Abstract
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.
Abstract (translated)
学习扩散桥模型（Diffusion Bridge Models，DBMs）很简单；但要使它们变得快速且实用则是一门艺术。扩散桥模型是扩散模型在图像到图像转换应用中的一个有前景的扩展。然而，与许多现代的扩散和流模型一样，DBMs面临着推理速度慢的问题。为了解决这个问题，我们提出了一种基于逆向桥匹配公式的新颖蒸馏技术，并推导出实用的目标函数来解决这一问题。 不同于之前开发的DBM蒸馏技术，我们的方法可以同时对条件性和非条件性的DBMs进行蒸馏，在一步生成器中训练模型，并仅使用损坏的图像进行训练。我们在一系列广泛的设置上评估了我们这种方法在条件性和非条件性桥匹配上的表现，包括超分辨率、JPEG恢复、草图到图像转换以及其他任务，结果显示我们的蒸馏技术可以将DBM的推理速度提高4倍至100倍不等，并且在某些情况下甚至能提供比原教师模型更好的生成质量。
URL
https://arxiv.org/abs/2502.01362
