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<h1 id="">이미지 밝기 조절하기</h1>
<p>Stable Diffusion 파이프라인은 <a href="https://huggingface.co/papers/2305.08891" rel="nofollow">일반적인 디퓨전 노이즈 스케줄과 샘플 단계에 결함이 있음</a> 논문에서 설명한 것처럼 매우 밝거나 어두운 이미지를 생성하는 데는 성능이 평범합니다. 이 논문에서 제안한 솔루션은 현재 <code>DDIMScheduler</code>에 구현되어 있으며 이미지의 밝기를 개선하는 데 사용할 수 있습니다.</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>💡 제안된 솔루션에 대한 자세한 내용은 위에 링크된 논문을 참고하세요!</p></div>
<p>해결책 중 하나는 <em>v 예측값</em>과 <em>v 로스</em>로 모델을 훈련하는 것입니다. 다음 flag를 <a href="https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py" rel="nofollow"><code>train_text_to_image.py</code></a> 또는 <a href="https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py" rel="nofollow"><code>train_text_to_image_lora.py</code></a> 스크립트에 추가하여 <code>v_prediction</code>을 활성화합니다:</p>

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	<pre><!-- HTML_TAG_START -->--prediction_type=<span class="hljs-string">&quot;v_prediction&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>예를 들어, <code>v_prediction</code>으로 미세 조정된 <a href="https://huggingface.co/ptx0/pseudo-journey-v2" rel="nofollow"><code>ptx0/pseudo-journey-v2</code></a> 체크포인트를 사용해 보겠습니다.</p>
<p>다음으로 <code>DDIMScheduler</code>에서 다음 파라미터를 설정합니다:</p>
<ol><li>rescale_betas_zero_snr=True`, 노이즈 스케줄을 제로 터미널 신호 대 잡음비(SNR)로 재조정합니다.</li>
<li><code>timestep_spacing=&quot;trailing&quot;</code>, 마지막 타임스텝부터 샘플링 시작</li></ol>

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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, DDIMScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;ptx0/pseudo-journey-v2&quot;</span>)
<span class="hljs-comment"># switch the scheduler in the pipeline to use the DDIMScheduler</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.scheduler = DDIMScheduler.from_config(
<span class="hljs-meta">... </span>    pipeline.scheduler.config, rescale_betas_zero_snr=<span class="hljs-literal">True</span>, timestep_spacing=<span class="hljs-string">&quot;trailing&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>마지막으로 파이프라인에 대한 호출에서 <code>guidance_rescale</code>을 설정하여 과다 노출을 방지합니다:</p>

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	<pre><!-- HTML_TAG_START -->prompt = <span class="hljs-string">&quot;A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k&quot;</span>
image = pipeline(prompt, guidance_rescale=<span class="hljs-number">0.7</span>).images[<span class="hljs-number">0</span>]<!-- HTML_TAG_END --></pre></div>
<div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/zero_snr.png"></div>


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