Inverse Bridge Matching Distillation
Authors: name: Nikita Gushchin, name: David Li, name: Daniil Selikhanovych, name: Evgeny Burnaev, name: Dmitry Baranchuk, name: Alexander Korotin

Learning diffusion bridge models is easy; making 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 practice. Unlike previously developed DBM distillation techniques,
the proposed method can distill 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 quality than used teacher model
depending on particular setup.