This repository contains the code for the Real-ESRGAN framework used to increase the resolution of images, aka super resolution. You can find the code from the original authors here, which uses PyTorch instead of TensorFlow.
This work is also based on the Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data paper.
Most of the functions are just translations of PyTorch to TensorFlow from the original repository.
The presented version was trained using an anime dataset, to be exact, frames from My Little Witch Academia anime were used as the training dataset.
Inside the models folder you can find two versions of the generator, one uses the exponential moving average technique and the other is the original model.
Once you have installed the dependencies:
requirements.txt
And downloaded or created some dataset, you need to put your images inside the data/train_images folder or change the following code:
data_path = os.path.abspath("./data/train_images/*.png")
To train the model in one step run:
python train.py --save_gan_model --save_ema_model
once finished you need to train the whole framework (Generator + Discriminator):
python train_gan.py --load_gan --save_gan_model --save_ema_model
If you want to run multiple steps you can run:
python train.py --epochs=5000
then
python train.py --epochs=7000 --save_gan_model --save_ema_model --continue_training
Now the whole framework:
python train_gan.py --load_gan --epochs=5000
then
python train_gan.py --epochs=7000 --save_gan_model --save_ema_model --continue_training
The default batch size is 14 which works well with GPU's with 16gb of VRAM.
put your images inside the data/user_images folder and run:
python use_model.py --use_ema_model --batch_size=1
adjust the batch size according to the number of images inside the data/user_images folder.
For a 16gb GPU you should use images of a medium size, around 750x500 or below. If you want to use a larger batch size you need smaller images, around 500x500 or below.
You can also test some code, like models, loss functions and the generator:
python -m unittest -v test/test_loss.py
python -m unittest -v test/test_model.py
python -m unittest -v test/test_data_generator.py
