<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Google Colaboratory on kenji.blog</title><link>http://kenji.blog/ko/tags/google-colaboratory/</link><description>Recent content in Google Colaboratory on kenji.blog</description><generator>Hugo -- gohugo.io</generator><language>ko</language><copyright>kenjinote</copyright><lastBuildDate>Sun, 09 Apr 2023 01:02:19 +0900</lastBuildDate><atom:link href="http://kenji.blog/ko/tags/google-colaboratory/index.xml" rel="self" type="application/rss+xml"/><item><title>Python(matplotlib.pyplot)을 사용하여 그래프를 그리는 방법</title><link>http://kenji.blog/ko/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/</link><pubDate>Sun, 09 Apr 2023 01:02:19 +0900</pubDate><guid>http://kenji.blog/ko/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/</guid><description>&lt;img src="http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img.png" alt="Featured image of post Python(matplotlib.pyplot)을 사용하여 그래프를 그리는 방법" />&lt;p>&lt;img src="http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_1.png"
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srcset="http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_1_hue3f23818eeeec9c43d6452cc0f61fb52_43864_480x0_resize_box_3.png 480w, http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_1_hue3f23818eeeec9c43d6452cc0f61fb52_43864_1024x0_resize_box_3.png 1024w"
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>&lt;/p>
&lt;h1 id="필요한-것">필요한 것
&lt;/h1>&lt;ul>
&lt;li>Google 계정&lt;/li>
&lt;/ul>
&lt;h1 id="순서">순서
&lt;/h1>&lt;ol>
&lt;li>&lt;a class="link" href="https://colab.research.google.com/" target="_blank" rel="noopener"
>https://colab.research.google.com/&lt;/a> 에 접속&lt;/li>
&lt;li>&amp;lsquo;파일&amp;rsquo; → &amp;lsquo;새 노트&amp;rsquo; 선택&lt;/li>
&lt;li>아래의 코드를 붙여넣고 실행&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;span class="lnt">2
&lt;/span>&lt;span class="lnt">3
&lt;/span>&lt;span class="lnt">4
&lt;/span>&lt;span class="lnt">5
&lt;/span>&lt;span class="lnt">6
&lt;/span>&lt;span class="lnt">7
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-python" data-lang="python">&lt;span class="line">&lt;span class="cl">&lt;span class="kn">import&lt;/span> &lt;span class="nn">matplotlib.pyplot&lt;/span> &lt;span class="k">as&lt;/span> &lt;span class="nn">plt&lt;/span>
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">&lt;span class="kn">import&lt;/span> &lt;span class="nn">numpy&lt;/span> &lt;span class="k">as&lt;/span> &lt;span class="nn">np&lt;/span>
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">&lt;span class="n">x&lt;/span> &lt;span class="o">=&lt;/span> &lt;span class="n">np&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">linspace&lt;/span>&lt;span class="p">(&lt;/span>&lt;span class="mi">0&lt;/span>&lt;span class="p">,&lt;/span> &lt;span class="mi">2&lt;/span>&lt;span class="o">*&lt;/span>&lt;span class="n">np&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">pi&lt;/span>&lt;span class="p">,&lt;/span> &lt;span class="mi">500&lt;/span>&lt;span class="p">)&lt;/span>
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">&lt;span class="n">plt&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">plot&lt;/span>&lt;span class="p">(&lt;/span>&lt;span class="n">x&lt;/span>&lt;span class="p">,&lt;/span> &lt;span class="n">np&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">sin&lt;/span>&lt;span class="p">(&lt;/span>&lt;span class="n">x&lt;/span>&lt;span class="p">),&lt;/span> &lt;span class="n">label&lt;/span>&lt;span class="o">=&lt;/span>&lt;span class="s2">&amp;#34;sin curve&amp;#34;&lt;/span>&lt;span class="p">)&lt;/span>
