ICML 2025

VCT: Training Consistency Models with Variational Noise Coupling
Gianluigi Silvestri∗1 2 Luca Ambrogioni 2 Chieh-Hsin Lai 3 Yuhta Takida 3 Yuki Mitsufuji 3 4
Abstract
Consistency Training (CT) has recently emerged
as a strong alternative to diffusion models for im-
age generation.
However, non-distillation CT
often suffers from high variance and instabil-
ity, motivating ongoing research into its training
dynamics. We propose Variational Consistency
Training (VCT), a flexible and effective frame-
work compatible with various forward kernels,
including those in flow matching. Its key inno-
vation is a learned noise-data coupling scheme
inspired by Variational Autoencoders, where a
data-dependent encoder models noise emission.
This enables VCT to adaptively learn noise-to-
data pairings, reducing training variance relative
to the fixed, unsorted pairings in classical CT. Ex-
periments on multiple image datasets demonstrate
significant improvements: our method surpasses
baselines, achieves state-of-the-art FID among
non-distillation CT approaches on CIFAR-10, and
matches SoTA performance on ImageNet 64 × 64
with only two sampling steps. Code is available
at https://github.com/sony/vct.
1. Introduction
Generative Models are deep learning algorithms designed
to learn the underlying probability distribution of a given
dataset, in order to then generate samples coming from such
a distribution. Some widely used models are Generative
Adversarial Networks (Goodfellow et al., 2014), Variational
Autoencoders (VAE; Kingma & Welling, 2013; Rezende
et al., 2014), and Normalizing Flows (Chen et al., 2018;
Papamakarios et al., 2021; Kobyzev et al., 2020). More
∗Work done during an internship at Sony AI 1OnePlanet Re-
search Center, imec-the Netherlands, Wageningen, the Nether-
lands 2Donders Institute for Brain, Cognition and Behaviou, Ni-
jmegen, the Netherlands 3Sony AI, Tokyo, Japan 4Sony Group
Corporation.
Correspondence to: Gianluigi Silvestri <gian-
luigi.silvestri@imec.nl, gi...