@inproceedings{
wang2024phased,
title={Phased Consistency Models},
author={Fu-Yun Wang and Zhaoyang Huang and Alexander William Bergman and Dazhong Shen and Peng Gao and Michael Lingelbach and Keqiang Sun and Weikang Bian and Guanglu Song and Yu Liu and Xiaogang Wang and Hongsheng Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mtBmKqyqGS}
}
Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory. In this paper, we identify three key flaws in the current design of Latent Consistency Models~(LCMs). We investigate the reasons behind these limitations and propose Phased Consistency Models (PCMs), which generalize the design space and address the identified limitations. Our evaluations demonstrate that PCMs outperform LCMs across 1--16 step generation settings. While PCMs are specifically designed for multi-step refinement, they achieve comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show the methodology of PCMs is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. Our code is available at https://github.com/G-U-N/Phased-Consistency-Model.
Phased Consistency Models
Fu-Yun Wang Zhaoyang Huang Alexander William Bergman Dazhong Shen Peng Gao Michael Lingelbach Keqiang Sun Weikang Bian Guanglu Song Yu Liu Xiaogang Wang Hongsheng Li