Probability Flow ODE

Probability flow ordinary differential equations (ODEs) provide a deterministic approach to sampling from complex probability distributions, particularly within the context of score-based generative models. Current research focuses on improving the theoretical understanding of these ODEs, including analyzing convergence rates and error bounds under various assumptions, and developing efficient algorithms like Consistency Trajectory Models (CTMs) for faster and more controlled sampling. This work is significant because it offers a powerful alternative to stochastic sampling methods, enabling precise control over the generation process and facilitating tasks such as density estimation and image manipulation with improved efficiency and theoretical guarantees.