VQGAN Text-to-Image Generation Enhancement using CLIP

Text-to-image synthesis is a computer vision task that involves understanding and converting textual descriptions into corresponding and relevant images. Recently, Generative Adversarial Networks (GANs) and Contrastive Language-Image Pre-training (CLIP) have been employed to accomplish this objective. VQGAN comprises of a generator and a discriminator network that work together in a competitive manner to create realistic images based on input descriptions. In contrast, CLIP is a transformer-based language model that has been pre-trained using a diverse set of text and image data. Integrating the capabilities of both VQGAN and CLIP can lead to notable enhancements in text-to-image generation compared to using either technique individually. This abstract offers a concise overview of the latest progress in this area and emphasizes the potential advantages of leveraging a combination of GANs and CLIP for text-to-image synthesis.