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">&lt;span class="n">plt&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">plot&lt;/span>&lt;span class="p">(&lt;/span>&lt;span class="n">x&lt;/span>&lt;span class="p">,&lt;/span> &lt;span class="n">np&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">cos&lt;/span>&lt;span class="p">(&lt;/span>&lt;span class="n">x&lt;/span>&lt;span class="p">),&lt;/span> &lt;span class="n">label&lt;/span>&lt;span class="o">=&lt;/span>&lt;span class="s2">&amp;#34;cos curve&amp;#34;&lt;/span>&lt;span class="p">)&lt;/span>
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">&lt;span class="n">plt&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">legend&lt;/span>&lt;span class="p">()&lt;/span> &lt;span class="c1"># 범례 표시&lt;/span>
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">&lt;span class="n">plt&lt;/span>&lt;span class="o">.&lt;/span>&lt;span class="n">show&lt;/span>&lt;span class="p">()&lt;/span>
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;h1 id="실행-결과">실행 결과
&lt;/h1>&lt;p>&lt;img src="http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img.png"
width="568"
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srcset="http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_hucdb24ab588d845f9f7a56ef99be2c709_27745_480x0_resize_box_3.png 480w, http://kenji.blog/p/pythonmatplotlib.pyplot%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%B0%E3%83%A9%E3%83%95%E3%82%92%E6%8F%8F%E7%94%BB%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_hucdb24ab588d845f9f7a56ef99be2c709_27745_1024x0_resize_box_3.png 1024w"
loading="lazy"
alt="img.png"
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data-flex-grow="137"
data-flex-basis="330px"
>&lt;/p>
&lt;h1 id="참고">참고
&lt;/h1>&lt;ul>
&lt;li>&lt;a class="link" href="https://matplotlib.org/3.5.3/api/_as_gen/matplotlib.pyplot.html" target="_blank" rel="noopener"
>matplotlib.pyplot — Matplotlib 3.5.3 documentation&lt;/a>&lt;/li>
&lt;/ul></description></item><item><title>'Twitter API와 Google Colaboratory를 사용하여 트윗하는 방법'</title><link>http://kenji.blog/ko/p/twitter-api%E3%81%A8google-colaboratory%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6tweet%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/</link><pubDate>Sat, 08 Apr 2023 18:48:32 +0900</pubDate><guid>http://kenji.blog/ko/p/twitter-api%E3%81%A8google-colaboratory%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6tweet%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/</guid><description>&lt;img src="http://kenji.blog/p/twitter-api%E3%81%A8google-colaboratory%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6tweet%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img.png" alt="Featured image of post 'Twitter API와 Google Colaboratory를 사용하여 트윗하는 방법'" />&lt;h1 id="필요한-것">필요한 것
&lt;/h1>&lt;ul>
&lt;li>Twitter API&lt;/li>
&lt;li>Twitter API SECRET&lt;/li>
&lt;li>Twitter ACCESS TOKEN&lt;/li>
&lt;li>Twitter ACCESS TOKEN SECRET&lt;/li>
&lt;li>Google 계정&lt;/li>
&lt;/ul>
&lt;p>Twitter API 취득 방법은 참고 사이트를 참조해 주세요.&lt;/p>
&lt;h1 id="api를-사용하여-트윗하는-순서">API를 사용하여 트윗하는 순서
&lt;/h1>&lt;ol>
&lt;li>&lt;a class="link" href="https://colab.research.google.com/" target="_blank" rel="noopener"
>https://colab.research.google.com/&lt;/a> 에 접속&lt;/li>
&lt;li>「파일」→「새 노트 만들기」를 선택&lt;/li>
&lt;li>아래의 코드를 붙여넣고 실행 (실제 각 값은 직접 취득한 것을 사용해 주세요)&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;span class="lnt">2
&lt;/span>&lt;span class="lnt">3
&lt;/span>&lt;span class="lnt">4
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">API_KEY = &amp;#39;9Smu2f2RoLqbVQHQq6n79Z2JW&amp;#39;
