Continual Concept Personalization in Diffusion Models via Residual-Space Adaptation

Yuzhi Liu, Enis Simsar, Giulia Lanzillotta, Thomas Hofmann · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28567 · open-access PDF

Abstract

Text-to-image personalization aims to equip pretrained diffusion models with the ability to learn user-specific concepts from only a few reference images. Existing methods, however, typically train each concept independently or assume that all concepts are available simultaneously. In practical applications, concepts often arrive sequentially, requiring models to continually learn new subjects, objects, or styles while minimizing interference with previously acquired concepts. We address this continual concept personalization setting by extending residual-space adaptation to the stochastic ada

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