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<!DOCTYPE html>
<html lang="en">
<head>
<script async src="https://www.googletagmanager.com/gtag/js?id=G-C1CRWDNJ1J"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-C1CRWDNJ1J');
</script>
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Noto+Sans+SC:wght@100..900&display=swap" rel="stylesheet">
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Chinese reading task about ML</title>
<style>
body {
font-family: Arial, sans-serif;
background-color: #f4f4f9;
color: #333;
margin: 0;
padding: 20px;
}
.container {
max-width: 800px;
margin: 0 auto;
background-color: #fff;
padding: 20px;
border-radius: 8px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
h1 {
color: #0056b3;
text-align: center;
}
p {
line-height: 1.6;
}
.zh-text {
font-size: 1.3em;
font-family: 'Noto Sans SC';
font-weight: 300;
margin: 0 0 5px 0;
}
.pinyin {
padding-top: 5px;
padding-bottom: 5px;
font-style: italic;
color: #888;
}
table {
width: 100%;
border-collapse: collapse;
margin-top: 20px;
}
th, td {
padding: 12px;
border: 1px solid #ddd;
text-align: left;
}
th {
background-color: #0056b3;
color: #fff;
}
td {
background-color: #f9f9f9;
}
td.zh {
font-family: 'Noto Sans SC';
font-size: 1.2em;
font-weight: 400;
}
</style>
</head>
<body>
<div class="container">
<h1>TPDiff: Temporal Pyramid Video Diffusion Model</h1>
<div><p class='zh-text'>1. 这篇文章讨论了视频扩散模型的发展面临的计算需求挑战。</p>
<p class='zh-text'>2. 为了缓解这一问题，研究人员提出了TPDiff框架，通过分阶段增加帧率来优化计算效率。</p>
<p class='zh-text'>3. 该框架只在最后阶段使用全帧率，从而提高训练和推理效率。</p>
<p class='zh-text'>4. 实验结果显示，这种方法可以减少50%的训练成本，并提高1.5倍的推理效率。</p></div>
<div class="pinyin">
<p>1. 这篇文章讨论了视频扩散模型的发展面临的计算需求挑战。
Zhè piān wénzhāng tǎolùn le shìpín kuòsàn móxíng de fāzhǎn miànlín de jìsuàn xūqiú tiǎozhàn</p>
<p>2.
为了缓解这一问题，研究人员提出了TPDiff框架，通过分阶段增加帧率来优化计算效率。
Wèile huǎnjiě zhè yī wèntí, yánjiū rényuán tíchū le TP Diff kuàngjià, tōngguò fēn jiēduàn zēngjiā zhēnlǜ lái yōuhuà jìsuàn xiàolǜ</p>
<p>3.
该框架只在最后阶段使用全帧率，从而提高训练和推理效率。
Gǎi kuàngjià zhǐ zài zuìhòu jiēduàn shǐyòng quán zhēnlǜ, cóng'ér tígāo xùnliàn hé tuīlǐ xiàolǜ</p>
<p>4.
