diffusers/main/ko/_app/immutable/nodes/49.9abf8148.js

import{s as sl,o as tl,n as al}from"../chunks/scheduler.94020406.js";import{S as nl,i as pl,g as p,s as a,r as c,E as il,h as i,f as s,c as n,j as ll,u as r,x as o,k as ee,y as ol,a as t,v as M,d as u,t as d,w as f}from"../chunks/index.a08c8d92.js";import{T as ml}from"../chunks/Tip.3b0aeee8.js";import{C as y}from"../chunks/CodeBlock.b23cf525.js";import{D as cl}from"../chunks/DocNotebookDropdown.d8a25975.js";import{H as Qe,E as rl}from"../chunks/EditOnGithub.b1bceb47.js";function Ml(le){let m,T="선택적으로 <code>export_to_obj()</code> 함수를 사용하여 메시 출력을 <code>obj</code> 파일로 저장할 수 있습니다. 다양한 형식으로 메시 출력을 저장할 수 있어 다운스트림에서 더욱 유연하게 사용할 수 있습니다!";return{c(){m=p("p"),m.innerHTML=T},l(h){m=i(h,"P",{"data-svelte-h":!0}),o(m)!=="svelte-1t83wc3"&&(m.innerHTML=T)},m(h,O){t(h,m,O)},p:al,d(h){h&&s(m)}}}function ul(le){let m,T,h,O,w,se,j,te,Z,Ee="Shap-E는 비디오 게임 개발, 인테리어 디자인, 건축에 사용할 수 있는 3D 에셋을 생성하기 위한 conditional 모델입니다. 대규모 3D 에셋 데이터셋을 학습되었고, 각 오브젝트의 더 많은 뷰를 렌더링하고 4K point cloud 대신 16K를 생성하도록 후처리합니다. Shap-E 모델은 두 단계로 학습됩니다:",ae,I,xe="<li>인코더가 3D 에셋의 포인트 클라우드와 렌더링된 뷰를 받아들이고 에셋을 나타내는 implicit functions의 파라미터를 출력합니다.</li> <li>인코더가 생성한 latents를 바탕으로 diffusion 모델을 훈련하여 neural radiance fields(NeRF) 또는 textured 3D 메시를 생성하여 다운스트림 애플리케이션에서 3D 에셋을 더 쉽게 렌더링하고 사용할 수 있도록 합니다.</li>",ne,$,Xe="이 가이드에서는 Shap-E를 사용하여 나만의 3D 에셋을 생성하는 방법을 보입니다!",pe,_,Se="시작하기 전에 다음 라이브러리가 설치되어 있는지 확인하세요:",ie,W,oe,C,me,k,Ne="3D 객체의 gif를 생성하려면 텍스트 프롬프트를 <code>ShapEPipeline</code>에 전달합니다. 파이프라인은 3D 객체를 생성하는 데 사용되는 이미지 프레임 리스트를 생성합니다.",ce,G,re,B,Ve="이제 <code>export_to_gif()</code> 함수를 사용하여 이미지 프레임 리스트를 3D 객체의 gif로 변환합니다.",Me,v,ue,g,qe='<div><img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/firecracker_out.gif"/> <figcaption class="mt-2 text-center text-sm text-gray-500">prompt = &quot;A firecracker&quot;</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/cake_out.gif"/> <figcaption class="mt-2 text-center text-sm text-gray-500">prompt = &quot;A birthday cupcake&quot;</figcaption></div>',de,R,fe,Q,Ye='다른 이미지로부터 3D 개체를 생성하려면 <code>ShapEImg2ImgPipeline</code>을 사용합니다. 기존 이미지를 사용하거나 완전히 새로운 이미지를 생성할 수 있습니다. <a href="../api/pipelines/kandinsky">Kandinsky 2.1</a> 모델을 사용하여 새 이미지를 생성해 보겠습니다.',he,E,ye,x,ze="치즈버거를 <code>ShapEImg2ImgPipeline</code>에 전달하여 3D representation을 생성합니다.",ge,X,Je,J,He='<div><img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/burger_in.