Phased Consistency Model
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/.
6656961985c7a647ebc7fe15
Fu-Yun Wang
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2024-05-29T07:29:03.884Z
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Fu-Yun Wang
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Zhaoyang Huang
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2024-05-29T08:11:35.154Z
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Zhaoyang Huang
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DrinkingCoder
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Alexander William Bergman
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2024-05-29T08:12:05.352Z
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Alexander Bergman
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Dazhong Shen
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2024-05-29T08:12:35.686Z
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Dazhong Sehn
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Peng Gao
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Michael Lingelbach
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2024-05-29T08:13:04.201Z
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Michael Lingelbach
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Keqiang Sun
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2024-05-29T08:13:10.164Z
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Keqiang Sun
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Weikang Bian
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2024-05-29T08:13:15.503Z
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Weikang BIAN
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Guanglu Song
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2024-05-29T08:13:24.387Z
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Song
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Guanglu
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Yu Liu
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2024-05-29T08:13:38.432Z
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Yu Liu
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Hongsheng Li
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Hongsheng LI
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Xiaogang Wang
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2024-05-29T08:13:55.560Z
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wangxiaogang
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wangxiaogang
Phased Consistency Model
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/.
The Phased Consistency Model (PCM) addresses limitations in Latent Consistency Models (LCM) and outperforms them in high-resolution, text-conditioned image and few-step text-to-video generation.
consistency model diffusion models latent space Latent Consistency Model Phased Consistency Model multi-step refinement text-to-video generator