<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Improved Denoising Diffusion Probabilistic Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2102.09672/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2102.09672/feed.xml" rel="self" type="application/rss+xml" /><item><title>Improved Denoising Diffusion Probabilistic Models — mentioned by godelski</title><link>https://news.ycombinator.com/item?id=44401316</link><guid isPermaLink="false">urn:hackerpapers:mention:2102.09672:44401316</guid><pubDate>Fri, 27 Jun 2025 23:48:00 GMT</pubDate><description>&lt;p&gt;As far as I&amp;#x27;m aware, this is the largest Normalizing Flow that exists, and I think they undermined their work by not mentioning this... Their ImageNet model (4_1024_8_8_0.05[0]) is ~820M while AFHQ is ~472M. Prior to that there is DenseFlow[1] and MaCow[2], which are both &amp;lt;200M parameters. For more comparison, that makes DenseFlow and MaCow smaller than iDDPM[3] (270M params) and ADM[4] (553M for 256 unconditional). And now, it isn&amp;#x27;t uncommon for modern diffusion models to have several billion parameters![5] (from this we get some numbers on ImageNet-256, which allows a direct comparison…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2102.09672/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
