Inverse Bridge Matching Distillation (2502.01362v1)

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.

Inverse Bridge Matching Distillation: A Technical Overview

The paper "Inverse Bridge Matching Distillation" introduces a novel distillation method aimed at enhancing the efficiency of Diffusion Bridge Models (DBMs), focusing particularly on reducing the time complexity and broadening their applicability. DBMs are influential in data-to-data translation tasks such as image-to-image translation, but suffer from slow inference speeds akin to other diffusion and flow models. This research proposes the Inverse Bridge Matching Distillation (IBMD) method to accelerate DBMs, generating results that suggest speed improvements up to 100 times faster than traditional methods while also improving output quality under certain configurations.

DBMs are distinctive in that they construct diffusion processes directly between two data distributions, differing from classical diffusion models which usually involve noise mapping to data. They have been applied widely, not only in image processing but across diverse fields such as audio and biological data modeling. However, their practical adoption is hindered by their requirement for lengthy inference phases, making efficient acceleration methods necessary.

Methodology

The core innovation of this paper lies in reformulating the problem of optimizing diffusion bridges through an inverse bridge matching problem. The authors propose an optimization framework that seeks to minimize the Kullback-Leibler (KL) divergence between a mixture of bridges (constructed processes from DBMs) and the desired model output, effectively reducing the number of inference steps needed for satisfactory results. This method universally applies to both conditional and unconditional DBM types, a limitation not effectively addressed by previous acceleration techniques.

The research introduces a tractable objective to replace the constrained optimization problem originally formulated, allowing for efficient gradient-based optimization techniques. By applying a reparameterization strategy, the authors achieve a framework that only requires corrupted image data for training, avoiding reliance on paired data typically used in bridge training.

Results

Empirical evaluations demonstrate significant performance improvements across several image-to-image translation tasks such as super-resolution, JPEG restoration, sketch-to-image translation, and more. Notably, the IBMD approach surpassed existing methods with metrics showing FID (Fréchet Inception Distance) scores as low as 2.5 for super-resolution tasks, outperforming other advanced methods like ADM and I²SB.

The enhanced model exhibits robustness and adaptability in generating high-quality images with minimal NFE (Number of Function Evaluations), highlighting its potential for application in real-world scenarios where rapid inference and output quality are critical. The experiments conducted reveal IBMD's capability to offer substantial performance benefits while maintaining or even exceeding the quality of traditional methods.

Implications and Future Work

This research opens avenues for broader applications of DBMs by overcoming one of their primary limitations— slow inference times. The development of the IBMD method suggests that similar efficiency improvements could be applied to other classes of generative models, potentially influencing future explorations in model distillation and diffusion-based deep learning.

Furthermore, this research sets a precedent for the potential fusion of generative modeling with specific computational optimizations that tailor model inference to be both expedient and resource-effective. As AI systems continue to handle increasingly complex data translation tasks, the methodologies explored here could become integral to future advances in AI model design.

In terms of future developments, there are prospects for exploring the application of this method in more diverse and complex data settings beyond the current scope. Additional research may further refine the optimization framework to maximize its benefits across varying model architectures and application domains. Moreover, insights from this work may inspire parallel strategies in optimizing other types of diffusion models, reinforcing the broader impact of this paper on the landscape of machine learning and AI research.