This paper introduces the Latent Schr{ö}dinger Bridge Diffusion Model, a progressive generative learning technique. Here are the highlights:

• Utilization of a latent space diffusion model grounded in the Schrödinger bridge framework.
• Initial pre-training on encoder-decoder architecture with divergent data distribution.
• Robust theoretical backing to handle sample sizes through existing large models.
• Effective control of second-order Wasserstein distance for precise distribution modeling. Further investigations could harness this model for complex multi-modal data sets, providing a potentially pivotal tool for advanced AI applications in various sectors.