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We propose a one-shot ultra-high-resolution generative adversarial network(OUR-GAN) framework that generates non-repetitive 16K (16, 384 x 8, 640) imagesfrom a single training image and is trainable on a single consumer GPU. OUR-GANgenerates an initial image that is visually plausible and varied in shape atlow resolution, and then gradually increases the resolution by adding detailthrough super-resolution. Since OUR-GAN learns from a realultra-high-resolution (UHR) image, it can synthesize large shapes with finedetails and long-range coherence, which is difficult to achieve withconventional generative models that rely on the patch distribution learned fromrelatively small images. OUR-GAN can synthesize high-quality 16K images with12.5 GB of GPU memory and 4K images with only 4.29 GB as it synthesizes a UHRimage part by part through seamless subregion-wise super-resolution.Additionally, OUR-GAN improves visual coherence while maintaining diversity byapplying vertical positional convolution. In experiments on the ST4K and RAISEdatasets, OUR-GAN exhibited improved fidelity, visual coherency, and diversitycompared with the baseline one-shot synthesis models. To the best of ourknowledge, OUR-GAN is the first one-shot image synthesizer that generatesnon-repetitive UHR images on a single consumer GPU. The synthesized imagesamples are presented at https://our-gan.github.io.

One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU