","/using-diffusers/schedulers"),p(r,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(E,v){f(E,l,v),s(l,y),s(l,u),s(u,m),s(l,_),s(l,r),s(r,b),s(l,M)},d(E){E&&t(l)}}}function It(X){let l,y,u,m,_;return m=new wt({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEREUE1QaXBlbGluZSUwQSUwQSUyMyUyMGxvYWQlMjBtb2RlbCUyMGFuZCUyMHNjaGVkdWxlciUwQXBpcGUlMjAlM0QlMjBERFBNUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmRkcG0tY2F0LTI1NiUyMiklMEElMEElMjMlMjBydW4lMjBwaXBlbGluZSUyMGluJTIwaW5mZXJlbmNlJTIwKHNhbXBsZSUyMHJhbmRvbSUyMG5vaXNlJTIwYW5kJTIwZGVub2lzZSklMEFpbWFnZSUyMCUzRCUyMHBpcGUoKS5pbWFnZXMlNUIwJTVEJTBBJTBBJTIzJTIwc2F2ZSUyMGltYWdlJTBBaW1hZ2Uuc2F2ZSglMjJkZHBtX2dlbmVyYXRlZF9pbWFnZS5wbmclMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># load model and scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DDPMPipeline.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cat-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># run pipeline in inference (sample random noise and denoise)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe().images[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># save image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;ddpm_generated_image.png&quot;</span>)`}}),{c(){l=n("p"),y=d("Example:"),u=h(),S(m.$$.fragment)},l(r){l=a(r,"P",{});var b=i(l);y=c(b,"Example:"),b.forEach(t),u=g(r),k(m.$$.fragment,r)},m(r,b){f(r,l,b),s(l,y),f(r,u,b),T(m,r,b),_=!0},p:$t,i(r){_||(j(m.$$.fragment,r),_=!0)},o(r){A(m.$$.fragment,r),_=!1},d(r){r&&t(l),r&&t(u),O(m,r)}}}function xt(X){let l,y,u,m,_,r,b,M,E,v,$,D,Ee,Ie,te,xe,Ue,fe,Q,Se,ue,Y,se,ke,me,G,Te,L,je,Ae,he,N,ge,I,Z,ne,q,Oe,ae,Ge,_e,P,F,Ne,ie,Ze,Be,W,Je,K,Ce,Le,qe,w,R,Fe,re,We,Re,B,be,x,J,oe,z,ze,le,Ve,ve,U,V,He,pe,Xe,Pe;return r=new Ye({}),N=new Dt({props:{$$slots:{default:[Et]},$$scope:{ctx:X}}}),q=new Ye({}),F=new Qe({props:{name:"class diffusers.DDPMPipeline",anchor:"diffusers.DDPMPipeline",parameters:[{name:"unet",val:""},{name:"scheduler",val:""}],parametersDescription:[{anchor:"diffusers.DDPMPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
A <code>UNet2DModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.DDPMPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image. Can be one of
<a href="/docs/diffusers/main/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>, or <a href="/docs/diffusers/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm/pipeline_ddpm.py#L24"}}),R=new Qe({props:{name:"__call__",anchor:"diffusers.DDPMPipeline.__call__",parameters:[{name:"batch_size",val:": int = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"num_inference_steps",val:": int = 1000"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DDPMPipeline.__call__.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate.`,name:"batch_size"},{anchor:"diffusers.DDPMPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.DDPMPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1000) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.DDPMPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generated image. Choose between <code>PIL.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.DDPMPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> is returned, otherwise a <code>tuple</code> is
returned where the first element is a list with the generated images</p>
`,returnType:`
<p><a
  href="/docs/diffusers/main/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),B=new Mt({props:{anchor:"diffusers.DDPMPipeline.__call__.example",$$slots:{default:[It]},$$scope:{ctx:X}}}),z=new Ye({}),V=new Qe({props:{name:"class diffusers.ImagePipelineOutput",anchor:"diffusers.ImagePipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}],parametersDescription:[{anchor:"diffusers.ImagePipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code> or NumPy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L112"}}),{c(){l=n("meta"),y=h(),u=n("h1"),m=n("a"),_=n("span"),S(r.$$.fragment),b=h(),M=n("span"),E=d("DDPM"),v=h(),$=n("p"),D=n("a"),Ee=d("Denoising Diffusion Probabilistic Models"),Ie=d(" (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the \u{1F917} Diffusers library, DDPM refers to the "),te=n("em"),xe=d("discrete denoising scheduler"),Ue=d(" from the paper as well as the pipeline."),fe=h(),Q=n("p"),Se=d("The abstract from the paper is:"),ue=h(),Y=n("p"),se=n("em"),ke=d("We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN."),me=h(),G=n("p"),Te=d("The original codebase can be found at "),L=n("a"),je=d("hohonathanho/diffusion"),Ae=d("."),he=h(),S(N.$$.fragment),ge=h(),I=n("h1"),Z=n("a"),ne=n("span"),S(q.$$.fragment),Oe=h(),ae=n("span"),Ge=d("DDPMPipeline"),_e=h(),P=n("div"),S(F.$$.fragment),Ne=h(),ie=n("p"),Ze=d("Pipeline for image generation."),Be=h(),W=n("p"),Je=d("This model inherits from "),K=n("a"),Ce=d("DiffusionPipeline"),Le=d(`. Check the superclass documentation for the generic methods
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