Phased Consistency Model
Authors: name: Fu-Yun Wang, name: Zhaoyang Huang, name: Alexander William Bergman, name: Dazhong Shen, name: Peng Gao, name: Michael Lingelbach, name: Keqiang Sun, name: Weikang Bian, name: Guanglu Song, name: Yu Liu, name: Hongsheng Li, name: Xiaogang Wang

The consistency model (CM) has recently made significant progress in
accelerating the generation of diffusion models. However, its application to
high-resolution, text-conditioned image generation in the latent space (a.k.a.,
LCM) remains unsatisfactory. In this paper, we identify three key flaws in the
current design of LCM. We investigate the reasons behind these limitations and
propose the Phased Consistency Model (PCM), which generalizes the design space
and addresses all identified limitations. Our evaluations demonstrate that PCM
significantly outperforms LCM across 1--16 step generation settings. While PCM
is specifically designed for multi-step refinement, it achieves even superior
or comparable 1-step generation results to previously state-of-the-art
specifically designed 1-step methods. Furthermore, we show that PCM's
methodology is versatile and applicable to video generation, enabling us to
train the state-of-the-art few-step text-to-video generator. More details are
available at https://g-u-n.github.io/projects/pcm/.