import{s as Oe,n as Pe,o as Ae}from"../chunks/scheduler.bdbef820.js";import{S as Ke,i as qe,g as J,s as a,r as i,A as es,h as p,f as l,c as n,j as Xe,u as m,x as T,k as ve,y as ss,a as t,v as o,d as w,t as c,w as r}from"../chunks/index.33f81d56.js";import{C as u}from"../chunks/CodeBlock.3bad7fc9.js";import{D as ls}from"../chunks/DocNotebookDropdown.339158fb.js";import{H as ts,E as as}from"../chunks/index.e01163b6.js";function ns(De){let I,P,X,A,b,K,h,q,f,xe="Image-to-Image 작업은 애플리케이션이 이미지를 입력받아 또 다른 이미지를 출력하는 작업입니다. 여기에는 이미지 향상(초고해상도, 저조도 향상, 빗줄기 제거 등), 이미지 복원 등 다양한 하위 작업이 포함됩니다.",ee,d,Ne="이 가이드에서는 다음을 수행하는 방법을 보여줍니다.",se,y,Re="<li>초고해상도 작업을 위한 image-to-image 파이프라인 사용,</li> <li>파이프라인 없이 동일한 작업을 위한 image-to-image 모델 실행</li>",le,U,We="이 가이드가 발표된 시점에서는, <code>image-to-image</code> 파이프라인은 초고해상도 작업만 지원한다는 점을 유의하세요.",te,j,Ee="필요한 라이브러리를 설치하는 것부터 시작하겠습니다.",ae,$,ne,C,_e='이제 <a href="https://huggingface.co/caidas/swin2SR-lightweight-x2-64" rel="nofollow">Swin2SR 모델</a>을 사용하여 파이프라인을 초기화할 수 있습니다. 그런 다음 이미지와 함께 호출하여 파이프라인으로 추론할 수 있습니다. 현재 이 파이프라인에서는 <a href="https://huggingface.co/caidas/swin2SR-lightweight-x2-64" rel="nofollow">Swin2SR 모델</a>만 지원됩니다.',Je,V,pe,k,Ze="이제 이미지를 불러와 봅시다.",Te,v,ie,D,me,M,Qe='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/cat.jpg" alt="Photo of a cat"/>',oe,x,Be="이제 파이프라인으로 추론을 수행할 수 있습니다. 고양이 이미지의 업스케일된 버전을 얻을 수 있습니다.",we,N,ce,R,re,W,Ge="파이프라인 없이 직접 추론을 수행하려면 Transformers의 <code>Swin2SRForImageSuperResolution</code> 및 <code>Swin2SRImageProcessor</code> 클래스를 사용할 수 있습니다. 이를 위해 동일한 모델 체크포인트를 사용합니다. 모델과 프로세서를 초기화해 보겠습니다.",ue,E,Ie,_,ze="<code>pipeline</code> 우리가 직접 수행해야 하는 전처리와 후처리 단계를 추상화하므로, 이미지를 전처리해 보겠습니다. 이미지를 프로세서에 전달한 다음 픽셀값을 GPU로 이동시키겠습니다.",Me,Z,ge,Q,Se="이제 픽셀값을 모델에 전달하여 이미지를 추론할 수 있습니다.",be,B,he,G,He="출력은 아래와 같은 <code>ImageSuperResolutionOutput</code> 유형의 객체입니다 👇",fe,z,de,S,Ye="<code>reconstruction</code>를 가져와 시각화를 위해 후처리해야 합니다. 어떻게 생겼는지 살펴봅시다.",ye,H,Ue,Y,Le="출력 텐서의 차원을 축소하고 0번째 축을 제거한 다음, 값을 클리핑하고 NumPy 부동소수점 배열로 변환해야 합니다. 그런 다음 [1072, 880] 모양을 갖도록 축을 재정렬하고 마지막으로 출력을 0과 255 사이의 값을 갖도록 되돌립니다.",je,L,$e,g,Fe='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/cat_upscaled.png" alt="Upscaled photo of a cat"/>',Ce,F,Ve,O,ke;return b=new ts({props:{title:"Image-to-Image 작업 가이드",local:"image-to-image-task-guide",headingTag:"h1"}}),h=new ls({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/image_to_image.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/image_to_image.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/image_to_image.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/image_to_image.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/image_to_image.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/image_to_image.ipynb"}]}}),$=new u({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycw==",highlighted:"pip install transformers",wrap:!1}}),V=new u({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBZGV2aWNlJTIwJTNEJTIwdG9yY2guZGV2aWNlKCdjdWRhJyUyMGlmJTIwdG9yY2guY3VkYS5pc19hdmFpbGFibGUoKSUyMGVsc2UlMjAnY3B1JyklMEFwaXBlJTIwJTNEJTIwcGlwZWxpbmUodGFzayUzRCUyMmltYWdlLXRvLWltYWdlJTIyJTJDJTIwbW9kZWwlM0QlMjJjYWlkYXMlMkZzd2luMlNSLWxpZ2h0d2VpZ2h0LXgyLTY0JTIyJTJDJTIwZGV2aWNlJTNEZGV2aWNlKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

