Information-theoretic scaling laws predict memorization and FID in diffusion models
LucaAmb · x · 2026-10-29
A new paper (with Druv Pai and Yi Ma) introduces an information-theoretic perspective on diffusion models. By quantifying how much information a model learns from the training data, the authors show you can predict:
- When memorization begins: once the model's information dependence on the training set crosses a threshold, generated samples start replicating training data.
- How generation quality scales: FID follows a predictive scaling law with learned information.
The driving question: can we predict memorization vs. generalization and sample quality by measuring how much generated samples depend on the training dataset? The paper answers yes, offering a new diagnostic tool for diffusion models.
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