This repository houses the model architecture I used to create my Generative Adversarial Network to synthesize anime faces! I have seen other models do something similar, but they use only characters from the 90s, so I wanted to spice it up with newer images. The characters were all scraped from MAL and detected for facial features using a Faster-RCNN from user @qhgz2013 on GitHub. The images were downloaded in their original size from MAL, but cropped to 64x64 to make it easier for training, like so.
The model uses a convolutional framework similar to the DCGAN framework, but utilizes spectral normalization on the convolution layers. This was originally was going to use the LSGAN loss function, but I broke the model at some point so just fell back to BCE because I couldn't figure out what happened. The model can be found in model.py with their components in blocks.py. Train.py houses the main training loop for the whole model. Here is a silly goofy gif of the training of 300 epochs. The images become cursed after ~100 epochs, but I think it's still interesting to include.
