ICML 2025

Inverse Bridge Matching Distillation
Nikita Gushchin 1 2 David Li * 1 3 Daniil Selikhanovych * 1 4 5 Evgeny Burnaev 1 2 Dmitry Baranchuk 4
Alexander Korotin 1 2
Abstract
Learning diffusion bridge models is easy; mak-
ing them fast and practical is an art. Diffusion
bridge models (DBMs) are a promising extension
of diffusion models for applications in image-to-
image translation. However, like many modern
diffusion and flow models, DBMs suffer from
the problem of slow inference. To address it,
we propose a novel distillation technique based
on the inverse bridge matching formulation and
derive the tractable objective to solve it in prac-
tice. Unlike previously developed DBM distil-
lation techniques, the proposed method can dis-
till both conditional and unconditional types of
DBMs, distill models in a one-step generator, and
use only the corrupted images for training. We
evaluate our approach for both conditional and
unconditional types of bridge matching on a wide
set of setups, including super-resolution, JPEG
restoration, sketch-to-image, and other tasks, and
show that our distillation technique allows us to
accelerate the inference of DBMs from 4x to
100x and even provide better generation qual-
ity than used teacher model depending on par-
ticular setup. We provide the code at https:
//github.com/ngushchin/IBMD.
1. Introduction
Diffusion Bridge Models (DBMs) represent a specialized
class of diffusion models designed for data-to-data tasks,
such as image-to-image translation. Unlike standard diffu-
sion models, which operate by mapping noise to data (Ho
et al., 2020; Sohl-Dickstein et al., 2015), DBMs construct
diffusion processes directly between two data distributions
(Peluchetti, 2023a; Liu et al., 2022b; Somnath et al., 2023;
*Equal contribution 1Skolkovo Institute of Science and Technol-
ogy 2Artificial Intelligence Research Institute 3Moscow Institute
of Physics and Technology 4Yandex Research 5HSE University.
Correspondence to: Nikita Gushchin <n.gushchi...