  We introduce SinGAN, an unconditional generative model that can be learned
from a single natural image. Our model is trained to capture the internal
distribution of patches within the image, and is then able to generate high
quality, diverse samples that carry the same visual content as the image.
SinGAN contains a pyramid of fully convolutional GANs, each responsible for
learning the patch distribution at a different scale of the image. This allows
generating new samples of arbitrary size and aspect ratio, that have
significant variability, yet maintain both the global structure and the fine
textures of the training image. In contrast to previous single image GAN
schemes, our approach is not limited to texture images, and is not conditional
(i.e. it generates samples from noise). User studies confirm that the generated
samples are commonly confused to be real images. We illustrate the utility of
SinGAN in a wide range of image manipulation tasks.
