SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models to user-provided concepts (objects, people, or styles), but composing multiple such concepts remains challenging due to representation interference. Existing modular methods, usually built on low-rank adaptation (LoRA), either rely on expensive post-hoc fusion or freeze the LoRA adaptation subspaces, which limit expressiveness and concept fidelity. To address this trade-off, we propose Sequential regularized LoRA (SeqLoRA), a constrained continual learning framework that jointly optimizes both LoRA factors via bilevel optimization while keeping each new basis orthogonal to all previously learned...
| Authors | Javad Parsa, Enis Simsar, Amir Joudaki et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 30 September 2026 |
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