CLIP - Guided Generative Adversarial Network for Semantic Text-to-image synthesis

Abstract

Text-to-image synthesis has emerged as one of the most significant applications of deep learning, enabling the automatic generation of realistic images from natural language descriptions. Traditional Generative Adversarial Networks (GANs) have demonstrated promising capabilities in image generation; however, maintaining semantic consistency between textual descriptions and generated images remains a major challenge. Recent advancements in Contrastive Language– Image Pre-training (CLIP) have provided powerful multimodal representations that effectively capture the relationship between textual and visual information. This project presents a CLIP-Guided Generative Adversarial Network (GAN) framework for semantic text-to-image synthesis. The proposed approach integrates the semantic understanding capability of CLIP with the image generation strength of GANs to produce visually realistic and textually aligned images. CLIP is employed to evaluate and guide the generator by measuring the similarity between generated images and their corresponding textual descriptions. The generator learns to create images that not only exhibit high visual quality but also preserve the semantic meaning embedded in the input text. The discriminator further enhances image authenticity by distinguishing between real and synthesized samples.