Improving Diffusion-Based Generative Models via Approximated Optimal
  Transport
Daegyu Kim Jooyoung Choi Chaehun Shin Uiwon Hwang Sungroh Yoon
We introduce the Approximated Optimal Transport (AOT) technique, a novel
training scheme for diffusion-based generative models. Our approach aims to
approximate and integrate optimal transport into the training process,
significantly enhancing the ability of diffusion models to estimate the
denoiser outputs accurately. This improvement leads to ODE trajectories of
diffusion models with lower curvature and reduced truncation errors during
sampling. We achieve superior image quality and reduced sampling steps by
employing AOT in training. Specifically, we achieve FID scores of 1.88 with
just 27 NFEs and 1.73 with 29 NFEs in unconditional and conditional
generations, respectively. Furthermore, when applying AOT to train the
discriminator for guidance, we establish new state-of-the-art FID scores of
1.68 and 1.58 for unconditional and conditional generations, respectively, each
with 29 NFEs. This outcome demonstrates the effectiveness of AOT in enhancing
the performance of diffusion models.