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Scaling up GANs for Text-to-Image Synthesis
Cited 29 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2023
- Publisher
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Citation
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.10124-10134
- Abstract
- 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 center dot E 2, autoregressive 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 naively 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 images in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.
- ISSN
- 1063-6919
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