import{S as La,i as za,s as Qa,e as n,k as l,w as j,t as h,M as qa,c as i,d as a,m as p,a as s,x as R,h as c,b as r,G as t,g as m,y as W,L as Ha,q as E,o as Z,B as $,v as Oa}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Fa}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Ka}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as ve}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function et(fa){let d,we,u,M,ee,x,Se,ae,Ae,ye,f,T,te,B,De,ne,Ne,_e,G,V,Ce,Xe,be,L,Ye,Me,z,Fe,Te,J,Le,S,ze,Qe,Je,g,P,ie,A,qe,se,He,Pe,k,re,v,oe,Oe,Ke,le,ea,aa,Q,ta,na,pe,w,me,D,ia,sa,he,ce,ra,oa,q,la,ke,y,U,de,N,pa,ue,ma,Ue,C,Ie,_,I,fe,X,ha,ge,ca,je,b,Y,da,H,F,Re;return x=new ve({}),B=new ve({}),A=new ve({}),N=new ve({}),C=new Ka({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

<span class="hljs-keyword">import</span> torch

<span class="hljs-keyword">import</span> PIL
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> RePaintPipeline, RePaintScheduler


<span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url</span>):
    response = requests.get(url)
    <span class="hljs-keyword">return</span> PIL.Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)


img_url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png&quot;</span>
mask_url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png&quot;</span>

<span class="hljs-comment"># Load the original image and the mask as PIL images</span>
original_image = download_image(img_url).resize((<span class="hljs-number">256</span>, <span class="hljs-number">256</span>))
mask_image = download_image(mask_url).resize((<span class="hljs-number">256</span>, <span class="hljs-number">256</span>))

<span class="hljs-comment"># Load the RePaint scheduler and pipeline based on a pretrained DDPM model</span>
scheduler = RePaintScheduler.from_pretrained(<span class="hljs-string">&quot;google/ddpm-ema-celebahq-256&quot;</span>)
pipe = RePaintPipeline.from_pretrained(<span class="hljs-string">&quot;google/ddpm-ema-celebahq-256&quot;</span>, scheduler=scheduler)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
output = pipe(
    image=original_image,
    mask_image=mask_image,
    num_inference_steps=<span class="hljs-number">250</span>,
    eta=<span class="hljs-number">0.0</span>,
    jump_length=<span class="hljs-number">10</span>,
    jump_n_sample=<span class="hljs-number">10</span>,
    generator=generator,
)
inpainted_image = output.images[<span class="hljs-number">0</span>]`}}),X=new ve({}),Y=new Fa({props:{name:"class diffusers.RePaintPipeline",anchor:"diffusers.RePaintPipeline",parameters:[{name:"unet",val:""},{name:"scheduler",val:""}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/repaint/pipeline_repaint.py#L79"}}),F=new Fa({props:{name:"__call__",anchor:"diffusers.RePaintPipeline.__call__",parameters:[{name:"image",val:": typing.Union[torch.Tensor, PIL.Image.Image]"},{name:"mask_image",val:": typing.Union[torch.Tensor, PIL.Image.Image]"},{name:"num_inference_steps",val:": int = 250"},{name:"eta",val:": float = 0.0"},{name:"jump_length",val:": int = 10"},{name:"jump_n_sample",val:": int = 10"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.RePaintPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.FloatTensor</code> or <code>PIL.Image.Image</code>) &#x2014;
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The mask_image where 0.0 values define which part of the original image to inpaint (change).`,name:"mask_image"},{anchor:"diffusers.RePaintPipeline.__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.RePaintPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>) &#x2014;
The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM
and 1.0 is DDPM scheduler respectively.`,name:"eta"},{anchor:"diffusers.RePaintPipeline.__call__.jump_length",description:`<strong>jump_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
The number of steps taken forward in time before going backward in time for a single jump (&#x201C;j&#x201D; in
RePaint paper). Take a look at Figure 9 and 10 in <a href="https://arxiv.org/pdf/2201.09865.pdf" rel="nofollow">https://arxiv.org/pdf/2201.09865.pdf</a>.`,name:"jump_length"},{anchor:"diffusers.RePaintPipeline.__call__.jump_n_sample",description:`<strong>jump_n_sample</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
The number of times we will make forward time jump for a given chosen time sample. Take a look at
Figure 9 and 10 in <a href="https://arxiv.org/pdf/2201.09865.pdf" rel="nofollow">https://arxiv.org/pdf/2201.09865.pdf</a>.`,name:"jump_n_sample"},{anchor:"diffusers.RePaintPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.RePaintPipeline.__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 generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.RePaintPipeline.__call__.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/v0.18.0/en/api/outputs#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/repaint/pipeline_repaint.py#L87",returnDescription:`
<p><code>~pipelines.utils.ImagePipelineOutput</code> if <code>return_dict</code> is
True, otherwise a \`tuple. When returning a tuple, the first element is a list with the generated images.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.18.0/en/api/outputs#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
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