🤖 AI Summary
This work addresses the high computational cost of conventional score-based diffusion models, which typically initialize the reverse process from a Gaussian distribution and require a large number of discrete sampling steps. The authors theoretically demonstrate, for the first time, that the initialization of the reverse process critically influences sampling efficiency. They propose a learnable initialization strategy that is agnostic to the underlying model architecture, network design, and discretization scheme, directly minimizing the initial Kullback–Leibler (KL) divergence error. Grounded in KL convergence analysis and dynamical modeling of Variance Exploding (VE) diffusion processes, the method achieves comparable or superior generation quality on both synthetic data and standard benchmarks using significantly fewer sampling steps.
📝 Abstract
Score-based generative models (SGMs) aim at generating samples from a target distribution by approximating the reverse-time dynamics of a stochastic differential equation. Despite their strong empirical performance, classical samplers initialized from a Gaussian distribution require a long time horizon noising typically inducing a large number of discretization steps and high computational cost. In this work, we present a Kullback-Leibler convergence analysis of Variance Exploding diffusion samplers that highlights the critical role of the backward process initialization. Based on this result, we propose a theoretically grounded sampling strategy that learns the reverse-time initialization, directly minimizing the initialization error. The resulting procedure is independent of the specific score training procedure, network architecture, and discretization scheme. Experiments on toy distributions and benchmark datasets demonstrate competitive or improved generative quality while using significantly fewer sampling steps.