A Unified Framework for Diffusion Model Unlearning with f-Divergence

• URL: http://arxiv.org/abs/2509.21167v1
• Date: Thu, 25 Sep 2025 13:51:04 GMT
• Title: A Unified Framework for Diffusion Model Unlearning with f-Divergence
• Authors: Nicola Novello, Federico Fontana, Luigi Cinque, Deniz Gunduz, Andrea M. Tonello,
• Abstract summary: Unlearning methods for text-to-image (T2I) models often rely on minimizing the mean squared error (MSE) between the output distribution of a target and an anchor concept.<n>We show that this MSE-based approach is a special case of a unified $f$-divergence-based framework, in which any $f$-divergence can be utilized.
• Score: 15.819330608656337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning aims to remove specific knowledge from a trained model. While diffusion models (DMs) have shown remarkable generative capabilities, existing unlearning methods for text-to-image (T2I) models often rely on minimizing the mean squared error (MSE) between the output distribution of a target and an anchor concept. We show that this MSE-based approach is a special case of a unified $f$-divergence-based framework, in which any $f$-divergence can be utilized. We analyze the benefits of using different $f$-divergences, that mainly impact the convergence properties of the algorithm and the quality of unlearning. The proposed unified framework offers a flexible paradigm that allows to select the optimal divergence for a specific application, balancing different trade-offs between aggressive unlearning and concept preservation.

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