When fine-tuning diffusion models for multiple concepts, jointly optimizing LoRA factors with orthogonal constraints prevents representation interference and scales better than existing modular approaches—enabling cleaner composition of up to 101 concepts.
SeqLoRA improves how AI models learn multiple custom concepts at once by using a smarter optimization technique that prevents concepts from interfering with each other. Instead of freezing parts of the model or doing expensive post-processing, it jointly trains the adaptation components while keeping them orthogonal, enabling better multi-concept image generation with less computational cost.