Exploring Continual Learning of Diffusion Models

Understand

Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data.

• However, training a diffusion model from scratch is computationally expensive.
• This highlights the need to investigate the possibility of training these models iteratively, reusing computation while the data distribution changes.
• In this study, we take the first step in this direction and evaluate the continual learning (CL) properties of diffusion models.