Purify Before You Align: filtering locally predictable info improves diffusion alignment
volokuleshov · x · 2026-10-21
New work from Cornell PhD student Yingheng Wang improves representation alignment (REPA) in diffusion models with a counterintuitive finding: more information isn't always better.
The method removes locally predictable information before aligning representations to a subset of image content, providing both empirical and information-theoretic evidence for why 'purifying before aligning' is the right choice.
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