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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>OmniGen: Unified Image Generation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2409.11340/</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/2409.11340/feed.xml" rel="self" type="application/rss+xml" /><item><title>OmniGen: Unified Image Generation — mentioned by ed</title><link>https://news.ycombinator.com/item?id=41999115</link><guid isPermaLink="false">urn:hackerpapers:mention:2409.11340:41999115</guid><pubDate>Wed, 30 Oct 2024 19:21:25 GMT</pubDate><description>&lt;p&gt;Elegant architecture, trained from scratch, excels at image editing. This looks very interesting! From https://arxiv.org/html/2409.11340v1 &amp;gt; Unlike popular diffusion models, OmniGen features a very concise structure, comprising only two main components: a VAE and a transformer model, without any additional encoders. &amp;gt; OmniGen supports arbitrarily interleaved text and image inputs as conditions to guide image generation, rather than text-only or image-only conditions. &amp;gt; Additionally, we incorporate several classic computer vision tasks such as human pose estimation, edge detection, and image…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2409.11340/"&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>
