<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Todo - 标签 - mywebsite</title><link>https://steven-yl.github.io/mywebsite/tags/todo/</link><description>Todo - 标签 - mywebsite</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>steven@gmail.com (Steven)</managingEditor><webMaster>steven@gmail.com (Steven)</webMaster><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Wed, 25 Mar 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://steven-yl.github.io/mywebsite/tags/todo/" rel="self" type="application/rss+xml"/><item><title>todo</title><link>https://steven-yl.github.io/mywebsite/todo/</link><pubDate>Wed, 25 Mar 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/todo/</guid><description>收集一个外部链接，后续用于整理与更新。</description></item><item><title>The Principles of Diffusion Models</title><link>https://steven-yl.github.io/mywebsite/the-principles-of-diffusion-models/</link><pubDate>Sat, 28 Feb 2026 10:26:59 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/the-principles-of-diffusion-models/</guid><description>&lt;div class="featured-image">
                &lt;img src="/mywebsite/posts/images/the-principles-of-diffusion-models.webp" referrerpolicy="no-referrer">
            &lt;/div>《The Principles of Diffusion Models》（arXiv:2510.21890）全文技术解读：从前向破坏过程与反向生成出发，系统梳理扩散模型的三种表述——变分视角（VAE→DDPM）、基于分数的视角（EBM→NCSN→分数 SDE）、基于流的视角（NF→流匹配），阐明条件化技巧与福克–普朗克方程下的统一；并涵盖引导生成、数值求解器、蒸馏与从零学习的流映射模型（CM/CTM/MF）等。</description></item><item><title>Consistency Model</title><link>https://steven-yl.github.io/mywebsite/consistency-model/</link><pubDate>Fri, 27 Feb 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/consistency-model/</guid><description><![CDATA[<div class="featured-image">
                <img src="/mywebsite/posts/images/consistency-model.webp" referrerpolicy="no-referrer">
            </div><p>Consistency Model 技术文档（原理 + 自洽性 + 蒸馏/直接训练 + 采样 + 对比）</p>
<p>本文档为正式技术规格文档，系统介绍 Consistency Model 的核心思想、自洽性定义、两种训练范式（Consistency Distillation / Consistency Training）、一步与多步采样，以及与扩散模型、MeanFlow 的对比。</p>]]></description></item><item><title>DiffusionDriveV2 网络结构图</title><link>https://steven-yl.github.io/mywebsite/diffusiondrivev2_network/</link><pubDate>Wed, 25 Mar 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/diffusiondrivev2_network/</guid><description>本文以结构图的方式梳理 DiffusionDrive V2 的关键模块与连接关系：双流骨干特征融合、Transformer Decoder×3、扩散式 TrajectoryHead 的截断生成以及粗筛-精筛评分流水线。</description></item><item><title>DiffusionDriveV2 代码结构图</title><link>https://steven-yl.github.io/mywebsite/diffusiondrivev2_code_structure/</link><pubDate>Wed, 25 Mar 2026 00:00:00 +0800</pubDate><author><name>Steven</name><uri>https://github.com/steven-yl</uri></author><guid>https://steven-yl.github.io/mywebsite/diffusiondrivev2_code_structure/</guid><description>通过目录树与模块依赖关系，梳理 DiffusionDrive v2（RL/Selection 相关模型及扩散模块）在工程中的位置与调用链路。</description></item></channel></rss>