diffusers/pr_13489/en/_app/immutable/nodes/346.0c5a07ec.js

import{s as as,n as ns,o as is}from"../chunks/scheduler.53228c21.js";import{S as ps,i as os,e as i,s as a,c as r,h as rs,a as p,d as t,b as n,f as ls,g as m,j as o,k as A,l as ms,m as l,n as c,t as u,o as h,p as f}from"../chunks/index.100fac89.js";import{C as cs}from"../chunks/CopyLLMTxtMenu.8aee8eb3.js";import{C as y}from"../chunks/CodeBlock.d30a6509.js";import{D as us}from"../chunks/DocNotebookDropdown.74a16910.js";import{H as Xe,E as hs}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.37cdb888.js";function fs(Se){let d,ee,K,se,J,te,T,le,j,ae,U,Re="Shap-E is a conditional model for generating 3D assets which could be used for video game development, interior design, and architecture. It is trained on a large dataset of 3D assets, and post-processed to render more views of each object and produce 16K instead of 4K point clouds. The Shap-E model is trained in two steps:",ne,Z,Ve="<li>an encoder accepts the point clouds and rendered views of a 3D asset and outputs the parameters of implicit functions that represent the asset</li> <li>a diffusion model is trained on the latents produced by the encoder to generate either neural radiance fields (NeRFs) or a textured 3D mesh, making it easier to render and use the 3D asset in downstream applications</li>",ie,_,Ne="This guide will show you how to use Shap-E to start generating your own 3D assets!",pe,I,Ye="Before you begin, make sure you have the following libraries installed:",oe,W,re,v,me,C,qe='To generate a gif of a 3D object, pass a text prompt to the <a href="/docs/diffusers/pr_13489/en/api/pipelines/shap_e#diffusers.ShapEPipeline">ShapEPipeline</a>. The pipeline generates a list of image frames which are used to create the 3D object.',ce,$,ue,B,Qe='이제 <a href="/docs/diffusers/pr_13489/en/api/utilities#diffusers.utils.export_to_gif">export_to_gif()</a> 함수를 사용해 이미지 프레임 리스트를 3D 오브젝트의 gif로 변환합니다.',he,k,fe,M,ze='<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,G,ye,x,Fe='To generate a 3D object from another image, use the <a href="/docs/diffusers/pr_13489/en/api/pipelines/shap_e#diffusers.ShapEImg2ImgPipeline">ShapEImg2ImgPipeline</a>. You can use an existing image or generate an entirely new one. Let’s use the <a href="../api/pipelines/kandinsky">Kandinsky 2.1</a> model to generate a new image.',Me,E,ge,X,He='Pass the cheeseburger to the <a href="/docs/diffusers/pr_13489/en/api/pipelines/shap_e#diffusers.ShapEImg2ImgPipeline">ShapEImg2ImgPipeline</a> to generate a 3D representation of it.',be,S,we,g,De='<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>',Je,R,Te,V,Le='Shap-E is a flexible model that can also generate textured mesh outputs to be rendered for downstream applications. In this example, you’ll convert the output into a <code>glb</code> file because the 🤗 Datasets library supports mesh visualization of <code>glb</code> files which can be rendered by the <a href="https://huggingface.co/docs/hub/datasets-viewer#dataset-preview" rel="nofollow">Dataset viewer</a>.',je,N,Pe='You can generate mesh outputs for both the <a href="/docs/diffusers/pr_13489/en/api/pipelines/shap_e#diffusers.ShapEPipeline">ShapEPipeline</a> and <a href="/docs/diffusers/pr_13489/en/api/pipelines/shap_e#diffusers.ShapEImg2ImgPipeline">ShapEImg2ImgPipeline</a> by specifying the <code>output_type</code> parameter as <code>&quot;mesh&quot;</code>:',Ue,Y,Ze,q,Ae="Use the <code>export_to_ply()</code> function to save the mesh output as a <code>ply</code> file:",_e,b,Ke="<p>You can optionally save the mesh output as an <code>obj</code> file with the <code>export_to_obj()</code> function. The ability to save the mesh output in a variety of formats makes it more flexible for downstream usage!</p>",Ie,Q,We,z,Oe="Then you can convert the <code>ply</code> file to a <code>glb</code> file with the trimesh library:",ve,F,Ce,H,es="By default, the mesh output is focused from the bottom viewpoint but you can change the default viewpoint by applying a rotation transform:",$e,D,Be,L,ss="Upload the mesh file to your dataset repository to visualize it with the Dataset viewer!",ke,w,ts='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/3D-cake.gif"/>',Ge,P,xe,O,Ee;return J=new cs({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new us({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/shap-e.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/shap-e.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/shap-e.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/shap-e.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/shap-e.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/shap-e.ipynb"}]}}),j=new Xe({props:{title:"Shap-E",local:"shap-e",headingTag:"h1"}}),W=new y({props:{code:"JTIzJTIwdW5jb21tZW50JTIwdG8lMjBpbnN0YWxsJTIwdGhlJTIwbmVjZXNzYXJ5JTIwbGlicmFyaWVzJTIwaW4lMjBDb2xhYiUwQSUyMyFwaXAlMjBpbnN0YWxsJTIwLXElMjBkaWZmdXNlcnMlMjB0cmFuc2Zvcm1lcnMlMjBhY2NlbGVyYXRlJTIwdHJpbWVzaA==",highlighted:`<span class="hljs-comment"># uncomment to install the necessary libraries in Colab</span>
<span class="hljs-comment">#!pip install -q diffusers transformers accelerate trimesh</span>`,wrap:!1}}),v=new Xe({props:{title:"Text-to-3D",local:"text-to-3d",headingTag:"h2"}}),$=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}}),k=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}}),G=new Xe({props:{title:"Image-to-3D",local:"image-to-3d",headingTag:"h2"}}),E=new y({props:{code:"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",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}}),S=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}}),R=new Xe({props:{title:"Generate mesh",local:"generate-mesh",headingTag:"h2"}}),Y=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}}),Q=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}}),D=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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