diffusers/pr_12229/ko/_app/immutable/nodes/51.da8a2bbf.js

import{s as Fe,n as Ie,o as Be}from"../chunks/scheduler.23542ac5.js";import{S as Se,i as Ne,e as a,s as i,c as m,h as Ye,a as o,d as l,b as n,f as Re,g as d,j as p,k as ye,l as Ee,m as s,n as c,t as r,o as f,p as u}from"../chunks/index.9b1f405b.js";import{C as ze,H as Je,E as De}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.446fcef6.js";import{C as E}from"../chunks/CodeBlock.d59bc580.js";import{D as Le}from"../chunks/DocNotebookDropdown.68a629d2.js";function He(we){let b,z,N,D,h,L,M,H,Z,q,J,Te='<a href="https://huggingface.co/papers/2311.15127" rel="nofollow">Stable Video Diffusion (SVD)</a>은 입력 이미지에 맞춰 2~4초 분량의 고해상도(576x1024) 비디오를 생성할 수 있는 강력한 image-to-video 생성 모델입니다.',P,y,ve="이 가이드에서는 SVD를 사용하여 이미지에서 짧은 동영상을 생성하는 방법을 설명합니다.",K,w,Ve="시작하기 전에 다음 라이브러리가 설치되어 있는지 확인하세요:",A,T,O,v,Ue='이 모델에는 <a href="https://huggingface.co/stabilityai/stable-video-diffusion-img2vid" rel="nofollow">SVD</a>와 <a href="https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt" rel="nofollow">SVD-XT</a> 두 가지 종류가 있습니다. SVD 체크포인트는 14개의 프레임을 생성하도록 학습되었고, SVD-XT 체크포인트는 25개의 프레임을 생성하도록 파인튜닝되었습니다.',ee,V,Ce="이 가이드에서는 SVD-XT 체크포인트를 사용합니다.",te,U,le,g,ke='<div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">&quot;source image of a rocket&quot;</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/output_rocket.gif"/> <figcaption class="mt-2 text-center text-sm text-gray-500">&quot;generated video from source image&quot;</figcaption></div>',se,C,ie,k,_e='UNet을 <a href="../optimization/torch2.0#torchcompile">컴파일</a>하면 메모리 사용량이 살짝 증가하지만, 20~25%의 속도 향상을 얻을 수 있습니다.',ne,_,ae,W,oe,$,We="비디오 생성은 기본적으로 배치 크기가 큰 text-to-image 생성과 유사하게 ‘num_frames’를 한 번에 생성하기 때문에 메모리 사용량이 매우 높습니다. 메모리 사용량을 줄이기 위해 추론 속도와 메모리 사용량을 절충하는 여러 가지 옵션이 있습니다:",pe,j,$e="<li>모델 오프로링 활성화: 파이프라인의 각 구성 요소가 더 이상 필요하지 않을 때 CPU로 오프로드됩니다.</li> <li>Feed-forward chunking 활성화: feed-forward 레이어가 배치 크기가 큰 단일 feed-forward를 실행하는 대신 루프로 반복해서 실행됩니다.</li> <li><code>decode_chunk_size</code> 감소: VAE가 프레임들을 한꺼번에 디코딩하는 대신 chunk 단위로 디코딩합니다. <code>decode_chunk_size=1</code>을 설정하면 한 번에 한 프레임씩 디코딩하고 최소한의 메모리만 사용하지만(GPU 메모리에 따라 이 값을 조정하는 것이 좋습니다), 동영상에 약간의 깜박임이 발생할 수 있습니다.</li>",me,x,de,X,je="이러한 모든 방법들을 사용하면 메모리 사용량이 8GAM VRAM보다 적을 것입니다.",ce,G,re,Q,xe="Stable Diffusion Video는 또한 이미지 conditoning 외에도 micro-conditioning을 허용하므로 생성된 비디오를 더 잘 제어할 수 있습니다:",fe,R,Xe="<li><code>fps</code>: 생성된 비디오의 초당 프레임 수입니다.</li> <li><code>motion_bucket_id</code>: 생성된 동영상에 사용할 모션 버킷 아이디입니다. 생성된 동영상의 모션을 제어하는 데 사용할 수 있습니다. 모션 버킷 아이디를 늘리면 생성되는 동영상의 모션이 증가합니다.</li> <li><code>noise_aug_strength</code>: Conditioning 이미지에 추가되는 노이즈의 양입니다. 값이 클수록 비디오가 conditioning 이미지와 덜 유사해집니다. 이 값을 높이면 생성된 비디오의 움직임도 증가합니다.</li>",ue,F,Ge="예를 들어, 모션이 더 많은 동영상을 생성하려면 <code>motion_bucket_id</code> 및 <code>noise_aug_strength</code> micro-conditioning 파라미터를 사용합니다:",be,I,ge,B,Qe='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/output_rocket_with_conditions.gif"/>',he,S,Me,Y,Ze;return h=new ze({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),M=new Le({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/ko/svd.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/svd.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/svd.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/svd.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/svd.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/svd.ipynb"}]}}),Z=new Je({props:{title:"Stable Video Diffusion",local:"stable-video-diffusion",headingTag:"h1"}}),T=new E({props:{code:"IXBpcCUyMGluc3RhbGwlMjAtcSUyMC1VJTIwZGlmZnVzZXJzJTIwdHJhbnNmb3JtZXJzJTIwYWNjZWxlcmF0ZQ==",highlighted:"!pip install -q -U diffusers transformers accelerate",wrap:!1}}),U=new E({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> StableVideoDiffusionPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, export_to_video

pipe = StableVideoDiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-video-diffusion-img2vid-xt&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>
)
pipe.enable_model_cpu_offload()