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">cheeseburger</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/burger_out.gif"/> <figcaption class="mt-2 text-center text-sm text-gray-500">3D cheeseburger</figcaption></div>',Ue,S,be,N,Fe='Shap-E는 다운스트림 애플리케이션에 렌더링할 textured 메시 출력을 생성할 수도 있는 유연한 모델입니다. 이 예제에서는 🤗 Datasets 라이브러리에서 <a href="https://huggingface.co/docs/hub/datasets-viewer#dataset-preview" rel="nofollow">Dataset viewer</a>를 사용해 메시 시각화를 지원하는 <code>glb</code> 파일로 변환합니다.',Te,V,De="<code>output_type</code> 매개변수를 <code>&quot;mesh&quot;</code>로 지정함으로써 <code>ShapEPipeline</code>과 <code>ShapEImg2ImgPipeline</code> 모두에 대한 메시 출력을 생성할 수 있습니다:",we,q,je,Y,Le="메시 출력을 <code>ply</code> 파일로 저장하려면 <code>export_to_ply()</code> 함수를 사용합니다:",Ze,U,Ie,z,$e,H,Ae="그 다음 trimesh 라이브러리를 사용하여 <code>ply</code> 파일을 <code>glb</code> 파일로 변환할 수 있습니다:",_e,F,We,D,Pe="기본적으로 메시 출력은 아래쪽 시점에 초점이 맞춰져 있지만 회전 변환을 적용하여 기본 시점을 변경할 수 있습니다:",Ce,L,ke,A,Oe="메시 파일을 데이터셋 레포지토리에 업로드해 Dataset viewer로 시각화하세요!",Ge,b,Ke='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/3D-cake.gif"/>',Be,P,ve,K,Re;return w=new Qe({props:{title:"Shap-E",local:"shap-e",headingTag:"h1"}}),j=new cl({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/shap-e.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/shap-e.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/shap-e.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/shap-e.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/shap-e.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/shap-e.ipynb"}]}}),W=new y({props:{code:"JTIzJTIwQ29sYWIlRUMlOTclOTAlRUMlODQlOUMlMjAlRUQlOTUlODQlRUMlOUElOTQlRUQlOTUlOUMlMjAlRUIlOUQlQkMlRUMlOUQlQjQlRUIlQjglOEMlRUIlOUYlQUMlRUIlQTYlQUMlRUIlQTUlQkMlMjAlRUMlODQlQTQlRUMlQjklOTglRUQlOTUlOTglRUElQjglQjAlMjAlRUMlOUMlODQlRUQlOTUlQjQlMjAlRUMlQTMlQkMlRUMlODQlOUQlRUMlOUQlODQlMjAlRUMlQTAlOUMlRUMlOTklQjglRUQlOTUlOTglRUMlODQlQjglRUMlOUElOTQlMEElMjMhcGlwJTIwaW5zdGFsbCUyMC1xJTIwZGlmZnVzZXJzJTIwdHJhbnNmb3JtZXJzJTIwYWNjZWxlcmF0ZSUyMHRyaW1lc2g=",highlighted:`<span class="hljs-comment"># Colab에서 필요한 라이브러리를 설치하기 위해 주석을 제외하세요</span>
<span class="hljs-comment">#!pip install -q diffusers transformers accelerate trimesh</span>`,wrap:!1}}),C=new Qe({props:{title:"Text-to-3D",local:"text-to-3d",headingTag:"h2"}}),G=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ShapEPipeline

