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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>STAR: Scale-wise Text-conditioned AutoRegressive image generation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.10797/</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/2406.10797/feed.xml" rel="self" type="application/rss+xml" /><item><title>STAR: Scale-wise Text-conditioned AutoRegressive image generation — mentioned by famouswaffles</title><link>https://news.ycombinator.com/item?id=41750039</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.10797:41750039</guid><pubDate>Sat, 05 Oct 2024 14:15:51 GMT</pubDate><description>&lt;p&gt;Autoregressive Transformers scale and learn much better/faster when trained to predict the &amp;quot;next-resolution&amp;quot;/&amp;quot;next-scale&amp;quot;, i.e start very small and gradually scale up the resolution and size vs being trained to predict the next image token/patch. Related: VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling - https://arxiv.org/abs/2408.01181 STAR: Scale-wise Text-to-image generation via Auto-Regressive representations https://arxiv.org/abs/2406.10797&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.10797/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