<span class="hljs-comment"># Conditioning 이미지 불러오기</span>
image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png&quot;</span>)
image = image.resize((<span class="hljs-number">1024</span>, <span class="hljs-number">576</span>))

generator = torch.manual_seed(<span class="hljs-number">42</span>)
frames = pipe(image, decode_chunk_size=<span class="hljs-number">8</span>, generator=generator).frames[<span class="hljs-number">0</span>]

export_to_video(frames, <span class="hljs-string">&quot;generated.mp4&quot;</span>, fps=<span class="hljs-number">7</span>)`,wrap:!1}}),C=new Je({props:{title:"torch.compile",local:"torchcompile",headingTag:"h2"}}),_=new E({props:{code:"LSUyMHBpcGUuZW5hYmxlX21vZGVsX2NwdV9vZmZsb2FkKCklMEElMkIlMjBwaXBlLnRvKCUyMmN1ZGElMjIpJTBBJTJCJTIwcGlwZS51bmV0JTIwJTNEJTIwdG9yY2guY29tcGlsZShwaXBlLnVuZXQlMkMlMjBtb2RlJTNEJTIycmVkdWNlLW92ZXJoZWFkJTIyJTJDJTIwZnVsbGdyYXBoJTNEVHJ1ZSk=",highlighted:`<span class="hljs-deletion">- pipe.enable_model_cpu_offload()</span>
<span class="hljs-addition">+ pipe.to(&quot;cuda&quot;)</span>
<span class="hljs-addition">+ pipe.unet = torch.compile(pipe.unet, mode=&quot;reduce-overhead&quot;, fullgraph=True)</span>`,wrap:!1}}),W=new Je({props:{title:"메모리 사용량 줄이기",local:"메모리-사용량-줄이기",headingTag:"h2"}}),x=new E({props:{code:"LSUyMHBpcGUuZW5hYmxlX21vZGVsX2NwdV9vZmZsb2FkKCklMEEtJTIwZnJhbWVzJTIwJTNEJTIwcGlwZShpbWFnZSUyQyUyMGRlY29kZV9jaHVua19zaXplJTNEOCUyQyUyMGdlbmVyYXRvciUzRGdlbmVyYXRvcikuZnJhbWVzJTVCMCU1RCUwQSUyQiUyMHBpcGUuZW5hYmxlX21vZGVsX2NwdV9vZmZsb2FkKCklMEElMkIlMjBwaXBlLnVuZXQuZW5hYmxlX2ZvcndhcmRfY2h1bmtpbmcoKSUwQSUyQiUyMGZyYW1lcyUyMCUzRCUyMHBpcGUoaW1hZ2UlMkMlMjBkZWNvZGVfY2h1bmtfc2l6ZSUzRDIlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IlMkMlMjBudW1fZnJhbWVzJTNEMjUpLmZyYW1lcyU1QjAlNUQ=",highlighted:`<span class="hljs-deletion">- pipe.enable_model_cpu_offload()</span>
<span class="hljs-deletion">- frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]</span>
<span class="hljs-addition">+ pipe.enable_model_cpu_offload()</span>
<span class="hljs-addition">+ pipe.unet.enable_forward_chunking()</span>
<span class="hljs-addition">+ frames = pipe(image, decode_chunk_size=2, generator=generator, num_frames=25).frames[0]</span>`,wrap:!1}}),G=new Je({props:{title:"Micro-conditioning",local:"micro-conditioning",headingTag:"h2"}}),I=new E({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> StableVideoDiffusionPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, export_to_video

pipe = StableVideoDiffusionPipeline.from_pretrained(
  <span class="hljs-string">&quot;stabilityai/stable-video-diffusion-img2vid-xt&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>
)
pipe.enable_model_cpu_offload()

<span class="hljs-comment"># Conditioning 이미지 불러오기</span>
image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png&quot;</span>)
image = image.resize((<span class="hljs-number">1024</span>, <span class="hljs-number">576</span>))

generator = torch.manual_seed(<span class="hljs-number">42</span>)
frames = pipe(image, decode_chunk_size=<span class="hljs-number">8</span>, generator=generator, motion_bucket_id=<span class="hljs-number">180</span>, noise_aug_strength=<span class="hljs-number">0.1</span>).frames[<span class="hljs-number">0</span>]
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