  We present InfinityGAN, a method to generate arbitrary-resolution images. The
problem is associated with several key challenges. First, scaling existing
models to a high resolution is resource-constrained, both in terms of
computation and availability of high-resolution training data. Infinity-GAN
trains and infers patch-by-patch seamlessly with low computational resources.
Second, large images should be locally and globally consistent, avoid
repetitive patterns, and look realistic. To address these, InfinityGAN takes
global appearance, local structure and texture into account.With this
formulation, we can generate images with resolution and level of detail not
attainable before. Experimental evaluation supports that InfinityGAN generates
imageswith superior global structure compared to baselines at the same time
featuring parallelizable inference. Finally, we how several applications
unlocked by our approach, such as fusing styles spatially, multi-modal
outpainting and image inbetweening at arbitrary input and output resolutions
