  Developing image-generative models, which are robust to outliers in the
training process, has recently drawn attention from the research community. Due
to the ease of integrating unbalanced optimal transport (UOT) into adversarial
framework, existing works focus mainly on developing robust frameworks for
generative adversarial model (GAN). Meanwhile, diffusion models have recently
dominated GAN in various tasks and datasets. However, according to our
knowledge, none of them are robust to corrupted datasets. Motivated by DDGAN,
our work introduces the first robust-to-outlier diffusion. We suggest replacing
the UOT-based generative model for GAN in DDGAN to learn the backward diffusion
process. Additionally, we demonstrate that the Lipschitz property of divergence
in our framework contributes to more stable training convergence. Remarkably,
our method not only exhibits robustness to corrupted datasets but also achieves
superior performance on clean datasets.
