Training Consistency Models with Variational Noise Coupling
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 .
67c07fa2a43d7939d6d90d54
Gianluigi Silvestri
claimed_verified
2025-02-27T22:09:04.844Z
67c07f498589d8ecb7912686
/avatars/84e77389c211a7c4237f73208658c23a.svg
Gianluigi Silvestri
user
gisilvs
67c07fa2a43d7939d6d90d55
Luca Ambrogioni
67c07fa2a43d7939d6d90d56
Chieh-Hsin Lai
67c07fa2a43d7939d6d90d57
Yuhta Takida
admin_assigned
2025-02-28T15:15:50.340Z
66138c4074f830bc7d9d6622
/avatars/d50da6d7597d3bcf63f9f0c74e910155.svg
Yuhta Takida
user
ytakida
67c07fa2a43d7939d6d90d58
Yuki Mitsufuji
admin_assigned
2025-02-28T15:15:56.987Z
665e32384ecc8a7181634f6d
/avatars/8752f952010540d14f45eac849e91371.svg
Yuki Mitsufuji
user
mittu1204
Training Consistency Models with Variational Noise Coupling
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 .
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.
Flow Matching noise-coupling Variational Autoencoders noise emission model encoder architecture noise-to-data mapping