Phased Consistency Models

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.

Authors

• Fu-Yun Wang
• Zhaoyang Huang
• Alexander W. Bergman
• Dazhong Shen
• Peng Gao
• Michael Lingelbach Stanford University
• Keqiang Sun Multimedia Laboratory, The Chinese University of Hong Kong
• Weikang Bian
• Guanglu Song
• Yu Liu
• Xiaogang Wang The Chinese University of Hong Kong
• Hongsheng Li Multimedia Laboratory The Chinese University of Hong Kong, Centre for Perceptual and Interactive Intelligence

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