Continual Personalization for Diffusion Models

Updating diffusion models in an incremental setting would be practical in real-world applications yet computationally challenging. We present a novel learning strategy of $\textbf{C}$oncept $\textbf{N}$euron $\textbf{S}$election, a simple yet effective approach to perform personalization in a continual learning scheme. $\textbf{CNS}$ uniquely identifies neurons in diffusion models that are closely related to the target concepts. In order to mitigate catastrophic forgetting problems while preserving zero-shot text-to-image generation ability, $\textbf{CNS}$ finetunes concept neurons in an incremental manner and jointly preserves knowledge learned of previous concepts. Evaluation of real-world datasets demonstrates that $\textbf{CNS}$ achieves state-of-the-art performance with minimal parameter adjustments, outperforming previous methods in both single and multi-concept personalization works. $\textbf{CNS}$ also achieves fusion-free operation, reducing memory storage and processing time for continual personalization.