device = torch.device(<span class="hljs-string">&#x27;cuda&#x27;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&#x27;cpu&#x27;</span>)
pipe = pipeline(task=<span class="hljs-string">&quot;image-to-image&quot;</span>, model=<span class="hljs-string">&quot;caidas/swin2SR-lightweight-x2-64&quot;</span>, device=device)`,wrap:!1}}),v=new u({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZodWdnaW5nZmFjZSUyRmRvY3VtZW50YXRpb24taW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZ0cmFuc2Zvcm1lcnMlMkZ0YXNrcyUyRmNhdC5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEElMEFwcmludChpbWFnZS5zaXplKQ==",highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> requests

url = <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/cat.jpg&quot;</span>
image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-built_in">print</span>(image.size)`,wrap:!1}}),D=new u({props:{code:"JTIzJTIwKDUzMiUyQyUyMDQzMik=",highlighted:'<span class="hljs-comment"># (532, 432)</span>',wrap:!1}}),N=new u({props:{code:"dXBzY2FsZWQlMjAlM0QlMjBwaXBlKGltYWdlKSUwQXByaW50KHVwc2NhbGVkLnNpemUp",highlighted:`upscaled = pipe(image)
<span class="hljs-built_in">print</span>(upscaled.size)`,wrap:!1}}),R=new u({props:{code:"JTIzJTIwKDEwNzIlMkMlMjA4ODAp",highlighted:'<span class="hljs-comment"># (1072, 880)</span>',wrap:!1}}),E=new u({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFN3aW4yU1JGb3JJbWFnZVN1cGVyUmVzb2x1dGlvbiUyQyUyMFN3aW4yU1JJbWFnZVByb2Nlc3NvciUyMCUwQSUwQW1vZGVsJTIwJTNEJTIwU3dpbjJTUkZvckltYWdlU3VwZXJSZXNvbHV0aW9uLmZyb21fcHJldHJhaW5lZCglMjJjYWlkYXMlMkZzd2luMlNSLWxpZ2h0d2VpZ2h0LXgyLTY0JTIyKS50byhkZXZpY2UpJTBBcHJvY2Vzc29yJTIwJTNEJTIwU3dpbjJTUkltYWdlUHJvY2Vzc29yKCUyMmNhaWRhcyUyRnN3aW4yU1ItbGlnaHR3ZWlnaHQteDItNjQlMjIp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Swin2SRForImageSuperResolution, Swin2SRImageProcessor 

