StyleGAN + GAN Blending + pix2pixHD 

As a further step to my Smile Filter project, I explored the capablities of StyleGAN for image-to-image translation of cartoon faces. This project aimed to transform human faces into cartoon-style portraits using deep learning techniques.


To train the StyleGAN model, I first finetuned a StyleGAN2 model on a cartoon dataset and created a cartoon-model. I then blended this model with an FFHQ face model to generate a lot of pair data containing fake human faces vs. cartoon images. I generated approximately 18,000 pairs of data, which I used to train a pix2pixHD model for image-to-image translation.

To achieve the desired results, I considered various factors such as variety of low-polies, resolution, model configuration, and training steps. I collected web low poly portraits and captured various images of them from various angles to create a diverse and rich dataset.


The trained pix2pixHD model was exported to ONNX format and imported into Lens Studio for real-time transformation of human faces into cartoon-style portraits. This can be used to create fun and engaging filters for social media platforms and other applications.


In conclusion, my StyleGAN Toonify project demonstrates the power of deep learning techniques such as StyleGAN and pix2pixHD for image-to-image translation of human faces into cartoon-style portraits. With the right training data and model configuration, we can achieve stunning results that are useful for a variety of applications. This project is still in progress, and I plan to continue exploring the capabilities of StyleGAN for image-to-image translation.