Image Super-Resolution for anime/fan-art using Deep Convolutional Neural Networks.
Demo-Application can be found at https://waifu2x.udp.jp/ .
Click to see the slide show.
waifu2x is inspired by SRCNN [1]. 2D character picture (HatsuneMiku) is licensed under CC BY-NC by piapro [2].
- [1] Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, "Image Super-Resolution Using Deep Convolutional Networks", https://arxiv.org/abs/1501.00092
- [2] "For Creators", https://piapro.net/en_for_creators.html
AMI name: waifu2x server
AMI ID: ami-75f01931
Region: N. California
Instance: g2.2xlarge (require a GPU)
OS: Ubuntu 14.04
User: ubuntu
- cutorch
- cunn
- cudnn
- graphicsmagick
- turbo
- md5
- uuid
NOTE: Turbo 1.1.3 has bug in file uploading. Please install from the master branch on github.
Please edit the first line in web.lua.
local ROOT = '/path/to/waifu2x/dir'
Run.
th web.lua
View at: https://localhost:8812/
th waifu2x.lua -m noise -noise_level 1 -i input_image.png -o output_image.png
th waifu2x.lua -m noise -noise_level 2 -i input_image.png -o output_image.png
th waifu2x.lua -m scale -i input_image.png -o output_image.png
th waifu2x.lua -m noise_scale -noise_level 1 -i input_image.png -o output_image.png
th waifu2x.lua -m noise_scale -noise_level 2 -i input_image.png -o output_image.png
See also images/gen.sh.
Genrating a file list.
find /path/to/image/dir -name "*.png" > data/image_list.txt
(You should use PNG! In my case, waifu2x is trained by 3000 PNG images.)
Converting training data.
th convert_data.lua
th train.lua -method noise -noise_level 1 -test images/miku_noise.png
th cleanup_model.lua -model models/noise1_model.t7 -oformat ascii
You can check the performance of model with models/noise1_best.png.
th train.lua -method noise -noise_level 2 -test images/miku_noise.png
th cleanup_model.lua -model models/noise2_model.t7 -oformat ascii
You can check the performance of model with models/noise2_best.png.
th train.lua -method scale -scale 2 -test images/miku_small.png
th cleanup_model.lua -model models/scale2.0x_model.t7 -oformat ascii
You can check the performance of model with models/scale2.0x_best.png.