import{S as Va,i as Ya,s as Ja,e as n,k as l,w as E,t as h,M as Ka,c as i,d as a,m as p,a as s,x as D,h as d,b as r,G as t,g as m,y as T,L as Qa,q,o as A,B as L,v as Xa}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Wa}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Za}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as _e}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function et(ca){let u,ve,f,y,ee,M,Ge,ae,Be,Pe,c,k,te,S,Ce,ne,Fe,be,N,O,He,Ue,we,V,ze,ye,Y,We,ke,$,Ve,G,Ye,Je,$e,g,x,ie,B,Ke,se,Qe,xe,R,re,_,oe,Xe,Ze,le,ea,aa,J,ta,na,pe,v,me,C,ia,sa,he,de,ra,oa,K,la,Re,P,j,ue,F,pa,fe,ma,je,H,Ie,b,I,ce,U,ha,ge,da,Ee,w,z,ua,Q,W,De;return M=new _e({}),S=new _e({}),B=new _e({}),F=new _e({}),H=new Za({props:{code:`from io import BytesIO

import torch

import PIL
import requests
from diffusers import RePaintPipeline, RePaintScheduler


def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")


img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"

# Load the original image and the mask as PIL images
original_image = download_image(img_url).resize((256, 256))
mask_image = download_image(mask_url).resize((256, 256))

# Load the RePaint scheduler and pipeline based on a pretrained DDPM model
scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256")
pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)
pipe = pipe.to("cuda")

generator = torch.Generator(device="cuda").manual_seed(0)
output = pipe(
    original_image=original_image,
    mask_image=mask_image,
    num_inference_steps=250,
    eta=0.0,
    jump_length=10,
    jump_n_sample=10,
    generator=generator,
)
inpainted_image = output.images[0]`,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(
    original_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>]`}}),U=new _e({}),z=new Wa({props:{name:"class diffusers.RePaintPipeline",anchor:"diffusers.RePaintPipeline",parameters:[{name:"unet",val:""},{name:"scheduler",val:""}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/repaint/pipeline_repaint.py#L73"}}),W=new Wa({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 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;
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<p><code>~pipelines.utils.ImagePipelineOutput</code> if <code>return_dict</code> is
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`,returnType:`
<p><a
  href="/docs/diffusers/v0.16.0/en/api/diffusion_pipeline#diffusers.ImagePipelineOutput"
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