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<meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{&quot;local&quot;:&quot;unconditional&quot;,&quot;sections&quot;:[{&quot;local&quot;:&quot;&quot;,&quot;title&quot;:&quot;모델을 허브에 업로드하기&quot;},{&quot;local&quot;:&quot;&quot;,&quot;title&quot;:&quot;체크포인트 저장하고 불러오기&quot;},{&quot;local&quot;:&quot;&quot;,&quot;sections&quot;:[{&quot;local&quot;:&quot;gpu&quot;,&quot;title&quot;:&quot;여러개의 GPU로 훈련하기&quot;}],&quot;title&quot;:&quot;파인튜닝&quot;}],&quot;title&quot;:&quot;Unconditional 이미지 생성&quot;}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="unconditional" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#unconditional"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Unconditional 이미지 생성
</span></h1>
<p>unconditional 이미지 생성은 text-to-image 또는 image-to-image 모델과 달리 텍스트나 이미지에 대한 조건이 없이 학습 데이터 분포와 유사한 이미지만을 생성합니다.</p>
<iframe src="https://stevhliu-ddpm-butterflies-128.hf.space" frameborder="0" width="850" height="550"></iframe>
<p>이 가이드에서는 기존에 존재하던 데이터셋과 자신만의 커스텀 데이터셋에 대해 unconditional image generation 모델을 훈련하는 방법을 설명합니다. 훈련 세부 사항에 대해 더 자세히 알고 싶다면 unconditional image generation을 위한 모든 학습 스크립트를 <a href="https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation" rel="nofollow">여기</a>에서 확인할 수 있습니다.</p>
<p>스크립트를 실행하기 전, 먼저 의존성 라이브러리들을 설치해야 합니다.</p>
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<pre><!-- HTML_TAG_START -->pip install diffusers[training] accelerate datasets<!-- HTML_TAG_END --></pre></div>
<p>그 다음 🤗 <a href="https://github.com/huggingface/accelerate/" rel="nofollow">Accelerate</a> 환경을 초기화합니다.</p>
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<pre><!-- HTML_TAG_START -->accelerate config<!-- HTML_TAG_END --></pre></div>
<p>별도의 설정 없이 기본 설정으로 🤗 <a href="https://github.com/huggingface/accelerate/" rel="nofollow">Accelerate</a> 환경을 초기화해봅시다.</p>
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<pre><!-- HTML_TAG_START -->accelerate config default<!-- HTML_TAG_END --></pre></div>
<p>노트북과 같은 대화형 쉘을 지원하지 않는 환경의 경우, 다음과 같이 사용해볼 수도 있습니다.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> accelerate.utils <span class="hljs-keyword">import</span> write_basic_config
write_basic_config()<!-- HTML_TAG_END --></pre></div>
<h2 id="">모델을 허브에 업로드하기</h2>
<p>학습 스크립트에 다음 인자를 추가하여 허브에 모델을 업로드할 수 있습니다.</p>
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<pre><!-- HTML_TAG_START -->--push_to_hub<!-- HTML_TAG_END --></pre></div>
<h2 id="">체크포인트 저장하고 불러오기</h2>
<p>훈련 중 문제가 발생할 경우를 대비하여 체크포인트를 정기적으로 저장하는 것이 좋습니다. 체크포인트를 저장하려면 학습 스크립트에 다음 인자를 전달합니다:</p>
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<pre><!-- HTML_TAG_START -->--checkpointing_steps=500<!-- HTML_TAG_END --></pre></div>
<p>전체 훈련 상태는 500스텝마다 <code>output_dir</code>의 하위 폴더에 저장되며, 학습 스크립트에 <code>--resume_from_checkpoint</code> 인자를 전달함으로써 체크포인트를 불러오고 훈련을 재개할 수 있습니다.</p>
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<pre><!-- HTML_TAG_START -->--resume_from_checkpoint=<span class="hljs-string">&quot;checkpoint-1500&quot;</span><!-- HTML_TAG_END --></pre></div>
<h2 id="">파인튜닝</h2>
<p>이제 학습 스크립트를 시작할 준비가 되었습니다! <code>--dataset_name</code> 인자에 파인튜닝할 데이터셋 이름을 지정한 다음, <code>--output_dir</code> 인자에 지정된 경로로 저장합니다. 본인만의 데이터셋를 사용하려면, <a href="create_dataset">학습용 데이터셋 만들기</a> 가이드를 참조하세요.</p>
<p>학습 스크립트는 <code>diffusion_pytorch_model.bin</code> 파일을 생성하고, 그것을 당신의 리포지토리에 저장합니다.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>💡 전체 학습은 V100 GPU 4개를 사용할 경우, 2시간이 소요됩니다.</p></div>
<p>예를 들어, <a href="https://huggingface.co/datasets/huggan/flowers-102-categories" rel="nofollow">Oxford Flowers</a> 데이터셋을 사용해 파인튜닝할 경우:</p>
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<pre><!-- HTML_TAG_START -->accelerate launch train_unconditional.py \
--dataset_name=<span class="hljs-string">&quot;huggan/flowers-102-categories&quot;</span> \
--resolution=64 \
--output_dir=<span class="hljs-string">&quot;ddpm-ema-flowers-64&quot;</span> \
--train_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--push_to_hub<!-- HTML_TAG_END --></pre></div>
<div class="flex justify-center"><img src="https://user-images.githubusercontent.com/26864830/180248660-a0b143d0-b89a-42c5-8656-2ebf6ece7e52.png"></div>
[Pokemon](https://huggingface.co/datasets/huggan/pokemon) 데이터셋을 사용할 경우:
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<pre><!-- HTML_TAG_START -->accelerate launch train_unconditional.py \
--dataset_name=<span class="hljs-string">&quot;huggan/pokemon&quot;</span> \
--resolution=64 \
--output_dir=<span class="hljs-string">&quot;ddpm-ema-pokemon-64&quot;</span> \
--train_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--push_to_hub<!-- HTML_TAG_END --></pre></div>
<div class="flex justify-center"><img src="https://user-images.githubusercontent.com/26864830/180248200-928953b4-db38-48db-b0c6-8b740fe6786f.png"></div>
<h3 class="relative group"><a id="gpu" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#gpu"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>여러개의 GPU로 훈련하기
</span></h3>
<p><code>accelerate</code>을 사용하면 원활한 다중 GPU 훈련이 가능합니다. <code>accelerate</code>을 사용하여 분산 훈련을 실행하려면 <a href="https://huggingface.co/docs/accelerate/basic_tutorials/launch" rel="nofollow">여기</a> 지침을 따르세요. 다음은 명령어 예제입니다.</p>
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<pre><!-- HTML_TAG_START -->accelerate launch --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> --multi_gpu train_unconditional.py \
--dataset_name=<span class="hljs-string">&quot;huggan/pokemon&quot;</span> \
--resolution=64 --center_crop --random_flip \
--output_dir=<span class="hljs-string">&quot;ddpm-ema-pokemon-64&quot;</span> \
--train_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--use_ema \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \
--logger=<span class="hljs-string">&quot;wandb&quot;</span> \
--push_to_hub<!-- HTML_TAG_END --></pre></div>
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