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LICENSE (1091, 2016-06-15)
NOTICE (161, 2016-06-15)
appendix (0, 2016-06-15)
appendix\benchmark.md (1825, 2016-06-15)
appendix\purge_cache.lua (430, 2016-06-15)
appendix\purge_cache.sh (120, 2016-06-15)
appendix\run-web.sh (281, 2016-06-15)
appendix\waifu2x.nginx.conf (969, 2016-06-15)
appendix\waifu2x.upstart.conf (217, 2016-06-15)
assets (0, 2016-06-15)
assets\bg.png (202987, 2016-06-15)
assets\favicon.ico (1150, 2016-06-15)
assets\index.de.html (5219, 2016-06-15)
assets\index.es.html (5222, 2016-06-15)
assets\index.fr.html (5248, 2016-06-15)
assets\index.html (4979, 2016-06-15)
assets\index.ja.html (5154, 2016-06-15)
assets\index.ko.html (5201, 2016-06-15)
assets\index.pt.html (5128, 2016-06-15)
assets\index.ru.html (5792, 2016-06-15)
assets\index.tr.html (5107, 2016-06-15)
assets\index.zh-CN.html (5001, 2016-06-15)
assets\mobile.css (355, 2016-06-15)
assets\style.css (3539, 2016-06-15)
assets\ui.js (2859, 2016-06-15)
cache (0, 2016-06-15)
convert_data.lua (1469, 2016-06-15)
data (0, 2016-06-15)
images (0, 2016-06-15)
images\gen.sh (494, 2016-06-15)
images\lena.png (470760, 2016-06-15)
images\lena_waifu2x.png (406426, 2016-06-15)
images\lena_waifu2x_ukbench.png (1503055, 2016-06-15)
images\miku_CC_BY-NC.jpg (78998, 2016-06-15)
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# waifu2x Image Super-Resolution for Anime-style art using Deep Convolutional Neural Networks. And it supports photo. Demo-Application can be found at http://waifu2x.udp.jp/ . ## Summary Click to see the slide show.  ## References 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 ## Public AMI ``` Region: us-east-1 (N.Virginia) AMI ID: ami-568f823c AMI NAME: waifu2x-server Instance Type: g2.2xlarge OS: Ubuntu 14.04 User: ubuntu Created at: 2016-03-22 ``` See ~/README.md Please update the git repo first. ``` git pull ``` ## Third Party Software [Third-Party](https://github.com/nagadomi/waifu2x/wiki/Third-Party) If you are a windows user, I recommend you to use [waifu2x-caffe](https://github.com/lltcggie/waifu2x-caffe)(Just download from `releases` tab) or [waifu2x-conver-cpp](https://github.com/tanakamura/waifu2x-converter-cpp). ## Dependencies ### Hardware - NVIDIA GPU ### Platform - [Torch7](http://torch.ch/) - [NVIDIA CUDA](https://developer.nvidia.com/cuda-toolkit) ### LuaRocks packages (excludes torch7's default packages) - lua-csnappy - md5 - uuid - [turbo](https://github.com/kernelsauce/turbo) ## Installation ### Setting Up the Command Line Tool Environment (on Ubuntu 14.04) #### Install CUDA See: [NVIDIA CUDA Getting Started Guide for Linux](http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux/#ubuntu-installation) Download [CUDA](http://developer.nvidia.com/cuda-downloads) ``` sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd***.deb sudo apt-get update sudo apt-get install cuda ``` #### Install Package ``` sudo apt-get install libsnappy-dev sudo apt-get install libgraphicsmagick1-dev ``` #### Install Torch7 See: [Getting started with Torch](http://torch.ch/docs/getting-started.html) And install luarocks packages. ``` luarocks install graphicsmagick # upgrade luarocks install lua-csnappy luarocks install md5 luarocks install uuid PREFIX=$HOME/torch/install luarocks install turbo # if you need to use web application ``` #### Getting waifu2x ``` git clone --depth 1 https://github.com/nagadomi/waifu2x.git ``` #### Validation Testing the waifu2x command line tool. ``` th waifu2x.lua ``` ## Web Application ``` th web.lua ``` View at: http://localhost:8812/ ## Command line tools ### Noise Reduction ``` 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 noise -noise_level 3 -i input_image.png -o output_image.png ``` ### 2x Upscaling ``` th waifu2x.lua -m scale -i input_image.png -o output_image.png ``` ### Noise Reduction + 2x Upscaling ``` 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 th waifu2x.lua -m noise_scale -noise_level 3 -i input_image.png -o output_image.png ``` ### Batch conversion ``` find /path/to/imagedir -name "*.png" -o -name "*.jpg" > image_list.txt th waifu2x.lua -m scale -l ./image_list.txt -o /path/to/outputdir/prefix_%d.png ``` See also `th waifu2x.lua -h`. ### Using photo model Please add `-model_dir models/photo` to command line option, if you want to use photo model. For example, ``` th waifu2x.lua -model_dir models/photo -m scale -i input_image.png -o output_image.png ``` ### Video Encoding \* `avconv` is alias of `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 -framerate 24 -i new_frames/%d.png -i audio.mp3 -r 24 -vcodec libx2*** -crf 16 video.mp4 ``` ## Train Your Own Model Notes: If you have cuDNN library, you can use cudnn kernel with `-backend cudnn` option. And you can convert trained cudnn model to cunn model with `tools/cudnn2cunn.lua`. ### Data Preparation Genrating a file list. ``` find /path/to/image/dir -name "*.png" > data/image_list.txt ``` You should use noise free images. In my case, waifu2x is trained with 6000 high-resolution-noise-free-PNG images. Converting training data. ``` th convert_data.lua ``` ### Train a Noise Reduction(level1) model ``` 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`. ### Train a Noise Reduction(level2) model ``` 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`. ### Train a 2x UpScaling model ``` 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`.

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