Continual Conceptual Entity Learning for Text-to-Image Generative Models

Current Text-to-Image generative models struggle to continuously learn multiple distinct entities or concepts, limiting their scalability and hindering practical deployment in dynamic environments. We formulate this task as Continual Conceptual Entity Learning (CEL) and propose a novel framework called Continual Entity Adapter Learning (CEAL). CEAL leverages a compact set of tunable parameters, termed SuperLoRA, to efficient and scalable learning of new entities. We propose a dynamic rank-increasing strategy to train the SuperLoRA, balancing computational efficiency with performance. To evaluate our method, we create three benchmarks encompassing generic objects, human faces, and artistic styles. Experimental results demonstrate that CEAL effectively learns new entities while preserving prior knowledge, outperforming existing methods in both entity fidelity and parameter efficiency.