  Many traditional computer vision algorithms generate realistic images by
requiring that each patch in the generated image be similar to a patch in a
training image and vice versa. Recently, this classical approach has been
replaced by adversarial training with a patch discriminator. The adversarial
approach avoids the computational burden of finding nearest neighbors of
patches but often requires very long training times and may fail to match the
distribution of patches. In this paper we leverage the recently developed
Sliced Wasserstein Distance and develop an algorithm that explicitly and
efficiently minimizes the distance between patch distributions in two images.
Our method is conceptually simple, requires no training and can be implemented
in a few lines of codes. On a number of image generation tasks we show that our
results are often superior to single-image-GANs, require no training, and can
generate high quality images in a few seconds. Our implementation is available
at https://github.com/ariel415el/GPDM
