Combining multiple custom concepts in AI images without interference

Personalizing text-to-image models with multiple custom concepts—say, a specific face and a specific style together—causes interference: the model forgets one while learning the other. SeqLoRA solves this with a constrained optimization approach that learns both parts of the adapter simultaneously, avoiding the cost of post-hoc fusion or the limits of frozen methods. Testing on up to 101 concepts shows better identity preservation and fewer unwanted attribute leakage than existing approaches.