#srgan

Manga109 model attempt (for illustrations): http://www.mediafire.com/file/w3jujtm752hvdj1/Manga109Attempt.pth.zip/file

List of models made by the /r/GameUpscale community:https://upscale.wiki/wiki/Model_Database

I’ve spent the night training ESRGAN on the Manga109 dataset. Compared to the default model, it’s more soft and more subtle, but it handles dithering well You can also interpolate it with the existing networks by modifying net_interp.py.

There’s a lot of room for improvement. If you want to train it yourself, read https://github.com/xinntao/BasicSR and https://github.com/xinntao/BasicSR/tree/master/codes/data

I used ImageMagick to turn the dataset into 128x128 tiles (any higher and it ran out of memory), then downscaled them to 32x32 for the low res dataset. I didn’t use BasicSR’s matlab downscaler nor did I make an LMDB file. Make absolutely sure that all tiles are the same size, that the HR and LR versions have the same filenames, and that they’re all RGB, no greyscale or indexed images (or else you’ll get a “Got 3 and 1 in dimension 1″ error). You’ll need to put the images in 4 folders: 80-90% of the pics into “training” folders and 10-20% into “validation” folders, and separate folders for HR and LR images. I also went into options.py and changed val_frequency and save_checkpoint_freq to 256 (too many checkpoints will eat up your hard drive space), and in train_esrgan.json I turned down the batch size and number of workers to keep memory usage down.

The experiments/ folder contains the validation results and the models (specifically the files ending in G.pth).

It’s a massive pain to set up, I haven’t tried training it on Windows and I probably left out vital info. I don’t even know if I’m training it “correctly” because I left the GAN settings untouched. Good luck!

The BasicSR creator uploaded some image datasets here: https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU

SRGAN is a new program that attempts to do for photographs what Waifu2x does for drawings; upscaling photos while preserving sharpness and reconstructing lost detail. It only supports 4x upsacling, meaning that 16 new pixels are generated for every old pixel.

You have 2 options for playing with it yourself:

The hard way: If you have Linux and are willing to install Tensorflow, you can download an implementation of it here. It comes with a pre-trained network so you don’t have to do that yourself, though you can if you wish. https://github.com/brade31919/SRGAN-tensorflow

The easy way: use the website Let’s Enhance. https://letsenhance.io/ It also uses SRGAN, and the results are superior to SRGAN-tensorflow’s defaults. I’m unsure if this is due to modifications to the program itself, or it was trained on a better network. The first 20 images are free, afterwards it’s $6 a month or $55 a year.

Unfortunately, it’s a bit inconsistent. For every acceptable result, there’s several failure cases like these (tried with both LE and SRGAN-tensorflow). It may help to train the network on high res CGI images or game textures or or something, but I would likely need thousands of images and it would take a couple of weeks to train. Should I give it a shot anyways?