Flow Sampling enables efficient sampling from unnormalized densities by reversing the diffusion process with energy guidance, making it practical for expensive-to-evaluate energy functions and non-Euclidean geometries.
This paper presents Flow Sampling, a method for drawing samples from energy functions without needing data. It adapts diffusion models to work backwards from noise, using the energy function to guide the sampling process. The approach is efficient because it minimizes how many times the energy function must be evaluated, and it works on curved spaces like spheres and hyperbolic geometry.