Painting with words: a history of text-to-image AI

Text-to-image AI has evolved significantly over the past few years, transitioning from generating abstract, often incomprehensible images to producing high-quality, artistically comparable works that can closely mimic human creativity. This development journey began with early models like CLIP and BigGAN, which laid the foundational understanding by mapping text and images into a shared semantic space. Subsequent models, such as VQGAN+CLIP and Pixray, built upon this foundation, enhancing the artistic fidelity of generated images. The introduction of diffusion models, including DALL·E 2 and various iterations of Stable Diffusion, marked a turning point, improving image quality and consistency. Stable Diffusion XL (SDXL) is the latest advancement, offering refined image enhancements and the ability to fine-tune models for personalized outputs. These advancements have been facilitated by open-source platforms like Replicate, which provide tools for experimenting with and comparing different AI models. As the field continues to develop, there is anticipation for further improvements in creative control and fine-tuning capabilities, promising even greater artistic and practical applications.