Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models

• URL: http://arxiv.org/abs/2601.06162v1
• Date: Tue, 06 Jan 2026 23:59:17 GMT
• Title: Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models
• Authors: Kaiyuan Deng, Gen Li, Yang Xiao, Bo Hui, Xiaolong Ma,
• Abstract summary: ScaPre is a unified framework tailored for large-scale unlearning.<n>It integrates spectral trace regularization and geometry alignment to stabilize optimization, suppress conflicts, and preserve global structure.<n>It forgets up to $times mathbf5$ more concepts than the best baseline within acceptable quality limits.
• Score: 17.91843469884079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image diffusion models have achieved remarkable progress, yet their use raises copyright and misuse concerns, prompting research into machine unlearning. However, extending multi-concept unlearning to large-scale scenarios remains difficult due to three challenges: (i) conflicting weight updates that hinder unlearning or degrade generation; (ii) imprecise mechanisms that cause collateral damage to similar content; and (iii) reliance on additional data or modules, creating scalability bottlenecks. To address these, we propose Scalable-Precise Concept Unlearning (ScaPre), a unified framework tailored for large-scale unlearning. ScaPre introduces a conflict-aware stable design, integrating spectral trace regularization and geometry alignment to stabilize optimization, suppress conflicts, and preserve global structure. Furthermore, an Informax Decoupler identifies concept-relevant parameters and adaptively reweights updates, strictly confining unlearning to the target subspace. ScaPre yields an efficient closed-form solution without requiring auxiliary data or sub-models. Comprehensive experiments on objects, styles, and explicit content demonstrate that ScaPre effectively removes target concepts while maintaining generation quality. It forgets up to $\times \mathbf{5}$ more concepts than the best baseline within acceptable quality limits, achieving state-of-the-art precision and efficiency for large-scale unlearning.

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