  Recently there has been an interest in the potential of learning generative
models from a single image, as opposed to from a large dataset. This task is of
practical significance, as it means that generative models can be used in
domains where collecting a large dataset is not feasible. However, training a
model capable of generating realistic images from only a single sample is a
difficult problem. In this work, we conduct a number of experiments to
understand the challenges of training these methods and propose some best
practices that we found allowed us to generate improved results over previous
work in this space. One key piece is that unlike prior single image generation
methods, we concurrently train several stages in a sequential multi-stage
manner, allowing us to learn models with fewer stages of increasing image
resolution. Compared to a recent state of the art baseline, our model is up to
six times faster to train, has fewer parameters, and can better capture the
global structure of images.
