GigaGAN: A Revolutionary Leap in Text-to-Image Synthesis

The world of image synthesis has been revolutionized by the advent of Generative Adversarial Networks (GANs), particularly with the development of GigaGAN, a new GAN architecture that has taken text-to-image synthesis to new heights. This innovative technology has been introduced by a team of researchers from POSTECH, Carnegie Mellon University, and Adobe Research, and is set to be highlighted in the CVPR 2023 conference.

GigaGAN has been designed to scale up GANs to benefit from large datasets like LAION. The conventional StyleGAN architecture becomes unstable when its capacity is increased naively. However, GigaGAN far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis.

The GigaGAN architecture offers three major advantages. Firstly, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Secondly, it can synthesize high-resolution images, for example, 16-megapixel pixels in just 3.66 seconds. Lastly, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.

The GigaGAN generator consists of a text encoding branch, style mapping network, multi-scale synthesis network, augmented by stable attention and adaptive kernel selection. The text encoding branch extracts text embeddings using a pretrained CLIP model and a learned attention layers. The embedding is then passed to the style mapping network to produce the style vector, similar to StyleGAN. The synthesis network uses the style code as modulation and the text embeddings as attention to produce an image pyramid.

GigaGAN also comes with a disentangled, continuous, and controllable latent space. It can achieve layout-preserving fine style control by applying a different prompt at fine scales. This allows for changing texture and style with prompting, providing a high degree of control over the final image output.

In addition to its other capabilities, GigaGAN can also be used to train an efficient, higher-quality upsampler. This can be applied to real images, or to the outputs of other text-to-image models like diffusion. GigaGAN can synthesize ultra high-res images at 4k resolution in just 3.66 seconds.

In conclusion, GigaGAN represents a significant leap forward in the field of text-to-image synthesis. Its speed, high-resolution capabilities, and controllable latent space make it a powerful tool for image synthesis. As we continue to explore the potential of GANs, technologies like GigaGAN will undoubtedly play a crucial role in shaping the future of this exciting field.

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Paper location can be found here : GigaGAN: Scaling up GANs for Text-to-Image Synthesis (mingukkang.github.io)