device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>)

pipe = ShapEPipeline.from_pretrained(<span class="hljs-string">&quot;openai/shap-e&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipe = pipe.to(device)

guidance_scale = <span class="hljs-number">15.0</span>
prompt = [<span class="hljs-string">&quot;A firecracker&quot;</span>, <span class="hljs-string">&quot;A birthday cupcake&quot;</span>]

images = pipe(
    prompt,
    guidance_scale=guidance_scale,
    num_inference_steps=<span class="hljs-number">64</span>,
    frame_size=<span class="hljs-number">256</span>,
).images`,wrap:!1}}),v=new y({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGV4cG9ydF90b19naWYlMEElMEFleHBvcnRfdG9fZ2lmKGltYWdlcyU1QjAlNUQlMkMlMjAlMjJmaXJlY3JhY2tlcl8zZC5naWYlMjIpJTBBZXhwb3J0X3RvX2dpZihpbWFnZXMlNUIxJTVEJTJDJTIwJTIyY2FrZV8zZC5naWYlMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_gif

export_to_gif(images[<span class="hljs-number">0</span>], <span class="hljs-string">&quot;firecracker_3d.gif&quot;</span>)
export_to_gif(images[<span class="hljs-number">1</span>], <span class="hljs-string">&quot;cake_3d.gif&quot;</span>)`,wrap:!1}}),R=new Qe({props:{title:"Image-to-3D",local:"image-to-3d",headingTag:"h2"}}),E=new y({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFwcmlvcl9waXBlbGluZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJrYW5kaW5za3ktY29tbXVuaXR5JTJGa2FuZGluc2t5LTItMS1wcmlvciUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiUyQyUyMHVzZV9zYWZldGVuc29ycyUzRFRydWUpLnRvKCUyMmN1ZGElMjIpJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIya2FuZGluc2t5LWNvbW11bml0eSUyRmthbmRpbnNreS0yLTElMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKS50byglMjJjdWRhJTIyKSUwQSUwQXByb21wdCUyMCUzRCUyMCUyMkElMjBjaGVlc2VidXJnZXIlMkMlMjB3aGl0ZSUyMGJhY2tncm91bmQlMjIlMEElMEFpbWFnZV9lbWJlZHMlMkMlMjBuZWdhdGl2ZV9pbWFnZV9lbWJlZHMlMjAlM0QlMjBwcmlvcl9waXBlbGluZShwcm9tcHQlMkMlMjBndWlkYW5jZV9zY2FsZSUzRDEuMCkudG9fdHVwbGUoKSUwQWltYWdlJTIwJTNEJTIwcGlwZWxpbmUoJTBBJTIwJTIwJTIwJTIwcHJvbXB0JTJDJTBBJTIwJTIwJTIwJTIwaW1hZ2VfZW1iZWRzJTNEaW1hZ2VfZW1iZWRzJTJDJTBBJTIwJTIwJTIwJTIwbmVnYXRpdmVfaW1hZ2VfZW1iZWRzJTNEbmVnYXRpdmVfaW1hZ2VfZW1iZWRzJTJDJTBBKS5pbWFnZXMlNUIwJTVEJTBBJTBBaW1hZ2Uuc2F2ZSglMjJidXJnZXIucG5nJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

prior_pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A cheeseburger, white background&quot;</span>

image_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=<span class="hljs-number">1.0</span>).to_tuple()
image = pipeline(
    prompt,
    image_embeds=image_embeds,
    negative_image_embeds=negative_image_embeds,
).images[<span class="hljs-number">0</span>]

image.save(<span class="hljs-string">&quot;burger.png&quot;</span>)`,wrap:!1}}),X=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ShapEImg2ImgPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_gif

pipe = ShapEImg2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;openai/shap-e-img2img&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

guidance_scale = <span class="hljs-number">3.0</span>
image = Image.<span class="hljs-built_in">open</span>(<span class="hljs-string">&quot;burger.png&quot;</span>).resize((<span class="hljs-number">256</span>, <span class="hljs-number">256</span>))

images = pipe(
    image,
    guidance_scale=guidance_scale,
    num_inference_steps=<span class="hljs-number">64</span>,
    frame_size=<span class="hljs-number">256</span>,
).images

gif_path = export_to_gif(images[<span class="hljs-number">0</span>], <span class="hljs-string">&quot;burger_3d.gif&quot;</span>)`,wrap:!1}}),S=new Qe({props:{title:"메시 생성하기",local:"메시-생성하기",headingTag:"h2"}}),q=new y({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ShapEPipeline

device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>)

pipe = ShapEPipeline.from_pretrained(<span class="hljs-string">&quot;openai/shap-e&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipe = pipe.to(device)

