title: Phased Consistency Model
summary: 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/.
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paper_authors.name: Peng Gao
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paper_authors.name: Michael Lingelbach
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paper_authors.fullname: Michael Lingelbach
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paper_authors.name: Keqiang Sun
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paper_authors.fullname: Keqiang Sun
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paper_authors.name: Weikang Bian
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paper_authors.fullname: Weikang BIAN
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paper_authors.name: Guanglu Song
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paper_authors.name: Yu Liu
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paper_authors.fullname: Yu Liu
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paper_authors.name: Xiaogang Wang
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paper_title: Phased Consistency Model
paper_summary: 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/.
paper_ai_summary: 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.
paper_ai_keywords: consistency model diffusion models latent space Latent Consistency Model Phased Consistency Model multi-step refinement text-to-video generator