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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Progressive Distillation for Fast Sampling of Diffusion Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2202.00512/</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/2202.00512/feed.xml" rel="self" type="application/rss+xml" /><item><title>Progressive Distillation for Fast Sampling of Diffusion Models — mentioned by johndough</title><link>https://news.ycombinator.com/item?id=31086823</link><guid isPermaLink="false">urn:hackerpapers:mention:2202.00512:31086823</guid><pubDate>Tue, 19 Apr 2022 18:20:51 GMT</pubDate><description>&lt;p&gt;I was wondering it if would be possible to train a neural network to do multiple iterative steps at once. As it turns out, it has already been done and it requires about 4 to 8 distilled iterations for comparable quality. If this pace keeps up, we will probably see similar running time to GANs in the near future. https://arxiv.org/pdf/2202.00512.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2202.00512/"&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>
