Experimenting with StyleGAN2
To dive deeper, I started working with StyleGAN2, one of the most advanced face generation models. I trained the model extensively, fine-tuning parameters to understand the subtle ways it could synthesize facial features like eyes, noses, and expressions. Although the results were already impressive, I wanted to push the boundaries further. My goal was to enhance the realism and efficiency of face generation models beyond what was currently available.
The OneMillionFaces Dataset
I developed an advanced face generation system by training StyleGAN2 and creating the OneMillionFaces dataset, an AI-generated collection of highly diverse, photorealistic faces. This project allowed me to contribute significantly to the field by providing a vast dataset that researchers and developers could use for various applications. My work extended to creating an ultra-efficient face generation AI, capable of running directly in-browser, even on mobile devices, offering real-time facial synthesis with minimal resources.
Designing an Efficient Face Generation AI
While StyleGAN2 was powerful, I aimed to develop a more efficient solution capable of real-time face generation on resource-limited devices. A typical StyleGAN2 model for generating 512x512 images is around 300 MB in size, but after months of optimization, I managed to create a highly efficient AI model that produces the same 512x512 images with a model size as low as 15 MB. This remarkable efficiency allows the model to run directly in a browser, even on mobile phones, offering real-time face synthesis with minimal computational resources. My innovations have made advanced face generation technology more accessible than ever before.
