AI Image Synthesis: What The Future Holds

Originally published at Ross Dawson. Shortly after the new year 2021, the Media Synthesis community at Reddit began to become more than usually psychedelic. The board became saturated with unearthly images depicting rivers of blood, Picasso's King Kong, a Pikachu chasing Mark Zuckerberg, Synthwave witches, acid-induced kittens, an inter-dimensional portal, the industrial revolution and the possible child of Barack Obama and Donald Trump. The bizarre images were generated by inputting short phrases into Google Colab notebooks (web pages from which a user can access the formidable machine learning resources of the search giant), and letting the trained algorithms compute possible images based on that text. In most cases, the optimal results were obtained in minutes. Various attempts at the same phrase would usually produce wildly different results. In the image synthesis field, this free-ranging facility of invention is something new; not just a bridge between the text and image domains, but an early look at comprehensive AI-driven image generation systems that don't need hyper-specific training in very limited domains (i.e. NVIDIA's landscape generation framework GauGAN [on which, more later], which can turn sketches into landscapes, but only into landscapes; or the various sketch face Pix2Pix projects, that are likewise'specialized'). Example images generated with the Big Sleep Colab notebook [12].