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">API_SECRET = &amp;#39;uGVRIkLL2l8sRyPv2Lr4mXxXppnQF1isMoRnvktcXCtFgAK2R8&amp;#39;
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">ACCESS_TOKEN = &amp;#39;0367292979164670705-7hSErDoQbO6fkFtnn5UY0vqpvecy0O&amp;#39;
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">ACCESS_TOKEN_SECRET = &amp;#39;pUv81U9GVzZirz5g4AxZPHAJ4GpSXnBo8GUcZ1egtjw9q&amp;#39;
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;ol start="3">
&lt;li>아래의 코드를 붙여넣고 실행&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">import tweepy
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;ol start="4">
&lt;li>아래의 코드를 붙여넣고 실행 (API v1.1)&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;span class="lnt">2
&lt;/span>&lt;span class="lnt">3
&lt;/span>&lt;span class="lnt">4
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">auth = tweepy.OAuthHandler(API_KEY, API_SECRET)
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">api = tweepy.API(auth)
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">api.update_status(&amp;#34;hello&amp;#34;)
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;p>→&lt;code>hello&lt;/code>라는 트윗이 게시됨&lt;/p>
&lt;ol start="5">
&lt;li>아래의 코드를 붙여넣고 실행 (API v2.0)&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;span class="lnt">2
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">client = tweepy.Client(consumer_key=API_KEY, consumer_secret=API_SECRET, access_token=ACCESS_TOKEN, access_token_secret=ACCESS_TOKEN_SECRET)
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">client.create_tweet(text=&amp;#39;hello v2&amp;#39;)
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;p>→&lt;code>hello v2&lt;/code>라는 트윗이 게시됨&lt;/p>
&lt;p>이상&lt;/p>
&lt;h1 id="참고">참고
&lt;/h1>&lt;ul>
&lt;li>&lt;a class="link" href="https://bloomtectec.com/twitter-api-application-procedure/" target="_blank" rel="noopener"
>【2021년 4월 기준】 Twitter API 이용 신청을 사용 용도 예문과 스크린샷으로 철저하게 해설&lt;/a>&lt;/li>
&lt;li>&lt;a class="link" href="https://bloomtectec.com/use-twitter-api-in-google-colab/" target="_blank" rel="noopener"
>【번거로운 설정 불필요!】 Twitter API 테스트 환경이라면 Google Colaboratory를 추천 【소스 코드도 공유합니다】&lt;/a>&lt;/li>
&lt;li>&lt;a class="link" href="https://3pysci.com/tweepy-28/" target="_blank" rel="noopener"
>【Tweepy】 Twitter API v2: 트윗, 답글(리플라이), 투표 포함 트윗, 미디어 포함 트윗 (v1.1) [Python]&lt;/a>&lt;/li>
&lt;/ul></description></item><item><title>'AI(StableDiffusion)를 사용하여 일러스트 이미지를 생성하는 방법'</title><link>http://kenji.blog/ko/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/</link><pubDate>Thu, 06 Apr 2023 00:43:19 +0900</pubDate><guid>http://kenji.blog/ko/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/</guid><description>&lt;img src="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img.png" alt="Featured image of post 'AI(StableDiffusion)를 사용하여 일러스트 이미지를 생성하는 방법'" />&lt;h1 id="stable-diffusion이란">Stable diffusion이란
&lt;/h1>&lt;p>Stable diffusion은 독일 뮌헨 대학교의 연구팀이 개발한, 입력된 텍스트 정보로부터 이미지를 생성하는 AI입니다.
다양한 이미지를 학습시킴으로써 실사부터 일러스트까지 다양한 이미지를 생성할 수 있습니다.&lt;/p>
&lt;p>이번에는 Stable diffusion의 학습된 데이터를 사용하여 일러스트 이미지를 생성하는 방법을 소개합니다.&lt;/p>
&lt;h1 id="준비물">준비물
&lt;/h1>&lt;ul>
&lt;li>Google 계정&lt;/li>
&lt;/ul>
&lt;p>이 전부입니다.&lt;/p>
&lt;h1 id="생성-절차">생성 절차
&lt;/h1>&lt;ol>
&lt;li>&lt;a class="link" href="https://colab.research.google.com" target="_blank" rel="noopener"
>https://colab.research.google.com&lt;/a> 을 엽니다.&lt;/li>
&lt;li>왼쪽 상단의 &lt;code>파일&lt;/code>에서 &lt;code>새 노트 만들기&lt;/code>를 선택합니다.&lt;/li>
&lt;li>&lt;code>수정&lt;/code>에서 &lt;code>노트 설정&lt;/code>을 선택합니다.&lt;/li>
&lt;li>&lt;code>하드웨어 가속기&lt;/code>를 &lt;code>GPU&lt;/code>로 변경합니다.
