  This paper introduces a novel theoretical simplification of the Diffusion
Schr\"odinger Bridge (DSB) that facilitates its unification with Score-based
Generative Models (SGMs), addressing the limitations of DSB in complex data
generation and enabling faster convergence and enhanced performance. By
employing SGMs as an initial solution for DSB, our approach capitalizes on the
strengths of both frameworks, ensuring a more efficient training process and
improving the performance of SGM. We also propose a reparameterization
technique that, despite theoretical approximations, practically improves the
network's fitting capabilities. Our extensive experimental evaluations confirm
the effectiveness of the simplified DSB, demonstrating its significant
improvements. We believe the contributions of this work pave the way for
advanced generative modeling. The code is available at
https://github.com/checkcrab/SDSB.
