GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

TL;DR

GALIP introduces a novel generative adversarial framework leveraging CLIP for efficient, controllable, and high-quality text-to-image synthesis, significantly reducing data and parameter requirements while increasing speed.

Contribution

The paper presents GALIP, a CLIP-based GAN model that improves efficiency, control, and speed in text-to-image synthesis compared to existing large models.

Findings

Requires only 3% of training data of large models

Achieves 120 times faster image synthesis

Maintains high image quality and controllability

Abstract

Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the image synthesis process heavily. 3) The synthesized visual features are difficult to control and require delicately designed prompts. To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP. GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator. Specifically, we propose a CLIP-based discriminator. The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality. Furthermore, we propose a CLIP-empowered generator that induces the visual concepts from CLIP through bridge features and prompts. The CLIP-integrated generator and discriminator boost training efficiency, and as a result, our model only requires about 3% training data and 6% learnable parameters, achieving comparable results to large pretrained autoregressive and diffusion models. Moreover, our model achieves 120 times faster synthesis speed and inherits the smooth latent space from GAN. The extensive experimental results demonstrate the excellent performance of our GALIP. Code is available at https://github.com/tobran/GALIP.