Continual Diffusion

Continual diffusion focuses on adapting generative diffusion models, particularly those used for image generation, to sequentially learn new concepts without forgetting previously acquired knowledge. Current research emphasizes developing efficient and scalable architectures, such as low-rank adapters and attention-masked methods, to address the "catastrophic forgetting" problem inherent in continual learning. This area is significant because it enables the creation of more adaptable and robust AI systems capable of continuously learning and improving from diverse data streams, with applications ranging from personalized image generation to more general continual learning tasks. Improved latent space smoothness within these models is also a key area of focus, enhancing downstream tasks like image editing and manipulation.