guidance_scale = <span class="hljs-number">15.0</span>
prompt = <span class="hljs-string">&quot;A birthday cupcake&quot;</span>

images = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=<span class="hljs-number">64</span>, frame_size=<span class="hljs-number">256</span>, output_type=<span class="hljs-string">&quot;mesh&quot;</span>).images`,wrap:!1}}),U=new ml({props:{$$slots:{default:[Ml]},$$scope:{ctx:le}}}),z=new y({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGV4cG9ydF90b19wbHklMEElMEFwbHlfcGF0aCUyMCUzRCUyMGV4cG9ydF90b19wbHkoaW1hZ2VzJTVCMCU1RCUyQyUyMCUyMjNkX2Nha2UucGx5JTIyKSUwQXByaW50KGYlMjJTYXZlZCUyMHRvJTIwZm9sZGVyJTNBJTIwJTdCcGx5X3BhdGglN0QlMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_ply

ply_path = export_to_ply(images[<span class="hljs-number">0</span>], <span class="hljs-string">&quot;3d_cake.ply&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Saved to folder: <span class="hljs-subst">{ply_path}</span>&quot;</span>)`,wrap:!1}}),F=new y({props:{code:"aW1wb3J0JTIwdHJpbWVzaCUwQSUwQW1lc2glMjAlM0QlMjB0cmltZXNoLmxvYWQoJTIyM2RfY2FrZS5wbHklMjIpJTBBbWVzaF9leHBvcnQlMjAlM0QlMjBtZXNoLmV4cG9ydCglMjIzZF9jYWtlLmdsYiUyMiUyQyUyMGZpbGVfdHlwZSUzRCUyMmdsYiUyMik=",highlighted:`<span class="hljs-keyword">import</span> trimesh

mesh = trimesh.load(<span class="hljs-string">&quot;3d_cake.ply&quot;</span>)
mesh_export = mesh.export(<span class="hljs-string">&quot;3d_cake.glb&quot;</span>, file_type=<span class="hljs-string">&quot;glb&quot;</span>)`,wrap:!1}}),L=new y({props:{code:"aW1wb3J0JTIwdHJpbWVzaCUwQWltcG9ydCUyMG51bXB5JTIwYXMlMjBucCUwQSUwQW1lc2glMjAlM0QlMjB0cmltZXNoLmxvYWQoJTIyM2RfY2FrZS5wbHklMjIpJTBBcm90JTIwJTNEJTIwdHJpbWVzaC50cmFuc2Zvcm1hdGlvbnMucm90YXRpb25fbWF0cml4KC1ucC5waSUyMCUyRiUyMDIlMkMlMjAlNUIxJTJDJTIwMCUyQyUyMDAlNUQpJTBBbWVzaCUyMCUzRCUyMG1lc2guYXBwbHlfdHJhbnNmb3JtKHJvdCklMEFtZXNoX2V4cG9ydCUyMCUzRCUyMG1lc2guZXhwb3J0KCUyMjNkX2Nha2UuZ2xiJTIyJTJDJTIwZmlsZV90eXBlJTNEJTIyZ2xiJTIyKQ==",highlighted:`<span class="hljs-keyword">import</span> trimesh
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

mesh = trimesh.load(<span class="hljs-string">&quot;3d_cake.ply&quot;</span>)
rot = trimesh.transformations.rotation_matrix(-np.pi / <span class="hljs-number">2</span>, [<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>])
mesh = mesh.apply_transform(rot)
mesh_export = mesh.export(<span class="hljs-string">&quot;3d_cake.glb&quot;</span>, file_type=<span class="hljs-string">&quot;glb&quot;</span>)`,wrap:!1}}),P=new 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dl='{"title":"Shap-E","local":"shap-e","sections":[{"title":"Text-to-3D","local":"text-to-3d","sections":[],"depth":2},{"title":"Image-to-3D","local":"image-to-3d","sections":[],"depth":2},{"title":"메시 생성하기","local":"메시-생성하기","sections":[],"depth":2}],"depth":1}';function fl(le){return tl(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Tl extends nl{constructor(m){super(),pl(this,m,fl,ul,sl,{})}}export{Tl as component};