<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Ddpm on Papers &amp; Code</title><link>https://woletee.github.io/diffusion-papers-/tags/ddpm/</link><description>Recent content in Ddpm on Papers &amp; Code</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 23 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://woletee.github.io/diffusion-papers-/tags/ddpm/index.xml" rel="self" type="application/rss+xml"/><item><title>DDPM: How Adding Noise Taught Machines to Draw</title><link>https://woletee.github.io/diffusion-papers-/diffusion/ddpm/</link><pubDate>Sun, 23 Aug 2026 00:00:00 +0000</pubDate><guid>https://woletee.github.io/diffusion-papers-/diffusion/ddpm/</guid><description>A plain-English, picture-first explanation of Denoising Diffusion Probabilistic Models (Ho, Jain &amp;amp; Abbeel, 2020), with a minimal runnable PyTorch implementation.</description></item></channel></rss>