<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pixel-Space on Text Matrix</title><link>https://txtmix.com/tags/pixel-space/</link><description>Recent content in Pixel-Space on Text Matrix</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Tue, 21 Jul 2026 20:06:14 +0800</lastBuildDate><atom:link href="https://txtmix.com/tags/pixel-space/index.xml" rel="self" type="application/rss+xml"/><item><title>L2P：腾讯开源的 Latent-to-Pixel 文生图高效迁移范式</title><link>https://txtmix.com/posts/tech/t2i-l2p-latent-potential-pixel-generation/</link><pubDate>Sat, 23 May 2026 03:15:00 +0800</pubDate><guid>https://txtmix.com/posts/tech/t2i-l2p-latent-potential-pixel-generation/</guid><description>&lt;h2 id="项目概览">项目概览&lt;/h2>
&lt;p>L2P（Latent-to-Pixel）是腾讯音视频实验室（Tencent Youtu Research）开源的一个文生图（Text-to-Image）项目，GitHub 仓库地址为 &lt;a href="https://github.com/TencentYoutuResearch/T2I-L2P" target="_blank" rel="noopener noreffer ">TencentYoutuResearch/T2I-L2P&lt;/a>。Stars 目前为 56，语言以 Python 为主。项目的核心主张很直接：把已经训练好的 latent-space 扩散模型的知识，迁移到 pixel-space 的端到端生成框架里，从而在低数据、低算力条件下获得高质量的文生图模型。&lt;/p></description></item></channel></rss>