The Future of High Quality Image Creation

GAN resurrected. AI Image Upscaling

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Move over Diffusion models because GANs are back and better than ever before. Yes, you heard that right — Generative Adversarial Networks have been resurrected and are making waves in the world of AI image upscaling.

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Recent models such as DALL·E 3, Imagen, Parti, and Stable Diffusion have brought in a new era of image generation, achieving unprecedented levels of image quality and model flexibility. However, these models rely on iterative inference, which can be both a blessing and a curse. While iterative methods enable stable training with simple objectives, they also incur a high computational cost during inference.

In contrast, GANs generate images through a single forward pass, making them inherently efficient. However, scaling them requires careful tuning of the network architectures and training considerations. Until now, GANs have excelled at modeling single or multiple object classes, but scaling to complex datasets, much less an open world, has remained challenging.

But fear not, because a team of researchers has found a way to overcome these barriers and achieve stable and scalable training of a one-billion-parameter GAN (GigaGAN) on large-scale datasets. Their method involves effectively scaling the generator’s capacity, adapting several techniques commonly used in the diffusion context, and reintroducing multi-scale training.

From Low-Res to High-Res: GAN Upscaling Takes AI Art to the Next Level

The New Era of AI Image Upscaling and AI Art: GANs and the Billion-Parameter Model

The results are nothing short of remarkable. GigaGAN is 36 times larger than Style GAN2 and six times larger than StyleGAN-XL and XMC-GAN. It generates a 512px image in 0.13 seconds and can synthesize ultra high-res images at 4k resolution in 3.66 seconds. Plus, it has a controllable, latent vector space that lends itself to well-studied controllable image syntheses applications such as style mixing, prompt interpolation, and prompt mixing.

Drag Your GAN: Unlocking the Magic of Interactive AI Art. Point-based Draggan tool.

GANs: The AI Art Pioneer is Back to Transform AI Image Upscaling Forever

So what does all of this mean for AI image upscaling? First, it means that GANs are still viable for text-to-image synthesis and should be considered for future aggressive scaling. With GigaGAN, we now have a powerful tool to take low-resolution images and upscale them to stunning, high-quality masterpieces in seconds.

In a world where AI is becoming increasingly prevalent, GANs are proving to be a game-changer in the field of image upscaling. So get ready to witness the power of GANs and the incredible photos they can create. The future is bright, and it looks a lot sharper, thanks to GigaGAN.

The End of the Photoshop Era

Adobe research created the project. Photoshop has a soft spot for outdated technologies. It is unclear whether the resurrected GAN will perform a miracle.

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Keywords: computer vision, Artificial Intelligence, Machine Learning, AI art, art, wombo dream, digital art, Dalle 2, Imagen, wombo ai, Parti, 3D point cloud, diffusion models, generative art, wombo art, photographic quality, img by AI system, AI art generator, text to art generator, 3D, midjourney, dalle2, stablediffusion, Dalle 3, vision-language model, AI artists, AI Image Upscaling, alternatives

project page:

https://mingukkang.github.io/GigaGAN/

@inproceedings{kang2023gigagan,
  author    = {Kang, Minguk and Zhu, Jun-Yan and Zhang, Richard and Park, Jaesik and Shechtman, Eli and Paris, Sylvain and Park, Taesung},
  title     = {Scaling up GANs for Text-to-Image Synthesis},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2023},
}