title: Stable Consistency Tuning: Understanding and Improving Consistency
    Models
summary: Diffusion models achieve superior generation quality but suffer from slow
  generation speed due to the iterative nature of denoising. In contrast,
  consistency models, a new generative family, achieve competitive performance
  with significantly faster sampling. These models are trained either through
  consistency distillation, which leverages pretrained diffusion models, or
  consistency training/tuning directly from raw data. In this work, we propose a
  novel framework for understanding consistency models by modeling the denoising
  process of the diffusion model as a Markov Decision Process (MDP) and framing
  consistency model training as the value estimation through Temporal
  Difference~(TD) Learning. More importantly, this framework allows us to analyze
  the limitations of current consistency training/tuning strategies. Built upon
  Easy Consistency Tuning (ECT), we propose Stable Consistency Tuning (SCT),
  which incorporates variance-reduced learning using the score identity. SCT
  leads to significant performance improvements on benchmarks such as CIFAR-10
  and ImageNet-64. On ImageNet-64, SCT achieves 1-step FID 2.42 and 2-step FID
  1.55, a new SoTA for consistency models.
paper_authors._id: 671af6c26e2ed37782064466
paper_authors.name: Fu-Yun Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-25T09:30:18.737Z
paper_authors._id: 63e9e92f20c109718713f5eb
paper_authors.avatarUrl: /avatars/9ff312e854d803e1a2e9e685a21d12f8.svg
paper_authors.fullname: Fu-Yun Wang
paper_authors.type: user
paper_authors.user: wangfuyun
paper_authors._id: 671af6c26e2ed37782064467
paper_authors.name: Zhengyang Geng
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-25T01:39:16.932Z
paper_authors._id: 626039656089b62d9f332854
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1650473291256-noauth.jpeg
paper_authors.fullname: Zhengyang Geng
paper_authors.type: user
paper_authors.user: Gsunshine
paper_authors._id: 671af6c26e2ed37782064468
paper_authors.name: Hongsheng Li
paper_title: Stable Consistency Tuning: Understanding and Improving Consistency
    Models
paper_summary: Diffusion models achieve superior generation quality but suffer from slow
  generation speed due to the iterative nature of denoising. In contrast,
  consistency models, a new generative family, achieve competitive performance
  with significantly faster sampling. These models are trained either through
  consistency distillation, which leverages pretrained diffusion models, or
  consistency training/tuning directly from raw data. In this work, we propose a
  novel framework for understanding consistency models by modeling the denoising
  process of the diffusion model as a Markov Decision Process (MDP) and framing
  consistency model training as the value estimation through Temporal
  Difference~(TD) Learning. More importantly, this framework allows us to analyze
  the limitations of current consistency training/tuning strategies. Built upon
  Easy Consistency Tuning (ECT), we propose Stable Consistency Tuning (SCT),
  which incorporates variance-reduced learning using the score identity. SCT
  leads to significant performance improvements on benchmarks such as CIFAR-10
  and ImageNet-64. On ImageNet-64, SCT achieves 1-step FID 2.42 and 2-step FID
  1.55, a new SoTA for consistency models.
paper_ai_summary: A novel framework understanding consistency models through MDP and TD Learning enhances performance with Stable Consistency Tuning, achieving state-of-the-art results.
paper_ai_keywords: diffusion models consistency models consistency distillation consistency training Markov Decision Process Temporal Difference Learning Easy Consistency Tuning Stable Consistency Tuning variance-reduced learning score identity CIFAR-10 ImageNet-64 FID