Continual Learning of Diffusion Models with Generative Distillation
CoLLAs • 2024
We propose generative distillation, an approach that distils the entire reverse process of a diffusion model, substantially improving continual learning performance of generative replay for diffusion models at only a modest extra compute cost.
Diffusion models are powerful generative models that achieve state-of-the-art performance in image synthesis. However, training them demands substantial amounts of data and computational resources. Continual learning would allow for incrementally learning new tasks and accumulating knowledge, thus enabling the reuse of trained models for further learning.
One potentially suitable continual learning approach is generative replay, where a copy of a generative model trained on previous tasks produces synthetic data that are interleaved with data from the current task. However, standard generative replay applied to diffusion models results in a catastrophic loss in denoising capabilities.
Our approach
In this paper, we propose generative distillation, an approach that distils the entire reverse process of a diffusion model.
Our approach substantially improves the continual learning performance of generative replay with only a modest increase in the computational costs.
Citation
@InProceedings{pmlr-v274-masip25a,
title = {Continual Learning of Diffusion Models with Generative Distillation},
author = {Masip, Sergi and Rodriguez, Pau and Tuytelaars, Tinne and Ven, Gido M van de},
booktitle = {Proceedings of The 3rd Conference on Lifelong Learning Agents},
pages = {431--456},
year = {2025},
editor = {Lomonaco, Vincenzo and Melacci, Stefano and Tuytelaars, Tinne and Chandar, Sarath and Pascanu, Razvan},
volume = {274},
series = {Proceedings of Machine Learning Research},
month = {29 Jul--01 Aug},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v274/masip25a.html}
}