A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control

The introduction of UniDB marks a pivotal advancement in diffusion bridge models, which have shown promise in image translation and restoration tasks but often suffer from issues like blurred details. By leveraging Stochastic Optimal Control (SOC), UniDB reformulates the optimization problem, allowing for a tunable terminal penalty coefficient that balances control costs and penalties effectively. This innovation not only improves the quality of output images but also circumvents the computational burdens associated with traditional iterative sampling methods. The framework's ability to enhance detail preservation while maintaining efficiency positions it as a noteworthy contribution to the field of artificial intelligence, particularly in applications requiring high-quality image processing.