SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation

Excerpt

arXiv:2605.22743v2 Announce Type: replace Abstract: 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