Information-theoretic scaling laws for diffusion models predict when memorization begins
LucaAmb · x · 2026-10-29
Luca Ambrogioni, Druv Pai and Yi Ma published work on information-theoretic scaling laws for diffusion models, giving a rigorous characterization of generation vs. memorization in denoising-based distribution learning from finite samples. By quantifying how much information a model learns from data, they predict when memorization begins and how FID scales with training. Yi Ma notes the biggest surprise: the theoretically sound information measure turns out surprisingly efficient to evaluate despite its daunting appearance.
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