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<h1 class="relative group"><a id="deterministic" 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="#deterministic"><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>Deterministic(결정적) 생성을 통한 이미지 품질 개선
	</span></h1>

<p>생성된 이미지의 품질을 개선하는 일반적인 방법은 <em>결정적 batch(배치) 생성</em>을 사용하는 것입니다. 이 방법은 이미지 batch(배치)를 생성하고 두 번째 추론 라운드에서 더 자세한 프롬프트와 함께 개선할 이미지 하나를 선택하는 것입니다. 핵심은 일괄 이미지 생성을 위해 파이프라인에 <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html#generator" rel="nofollow"><code>torch.Generator</code></a> 목록을 전달하고, 각 <code>Generator</code>를 시드에 연결하여 이미지에 재사용할 수 있도록 하는 것입니다.</p>
<p>예를 들어 <a href="runwayml/stable-diffusion-v1-5"><code>runwayml/stable-diffusion-v1-5</code></a>를 사용하여 다음 프롬프트의 여러 버전을 생성해 봅시다.</p>

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	<pre><!-- HTML_TAG_START -->prompt = <span class="hljs-string">&quot;Labrador in the style of Vermeer&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>(가능하다면) 파이프라인을 <code>DiffusionPipeline.from_pretrained()</code>로 인스턴스화하여 GPU에 배치합니다.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>이제 네 개의 서로 다른 <code>Generator</code>를 정의하고 각 <code>Generator</code>에 시드(<code>0</code> ~ <code>3</code>)를 할당하여 나중에 특정 이미지에 대해 <code>Generator</code>를 재사용할 수 있도록 합니다.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = [torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(i) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">4</span>)]<!-- HTML_TAG_END --></pre></div>
<p>이미지를 생성하고 살펴봅니다.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt, generator=generator, num_images_per_prompt=<span class="hljs-number">4</span>).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images<!-- HTML_TAG_END --></pre></div>
<p><img src="https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds.jpg" alt="img"></p>
<p>이 예제에서는 첫 번째 이미지를 개선했지만 실제로는 원하는 모든 이미지를 사용할 수 있습니다(심지어 두 개의 눈이 있는 이미지도!). 첫 번째 이미지에서는 시드가 ‘0’인 ‘생성기’를 사용했기 때문에 두 번째 추론 라운드에서는 이 ‘생성기’를 재사용할 것입니다. 이미지의 품질을 개선하려면 프롬프트에 몇 가지 텍스트를 추가합니다:</p>

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	<pre><!-- HTML_TAG_START -->prompt = [prompt + t <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> [<span class="hljs-string">&quot;, highly realistic&quot;</span>, <span class="hljs-string">&quot;, artsy&quot;</span>, <span class="hljs-string">&quot;, trending&quot;</span>, <span class="hljs-string">&quot;, colorful&quot;</span>]]
generator = [torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">4</span>)]<!-- HTML_TAG_END --></pre></div>
<p>시드가 <code>0</code>인 제너레이터 4개를 생성하고, 이전 라운드의 첫 번째 이미지처럼 보이는 다른 이미지 batch(배치)를 생성합니다!</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>images = pipe(prompt, generator=generator).images
<span class="hljs-meta">&gt;&gt;&gt; </span>images<!-- HTML_TAG_END --></pre></div>
<p><img src="https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds_2.jpg" alt="img"></p>


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