实验结果显示，这种方法可以减少50%的训练成本，并提高1</p>
<p>5. 5倍的推理效率。
Shíyàn jiéguǒ xiǎnshì, zhè zhǒng fāngfǎ kěyǐ jiǎnshǎo 50% de xùnliàn chéngběn, bìng tígāo 1</p>
<p>6. 5 bèi de tuīlǐ xiàolǜ</p>
</div>
<div><p>1. This article discusses the computational challenges faced in the development of video diffusion models.</p>
<p>2. To mitigate this issue, researchers have proposed the TPDiff framework, which optimizes computational efficiency by incrementally increasing the frame rate in stages.</p>
<p>3. The framework only uses the full frame rate in the final stage, thereby enhancing both training and inference efficiency.</p>
<p>4. Experimental results show that this method can reduce training costs by 50% and improve inference efficiency by 1.</p>
<p>5. 5 times.</p></div>
<h2>Vocabulary</h2>
<table>
<thead>
<tr>
<th>Word</th>
<th>Pinyin</th>
<th>Translation</th>
</tr>
</thead>
<tbody>
<tr>
<td class="zh">讨论</td>
<td>tǎo lùn</td>
<td>discuss</td>
</tr>
<tr>
<td class="zh">视频</td>
<td>shì pín</td>
<td>video</td>
</tr>
<tr>
<td class="zh">扩散</td>
<td>kuò sàn</td>
<td>diffusion</td>
</tr>
<tr>
<td class="zh">模型</td>
<td>mó xíng</td>
<td>model</td>
</tr>
<tr>
<td class="zh">发展</td>
<td>fā zhǎn</td>
<td>development</td>
</tr>
<tr>
<td class="zh">面临</td>
<td>miàn lín</td>
<td>face</td>
</tr>
<tr>
<td class="zh">计算</td>
<td>jì suàn</td>
<td>calculation</td>
</tr>
<tr>
<td class="zh">需求</td>
<td>xū qiú</td>
<td>demand</td>
</tr>
<tr>
<td class="zh">挑战</td>
<td>tiǎo zhàn</td>
<td>challenge</td>
</tr>
<tr>
<td class="zh">缓解</td>
<td>huǎn jiě</td>
<td>alleviate</td>
</tr>
<tr>
<td class="zh">提出</td>
<td>tí chū</td>
<td>propose</td>
</tr>
<tr>
<td class="zh">框架</td>
<td>kuàng jià</td>
<td>framework</td>
</tr>
<tr>
<td class="zh">通过</td>
<td>tōng guò</td>
<td>through</td>
</tr>
<tr>
<td class="zh">分阶段</td>
<td>fēn jiē duàn</td>
<td>in stages</td>
</tr>
<tr>
<td class="zh">增加</td>
<td>zēng jiā</td>
<td>increase</td>
</tr>
<tr>
<td class="zh">帧率</td>
<td>zhèn lǜ</td>
<td>frame rate</td>
</tr>
<tr>
<td class="zh">优化</td>
<td>yōu huà</td>
<td>optimize</td>
</tr>
<tr>
<td class="zh">效率</td>
<td>xiào lǜ</td>
<td>efficiency</td>
</tr>
<tr>
<td class="zh">阶段</td>
<td>jiē duàn</td>
<td>stage</td>
</tr>
<tr>
<td class="zh">使用</td>
<td>shǐ yòng</td>
<td>use</td>
</tr>
<tr>
<td class="zh">全帧率</td>
<td>quán zhèn lǜ</td>
<td>full frame rate</td>
</tr>
<tr>
<td class="zh">从而</td>
<td>cóng ér</td>
<td>thus</td>
</tr>
<tr>
<td class="zh">提高</td>
<td>tí gāo</td>
<td>improve</td>
</tr>
<tr>
<td class="zh">训练</td>
<td>xùn liàn</td>
<td>training</td>
</tr>
<tr>
<td class="zh">推理</td>
<td>tuī lǐ</td>
<td>inference</td>
</tr>
<tr>
<td class="zh">实验</td>
<td>shí yàn</td>
<td>experiment</td>
</tr>
<tr>
<td class="zh">结果</td>
<td>jié guǒ</td>
<td>result</td>
</tr>
<tr>
<td class="zh">显示</td>
<td>xiǎn shì</td>
<td>show</td>
</tr>
<tr>
<td class="zh">方法</td>
<td>fāng fǎ</td>
<td>method</td>
</tr>
<tr>
<td class="zh">减少</td>
<td>jiǎn shǎo</td>
<td>reduce</td>
</tr>
<tr>
<td class="zh">成本</td>
<td>chéng běn</td>
<td>cost</td>
</tr>
<tr>
<td class="zh">提高</td>
<td>tí gāo</td>
<td>increase</td>
</tr>
</tbody>
</table>
</div>
</body>
</html>