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DBMSolver: A Training-free Diffusion Bridge Sampler for High-Quality Image-to-Image Translation

2026-05-07 08:59:05
Sankarshana Venugopal (Seoul National University), Mohammad Mostafavi (Seoul National University), Jonghyun Choi (Seoul National University)
arXiv_AI
     
Abstract

Diffusion-based image-to-image (I2I) translation excels in high-fidelity generation but suffers from slow sampling in state-of-the-art Diffusion Bridge Models (DBMs), often requiring dozens of function evaluations (NFEs). We introduce DBMSolver, a training-free sampler that exploits the semi-linear structure of DBM's underlying SDE and ODE via exponential integrators, yielding highly-efficient 1st- and 2nd-order solutions. This reduces NFEs by up to 5x while boosting quality (e.g., FID drops 53% on DIODE at 20 NFEs vs. 2nd-order baseline). Experiments on inpainting, stylization, and semantics-to-image tasks across resolutions up to 256x256 show DBMSolver sets new SOTA efficiency-quality tradeoffs, enabling real-world applicability. Our code is publicly available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2605.05889

PDF

https://arxiv.org/pdf/2605.05889.pdf

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