The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodinger Bridge (SB). Building on this framework, we introduce LightSBB-M, an algorithm that computes the optimal SBB transport plan in only a few iterations. The method exploits a dual representation of the SBB objective to obtain analytic expressions for the optimal drift and volatility, and it incorporates a tunable parameter beta greater than zero that interpolates between pure drift (the Schrodinger Bridge) and pure volatility (Bass martingale transport). We show that LightSBB-M achieves the lowest 2-Wasserstein distance on synthetic datasets against state-of-the-art SB and diffusion baselines with up to 32 percent improvement. We also illustrate the generative capability of the framework on an unpaired image-to-image translation task (adult to child faces in FFHQ). These findings demonstrate that LightSBB-M provides a scalable, high-fidelity SBB solver that outperforms existing SB and diffusion baselines across both synthetic and real-world generative tasks. The code is available at https://github.com/alexouadi/LightSBB-M.

翻译：薛定谔桥与巴斯过程（SBB）公式通过联合控制漂移项与波动率，是经典薛定谔桥（SB）的既定扩展。基于此框架，我们提出了LightSBB-M算法，该算法仅需数次迭代即可计算出最优的SBB传输方案。该方法利用SBB目标函数的对偶表示来获得最优漂移项与波动率的解析表达式，并引入一个大于零的可调参数β，该参数可在纯漂移（薛定谔桥）与纯波动率（巴斯鞅传输）之间进行插值。实验表明，在合成数据集上，LightSBB-M相较于最先进的SB与扩散基线方法取得了最低的2-瓦瑟斯坦距离，提升幅度最高达32%。我们还在非配对图像到图像转换任务（FFHQ数据集中成人到儿童的面部转换）上展示了该框架的生成能力。这些结果表明，LightSBB-M提供了一个可扩展、高保真的SBB求解器，在合成与真实世界生成任务中均优于现有的SB与扩散基线方法。代码发布于https://github.com/alexouadi/LightSBB-M。