Exploring the Design Space of Diffusion Bridge Models via Stochasticity Control

Shaorong Zhang, Yuanbin Cheng, Xianghao Kong, Greg Ver Steeg

Keywords: Diffusion bridge models, image translation, diffusion models

Abstract: Diffusion bridge models effectively facilitate image-to-image (I2I) translation by connecting two distributions. However, existing methods overlook the impact of noise in sampling SDEs, transition kernel, and the base distribution on sampling efficiency, image quality and diversity. To address this gap, we propose the Stochasticity-controlled Diffusion Bridge (SDB), a novel theoretical framework that extends the design space of diffusion bridges, and provides strategies to mitigate singularities during both training and sampling. By controlling stochasticity in the sampling SDEs, our sampler achieves speeds up to $5 \times$ faster than the baseline, while also producing lower FID scores. After training, SDB sets new benchmarks in image quality and sampling efficiency via managing stochasticity within the transition kernel. Furthermore, introducing stochasticity into the base distribution significantly improves image diversity, as quantified by a newly introduced metric.

Primary Area: generative models

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Submission Number: 8903