MIT News  September 27, 2023 Researchers at MIT have introduced a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). The models produced generative trajectories for N dimensional data by embedding paths in N+D dimensional space while still controlling the progression with a simple scalar norm of the D additional variables. The new model reduced to PFGM when D=1 and to diffusion models when D→∞. The flexibility of choosing D allowed them to trade off robustness against rigidity as increasing D resulted in more concentrated coupling between the data and the […]