The recent success of text-to-image synthesis has taken the world by storm
and captured the general public's imagination. From a technical standpoint, it
also marked a drastic change in the favored architecture to design generative
image models. GANs used to be the de facto choice, with techniques like
StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new
standard for large-scale generative models overnight. This rapid shift raises a
fundamental question: can we scale up GANs to benefit from large datasets like
LAION? We find that na\"Ively increasing the capacity of the StyleGAN
architecture quickly becomes unstable. We introduce GigaGAN, a new GAN
architecture that far exceeds this limit, demonstrating GANs as a viable option
for text-to-image synthesis. GigaGAN offers three major advantages. First, it
is orders of magnitude faster at inference time, taking only 0.13 seconds to
synthesize a 512px image. Second, it can synthesize high-resolution images, for
example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various
latent space editing applications such as latent interpolation, style mixing,
and vector arithmetic operations.

本文提出了一种新的 GAN 架构 GigaGAN，用于文本到图像合成，并比较了其与传统的 GANs 和 DALL-E2 的性能，结果表明 GigaGAN 可以快速且高质量地合成高分辨率图像。