HOW DIFFUSION MODELS MEMORIZE

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Paper Summary

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Diffusion Models: Why They Can't Forget Your Face (and What Over-Confidence Has To Do With It!)

This paper uncovers that diffusion models memorize training data not just due to overfitting, but primarily because of "early overestimation" of training samples during denoising, driven by classifier-free guidance. This overestimation amplifies the training image's contribution, suppressing initial randomness and causing generated images to converge rapidly to memorized content. The severity of memorization directly correlates with these deviations from the theoretical denoising schedule.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Scope of Models Studied

While the paper tests Stable Diffusion v1.4, v2.1, and RealisticVision, the findings are not explicitly verified across the entire spectrum of diffusion models or other generative architectures, though the theoretical framework suggests broader applicability.

Reliance on a Single Memorization Metric

The paper primarily uses SSCD for quantifying memorization. While stated as a strong detector, relying on one metric might overlook nuances or alternative forms of memorization.

Focus on Explanation, Not Mitigation

The paper focuses on explaining *how* memorization occurs rather than proposing a novel mitigation technique, though it provides theoretical justification for existing mitigation strategies.

Rating Explanation

This paper provides a groundbreaking, fundamental explanation for a critical problem in generative AI—memorization in diffusion models. It rigorously challenges existing assumptions about overfitting, offering detailed empirical evidence and theoretical derivations for the mechanism of "early overestimation." The findings are highly significant for advancing our understanding of these models and paving the way for safer generative systems.

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