title: Training Consistency Models with Variational Noise Coupling
summary: Consistency Training (CT) has recently emerged as a promising alternative to
  diffusion models, achieving competitive performance in image generation tasks.
  However, non-distillation consistency training often suffers from high variance
  and instability, and analyzing and improving its training dynamics is an active
  area of research. In this work, we propose a novel CT training approach based
  on the Flow Matching framework. Our main contribution is a trained
  noise-coupling scheme inspired by the architecture of Variational Autoencoders
  (VAE). By training a data-dependent noise emission model implemented as an
  encoder architecture, our method can indirectly learn the geometry of the
  noise-to-data mapping, which is instead fixed by the choice of the forward
  process in classical CT. Empirical results across diverse image datasets show
  significant generative improvements, with our model outperforming baselines and
  achieving the state-of-the-art (SoTA) non-distillation CT FID on CIFAR-10, and
  attaining FID on par with SoTA on ImageNet at 64 times 64 resolution in
  2-step generation. Our code is available at https://github.com/sony/vct .
paper_authors._id: 67c07fa2a43d7939d6d90d54
paper_authors.name: Gianluigi Silvestri
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T22:09:04.844Z
paper_authors._id: 67c07f498589d8ecb7912686
paper_authors.avatarUrl: /avatars/84e77389c211a7c4237f73208658c23a.svg
paper_authors.fullname: Gianluigi Silvestri
paper_authors.type: user
paper_authors.user: gisilvs
paper_authors._id: 67c07fa2a43d7939d6d90d55
paper_authors.name: Luca Ambrogioni
paper_authors._id: 67c07fa2a43d7939d6d90d56
paper_authors.name: Chieh-Hsin Lai
paper_authors._id: 67c07fa2a43d7939d6d90d57
paper_authors.name: Yuhta Takida
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T15:15:50.340Z
paper_authors._id: 66138c4074f830bc7d9d6622
paper_authors.avatarUrl: /avatars/d50da6d7597d3bcf63f9f0c74e910155.svg
paper_authors.fullname: Yuhta Takida
paper_authors.type: user
paper_authors.user: ytakida
paper_authors._id: 67c07fa2a43d7939d6d90d58
paper_authors.name: Yuki Mitsufuji
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T15:15:56.987Z
paper_authors._id: 665e32384ecc8a7181634f6d
paper_authors.avatarUrl: /avatars/8752f952010540d14f45eac849e91371.svg
paper_authors.fullname: Yuki Mitsufuji
paper_authors.type: user
paper_authors.user: mittu1204
paper_title: Training Consistency Models with Variational Noise Coupling
paper_summary: Consistency Training (CT) has recently emerged as a promising alternative to
  diffusion models, achieving competitive performance in image generation tasks.
  However, non-distillation consistency training often suffers from high variance
  and instability, and analyzing and improving its training dynamics is an active
  area of research. In this work, we propose a novel CT training approach based
  on the Flow Matching framework. Our main contribution is a trained
  noise-coupling scheme inspired by the architecture of Variational Autoencoders
  (VAE). By training a data-dependent noise emission model implemented as an
  encoder architecture, our method can indirectly learn the geometry of the
  noise-to-data mapping, which is instead fixed by the choice of the forward
  process in classical CT. Empirical results across diverse image datasets show
  significant generative improvements, with our model outperforming baselines and
  achieving the state-of-the-art (SoTA) non-distillation CT FID on CIFAR-10, and
  attaining FID on par with SoTA on ImageNet at 64 times 64 resolution in
  2-step generation. Our code is available at https://github.com/sony/vct .
paper_ai_summary: A novel consistency training approach using Flow Matching with a noise-coupling scheme inspired by VAEs achieves state-of-the-art performance in image generation tasks.
paper_ai_keywords: Flow Matching noise-coupling Variational Autoencoders noise emission model encoder architecture noise-to-data mapping