Image Super-Resolution for anime-style-art using Deep Convolutional Neural Networks.
Demo-Application can be found at http://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", http://arxiv.org/abs/1501.00092
- [2] "For Creators", http://piapro.net/en_for_creators.html
AMI ID: ami-0be01e4f AMI NAME: waifu2x-server Instance Type: g2.2xlarge Region: US West (N.California) OS: Ubuntu 14.04 User: ubuntu Created at: 2015-08-12 - NVIDIA GPU
- cutorch
- cunn
- graphicsmagick
- turbo
- md5
- uuid
(on Ubuntu 14.04)
sudo apt-get install curl curl -s https://raw.githubusercontent.com/torch/ezinstall/master/install-all | sudo bash Download Cuda
sudo dpkg -i cuda-repo-ubuntu1404_7.0-28_amd64.deb sudo apt-get update sudo apt-get install cuda sudo luarocks install cutorch sudo luarocks install cunn sudo apt-get install graphicsmagick libgraphicsmagick-dev sudo luarocks install graphicsmagick Test the waifu2x command line tool.
th waifu2x.lua curl -O http://luajit.org/download/LuaJIT-2.0.4.tar.gz tar -xzvf LuaJIT-2.0.4.tar.gz cd LuaJIT-2.0.4 make sudo make install Install luarocks packages.
sudo luarocks install md5 sudo luarocks install uuid sudo luarocks install turbo Run.
th web.lua View at: http://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.
* avconv is ffmpeg on Ubuntu 14.04.
Extracting images and audio from a video. (range: 00:09:00 ~ 00:12:00)
mkdir frames avconv -i data/raw.avi -ss 00:09:00 -t 00:03:00 -r 24 -f image2 frames/%06d.png avconv -i data/raw.avi -ss 00:09:00 -t 00:03:00 audio.mp3 Generating a image list.
find ./frames -name "*.png" |sort > data/frame.txt waifu2x (for example, noise reduction)
mkdir new_frames th waifu2x.lua -m noise -noise_level 1 -resume 1 -l data/frame.txt -o new_frames/%d.png Generating a video from waifu2xed images and audio.
avconv -f image2 -r 24 -i new_frames/%d.png -i audio.mp3 -r 24 -vcodec libx264 -crf 16 video.mp4 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 with 3000 high-resolution-noise-free-PNG images.)
Converting training data.
th convert_data.lua mkdir models/my_model th train.lua -model_dir models/my_model -method noise -noise_level 1 -test images/miku_noisy.png th cleanup_model.lua -model models/my_model/noise1_model.t7 -oformat ascii # usage th waifu2x.lua -model_dir models/my_model -m noise -noise_level 1 -i images/miku_noisy.png -o output.png You can check the performance of model with models/my_model/noise1_best.png.
th train.lua -model_dir models/my_model -method noise -noise_level 2 -test images/miku_noisy.png th cleanup_model.lua -model models/my_model/noise2_model.t7 -oformat ascii # usage th waifu2x.lua -model_dir models/my_model -m noise -noise_level 2 -i images/miku_noisy.png -o output.png You can check the performance of model with models/my_model/noise2_best.png.
th train.lua -model_dir models/my_model -method scale -scale 2 -test images/miku_small.png th cleanup_model.lua -model models/my_model/scale2.0x_model.t7 -oformat ascii # usage th waifu2x.lua -model_dir models/my_model -m scale -scale 2 -i images/miku_small.png -o output.png You can check the performance of model with models/my_model/scale2.0x_best.png.