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

DBMSolver: A Training-free Diffusion Bridge Sampler for High-Quality Image-to-Image Translation
Sankarshana Venugopal, Mohammad Mostafavi, Jonghyun Choi
Seoul National University

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🚧 Repository Under Construction 🚧

We are finalizing the codebase and will release the full implementation of DBMSolver soon.

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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.

Citation If you find our work useful for your research, please consider citing:

@inproceedings{venugopal2026dbmsolver,
  title={DBMSolver: A Training-free Diffusion Bridge Sampler for High-Quality Image-to-Image Translation},
  author={Venugopal, Sankarshana and Mostafavi, Mohammad and Choi, Jonghyun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}