Toonify Product Information and Latest Updates (2025)

Amazing StyleGAN project. Fun for all the family. *the website does not store image data* How does this work? This toonification system is made using deep learning. It's based on distillation of a blended StyleGAN models into a pix2pixHD image to image translation network. How did you come up with this idea? This all started from some earlier experiments Doron shared on Twitter that got a lot of interest. For a description of those early experiments see this blog post on self toonification, the idea is the same, but the method is different (and not really suitable for hosting as a webapp). Going even futher back in time, this was all based on some new methods of mixing StyleGAN models I came up with trying to make realistic Ukiyo-e portraits How are you paying for this? Wasn't it costing too much? We are now generously supported by DeepAI who are running the neural network backend for us. Although DeepAI are now doing most of the heavy lifting, there is still a bit of cost in running the site, and we're very grateful for those who have supported us on Ko-fi. How do I get good results? The algorithm works best with high resolution images without much noise. Looking straight on to the camera also seem to work best. Something like a corporate headshot tends to work well. Do you store my photo? We don't store any of the images uploaded or generated. We send the image to DeepAI's servers to run the network and then show you the results. No imagery is stored on our systems. My face wasn't found! We use the open source dlib face detector to find faces, it's designed to pick up frontal faces but isn't perfect. Where did my glasses go? Not many characters from animated films wear glasses so the model seems to have learnt to mostly remove them. Can I use this model for my own project? DeepAI provide an API that you can use to integrate this into your own project. It's what we're using to run this site! Original tweet by Doron: https://twitter.com/Norod78/stat... See additional coverage here: https://gizmodo.com/i-have-seen-... A technical deep dive: