Jinxin Zhou1,*
Tianyu Ding2,*,†
Tianyi Chen2
Jiachen Jiang2
Ilya Zharkov2
Zhihui Zhu1
Luming Liang2,†
1Ohio State University 2Microsoft
CVPR 2024
Project Page | Paper
We present DREAM, a novel training framework representing Diffusion Rectification and Estimation-Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification, which adjusts training to reflect the sampling process, and estimation adaptation, which balances perception against distortion. When applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff between minimizing distortion and preserving high image quality. Experiments demonstrate DREAM's superiority over standard diffusion-based SR methods, showing a 2 to 3x faster training convergence and a 10 to 20x reduction in necessary sampling steps to achieve comparable or superior results. We hope DREAM will inspire a rethinking of diffusion model training paradigms.
If you find our work helpful, please kindly cite our work:
@article{zhou2023dream,
title={DREAM: Diffusion Rectification and Estimation-Adaptive Models},
author={Zhou, Jinxin and Ding, Tianyu and Chen, Tianyi and Jiang, Jiachen and Zharkov, Ilya and Zhu, Zhihui and Liang, Luming},
journal={arXiv preprint arXiv:2312.00210},
year={2023}
}
or
@InProceedings{Zhou_2024_CVPR,
author = {Zhou, Jinxin and Ding, Tianyu and Chen, Tianyi and Jiang, Jiachen and Zharkov, Ilya and Zhu, Zhihui and Liang, Luming},
title = {DREAM: Diffusion Rectification and Estimation-Adaptive Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {8342-8351}
}