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

• URL: http://arxiv.org/abs/2202.13799v3
• Date: Mon, 28 Aug 2023 04:52:53 GMT
• Title: One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU
• Authors: Junseok Oh, Donghwee Yoon and Injung Kim
• Abstract summary: OUR-GAN is a one-shot generative adversarial network framework that generates non-repetitive 16K images from a single training image. OUR-GAN can synthesize high-quality 16K images with 12.5 GB of GPU memory and 4K images with only 4.29 GB. OUR-GAN is the first one-shot image synthesizer that generates non-repetitive UHR images on a single consumer GPU.
• Score: 1.9060575156739825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a one-shot ultra-high-resolution generative adversarial network (OUR-GAN) framework that generates non-repetitive 16K (16, 384 x 8, 640) images from a single training image and is trainable on a single consumer GPU. OUR-GAN generates an initial image that is visually plausible and varied in shape at low resolution, and then gradually increases the resolution by adding detail through super-resolution. Since OUR-GAN learns from a real ultra-high-resolution (UHR) image, it can synthesize large shapes with fine details and long-range coherence, which is difficult to achieve with conventional generative models that rely on the patch distribution learned from relatively small images. OUR-GAN can synthesize high-quality 16K images with 12.5 GB of GPU memory and 4K images with only 4.29 GB as it synthesizes a UHR image part by part through seamless subregion-wise super-resolution. Additionally, OUR-GAN improves visual coherence while maintaining diversity by applying vertical positional convolution. In experiments on the ST4K and RAISE datasets, OUR-GAN exhibited improved fidelity, visual coherency, and diversity compared with the baseline one-shot synthesis models. To the best of our knowledge, OUR-GAN is the first one-shot image synthesizer that generates non-repetitive UHR images on a single consumer GPU. The synthesized image samples are presented at https://our-gan.github.io.

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