Is Noise Conditioning Necessary for Denoising Generative Models?
Qiao Sun, Zhicheng Jiang, Hanhong Zhao, Kaiming He
Abstract
It is widely believed that noise conditioning is indispensable for denoising diffusion models to work successfully. This work challenges this belief. Motivated by research on blind image denoising, we investigate a variety of denoising-based generative models in the absence of noise conditioning. To our surprise, most models exhibit graceful degradation, and in some cases, they even perform better without noise conditioning. We provide a mathematical analysis of the error introduced by removing noise conditioning and demonstrate that our analysis aligns with empirical observations. We further introduce a noise-*unconditional* model that achieves a competitive FID of 2.23 on CIFAR-10, significantly narrowing the gap to leading noise-conditional models. We hope our findings will inspire the community to revisit the foundations and formulations of denoising generative models.
BibTeX
@inproceedings{
sun2025is,
title={Is Noise Conditioning Necessary for Denoising Generative Models?},
author={Qiao Sun and Zhicheng Jiang and Hanhong Zhao and Kaiming He},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=pTSWi6RTtJ}
}