&lt;img src="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_2.png"
width="627"
height="324"
srcset="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_2_hud7bbdf6a24e33b2b36528d9e25b016c8_23961_480x0_resize_box_3.png 480w, http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_2_hud7bbdf6a24e33b2b36528d9e25b016c8_23961_1024x0_resize_box_3.png 1024w"
loading="lazy"
alt="img_2.png"
class="gallery-image"
data-flex-grow="193"
data-flex-basis="464px"
>&lt;/li>
&lt;li>아래의 코드를 붙여넣고 실행합니다.&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">!pip install diffusers==0.8.0 transformers
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;ol start="6">
&lt;li>아래의 코드를 붙여넣고 실행합니다.&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">from diffusers import StableDiffusionPipeline
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;ol start="7">
&lt;li>아래의 코드를 붙여넣고 실행합니다.&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;span class="lnt">2
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">pipe = StableDiffusionPipeline.from_pretrained(&amp;#34;gsdf/Counterfeit-V2.5&amp;#34;)
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">pipe.to(&amp;#34;cuda&amp;#34;)
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;ol start="8">
&lt;li>아래의 코드를 붙여넣고 실행합니다.&lt;/li>
&lt;/ol>
&lt;div class="highlight">&lt;div class="chroma">
&lt;table class="lntable">&lt;tr>&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code>&lt;span class="lnt">1
&lt;/span>&lt;span class="lnt">2
&lt;/span>&lt;span class="lnt">3
&lt;/span>&lt;span class="lnt">4
&lt;/span>&lt;span class="lnt">5
&lt;/span>&lt;/code>&lt;/pre>&lt;/td>
&lt;td class="lntd">
&lt;pre tabindex="0" class="chroma">&lt;code class="language-fallback" data-lang="fallback">&lt;span class="line">&lt;span class="cl">prompt = &amp;#34;((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field&amp;#34;
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">n_prompt = &amp;#34;EasyNegative, extra fingers,fewer fingers&amp;#34;
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">image = pipe(prompt, negative_prompt = n_prompt).images[0]
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">image
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/td>&lt;/tr>&lt;/table>
&lt;/div>
&lt;/div>&lt;p>여기서 사용한 &lt;code>Prompt&lt;/code>는 &lt;a class="link" href="https://huggingface.co/gsdf/Counterfeit-V2.5" target="_blank" rel="noopener"
>https://huggingface.co/gsdf/Counterfeit-V2.5&lt;/a>의 &lt;code>Prompt&lt;/code>를 참고했습니다.&lt;/p>
&lt;h2 id="생성-결과-일부">생성 결과 (일부)
&lt;/h2>&lt;p>&lt;img src="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_1.png"
width="512"
height="512"
srcset="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_1_hu1ec19e515fd7538067cc14f2c3c8bddf_594392_480x0_resize_box_3.png 480w, http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_1_hu1ec19e515fd7538067cc14f2c3c8bddf_594392_1024x0_resize_box_3.png 1024w"
loading="lazy"
alt="img_1.png"
class="gallery-image"
data-flex-grow="100"
data-flex-basis="240px"
>&lt;/p>
&lt;p>&lt;img src="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_3.png"
width="512"
height="512"
srcset="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_3_hu67e673668e1b02812ead4fb15912ac36_533749_480x0_resize_box_3.png 480w, http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_3_hu67e673668e1b02812ead4fb15912ac36_533749_1024x0_resize_box_3.png 1024w"
loading="lazy"
alt="img_3.png"
class="gallery-image"
data-flex-grow="100"
data-flex-basis="240px"
>&lt;/p>
&lt;p>&lt;img src="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_4.png"
width="512"
height="512"
srcset="http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_4_hu2b826ec3ad4d7685d995efaa40aac0c5_597506_480x0_resize_box_3.png 480w, http://kenji.blog/p/aistablediffusion%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%82%A4%E3%83%A9%E3%82%B9%E3%83%88%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90%E3%81%99%E3%82%8B%E6%96%B9%E6%B3%95/img_4_hu2b826ec3ad4d7685d995efaa40aac0c5_597506_1024x0_resize_box_3.png 1024w"
loading="lazy"
alt="img_4.png"
class="gallery-image"
data-flex-grow="100"
data-flex-basis="240px"
>&lt;/p>
&lt;h2 id="참고">참고
&lt;/h2>&lt;ul>
&lt;li>&lt;a class="link" href="https://huggingface.co/gsdf/Counterfeit-V2.5" target="_blank" rel="noopener"
>https://huggingface.co/gsdf/Counterfeit-V2.5&lt;/a>&lt;/li>
&lt;li>&lt;a class="link" href="https://www.youtube.com/watch?v=l8-fVSM2PVQ" target="_blank" rel="noopener"
>인공지능(AI)을 사용한 이미지 생성 프로그램을 15분 만에 만들어 보았다【실황 프로그래밍】&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>