Scaling up GANs for Text-to-Image Synthesis
This addresses the challenge for researchers and practitioners in generative AI by making GANs a viable, faster alternative for large-scale text-to-image synthesis, though it is incremental in improving upon existing GAN architectures.
The paper tackles the problem of scaling up GANs for text-to-image synthesis to compete with newer models like DALL-E 2, and introduces GigaGAN, which synthesizes a 512px image in 0.13 seconds and a 16-megapixel image in 3.66 seconds.
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 naÏvely 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.