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

While multi-concept unlearning has shown progress, extending to large-scale scenarios remains difficult, as existing methods face three persistent challenges: (i) they often introduce conflicting weight updates, making some targets difficult to unlearn or causing degradation of generative capability; (ii) they lack precise mechanisms to keep unlearning strictly confined to target concepts, resulting in collateral damage on similar content; (iii) many approaches rely on additional data or auxiliary modules, causing scalability and efficiency bottlenecks as the number of concepts grows. To simultaneously address these challenges, we propose Scalable-Precise Concept Unlearning (ScaPre), a unified and lightweight framework tailored for scalable and precise large-scale unlearning. ScaPre introduces a conflict-aware stable design, which integrates the spectral trace regularizer and geometry alignment to stabilize the optimization space, suppress conflicting updates, and preserve the pretrained global structure. Furthermore, the Informax Decoupler identifies concept-relevant parameters and adaptively reweights updates, ensuring that unlearning is confined to the target subspace without collateral damage. ScaPre yields an efficient closed-form solution, requiring no additional data or auxiliary sub-models, while maintaining both scalability and precision. Comprehensive experiments across large-scale objects, styles, and explicit content benchmarks demonstrate that ScaPre effectively removes target concepts while maintaining generation quality. It can forget up to ×5 more concepts than the best baseline within the limits of acceptable generative quality, and outperforms existing multi-concept approaches in precision and efficiency, achieving a new state of the art for large-scale unlearning.