model = Swin2SRForImageSuperResolution.from_pretrained(<span class="hljs-string">&quot;caidas/swin2SR-lightweight-x2-64&quot;</span>).to(device)
processor = Swin2SRImageProcessor(<span class="hljs-string">&quot;caidas/swin2SR-lightweight-x2-64&quot;</span>)`,wrap:!1}}),Z=new u({props:{code:"cGl4ZWxfdmFsdWVzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMikucGl4ZWxfdmFsdWVzJTBBcHJpbnQocGl4ZWxfdmFsdWVzLnNoYXBlKSUwQSUwQXBpeGVsX3ZhbHVlcyUyMCUzRCUyMHBpeGVsX3ZhbHVlcy50byhkZXZpY2Up",highlighted:`pixel_values = processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values
<span class="hljs-built_in">print</span>(pixel_values.shape)

pixel_values = pixel_values.to(device)`,wrap:!1}}),B=new u({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwb3V0cHV0cyUyMCUzRCUyMG1vZGVsKHBpeGVsX3ZhbHVlcyk=",highlighted:`<span class="hljs-keyword">import</span> torch

<span class="hljs-keyword">with</span> torch.no_grad():
  outputs = model(pixel_values)`,wrap:!1}}),z=new u({props:{code:"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",highlighted:`(<span class="hljs-name">loss=None</span>, reconstruction=tensor([[[[<span class="hljs-name">0.8270</span>, <span class="hljs-number">0.8269</span>, <span class="hljs-number">0.8275</span>,  ..., <span class="hljs-number">0.7463</span>, <span class="hljs-number">0.7446</span>, <span class="hljs-number">0.7453</span>],
          [<span class="hljs-name">0.8287</span>, <span class="hljs-number">0.8278</span>, <span class="hljs-number">0.8283</span>,  ..., <span class="hljs-number">0.7451</span>, <span class="hljs-number">0.7448</span>, <span class="hljs-number">0.7457</span>],
          [<span class="hljs-name">0.8280</span>, <span class="hljs-number">0.8273</span>, <span class="hljs-number">0.8269</span>,  ..., <span class="hljs-number">0.7447</span>, <span class="hljs-number">0.7446</span>, <span class="hljs-number">0.7452</span>],
          ...,
          [<span class="hljs-name">0.5923</span>, <span class="hljs-number">0.5933</span>, <span class="hljs-number">0.5924</span>,  ..., <span class="hljs-number">0.0697</span>, <span class="hljs-number">0.0695</span>, <span class="hljs-number">0.0706</span>],
          [<span class="hljs-name">0.5926</span>, <span class="hljs-number">0.5932</span>, <span class="hljs-number">0.5926</span>,  ..., <span class="hljs-number">0.0673</span>, <span class="hljs-number">0.0687</span>, <span class="hljs-number">0.0705</span>],
          [<span class="hljs-name">0.5927</span>, <span class="hljs-number">0.5914</span>, <span class="hljs-number">0.5922</span>,  ..., <span class="hljs-number">0.0664</span>, <span class="hljs-number">0.0694</span>, <span class="hljs-number">0.0718</span>]]]],
       device=<span class="hljs-symbol">&#x27;cuda:0</span>&#x27;), hidden_states=None, attentions=None)`,wrap:!1}}),H=new u({props:{code:"b3V0cHV0cy5yZWNvbnN0cnVjdGlvbi5kYXRhLnNoYXBlJTBBJTIzJTIwdG9yY2guU2l6ZSglNUIxJTJDJTIwMyUyQyUyMDg4MCUyQyUyMDEwNzIlNUQp",highlighted:`outputs.reconstruction.data.shape
<span class="hljs-comment"># torch.Size([1, 3, 880, 1072])</span>`,wrap:!1}}),L=new u({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-comment"># 크기를 줄이고, CPU로 이동하고, 값을 클리핑</span>
output = outputs.reconstruction.data.squeeze().cpu().clamp_(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>).numpy()
<span class="hljs-comment"># 축을 재정렬</span>
output = np.moveaxis(output, source=<span class="hljs-number">0</span>, destination=-<span class="hljs-number">1</span>)
<span class="hljs-comment"># 값을 픽셀값 범위로 되돌리기</span>
output = (output * <span class="hljs-number">255.0</span>).<span class="hljs-built_in">round</span>().astype(np.uint8)
Image.fromarray(output)`,wrap:!1}}),F=new 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