EfficientCLIP-GAN: Enabling Real-time Text-to-Image Synthesis

Synthesis of text to images has become a powerful technology, but existing methods often suffer from high computational demands, slow generation times and limited control over the generated images. This paper introduces EfficientCLIP-GAN, a new model for deep learning that addresses these limitations. EfficientCLIP-GAN using Generative Adversarial Networks (GANs) to their full potential and the pre-trained CLIP model to achieve superior efficiency, enhanced controllability and unprecedented speed. The proposed architecture integrates CLIP into the GAN framework, enabling efficient prompt understanding, image-text matching, and more discriminative power. EfficientCLIP-GAN demonstrate superior efficiency, require substantially less training data and processing power than other approaches. Furthermore, it facilitates precise control over the images that are produced based on textual description, ensuring precise adherence to user prompts. Notably EfficientCLIP-GAN generates high-resolution images ~100 times faster than existing approaches, allowing for real-time CPU-based inference. This exceptional speed and efficiency make the model accessible for deployment on the low-power devices, democratizing access to production of images from text. Experimental results on the CUB dataset validate the model's effectiveness in creating a variety of realistic bird representations from written specifications. EfficientCLIP-GAN exhibits rapid training and convergence due to CLIP integrated generator and discriminator, In contrast to diffusion and autoregressive models, our approach thus needs 3% training data and 5% learnable parameters.