Training Consistency Models with Variational Noise Coupling
Authors: name: Gianluigi Silvestri, name: Luca Ambrogioni, name: Chieh-Hsin Lai, name: Yuhta Takida, name: Yuki Mitsufuji

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 .