waifu2x

Image Super-Resolution for Anime-style art using Deep Convolutional Neural Networks. And it supports photo.

The demo application can be found at http://waifu2x.udp.jp/ .

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-b9be23ae
AMI NAME: waifu2x-server 20160807
Instance Type: g2.2xlarge
OS: Ubuntu 14.04
User: ubuntu
Created at: 2016-08-07

See ~/README.md

Please update the git repo first.

git pull

Third Party Software

Third-Party

If you are a windows user, I recommend you to use waifu2x-caffe(Just download from releases tab) or waifu2x-conver-cpp.

Installation

Setting Up the Command Line Tool Environment

(on Ubuntu 14.04)

Install CUDA

See: NVIDIA CUDA Getting Started Guide for Linux

Download CUDA

sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.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

And install luarocks packages.

luarocks install graphicsmagick # upgrade
luarocks install lua-csnappy
luarocks install md5
luarocks install uuid
luarocks install csvigo

# if you need to use web application
PREFIX=$HOME/torch/install luarocks install turbo

# if you need to use cuDNN library. cuDNN is required.
luarocks install cudnn

Getting waifu2x

git clone --depth 1 https://github.com/nagadomi/waifu2x.git

Validation

Testing the waifu2x command line tool.

th waifu2x.lua

Command line tools

Notes: If you have cuDNN library, than you can use cuDNN with -force_cudnn 1 option. cuDNN is too much faster than default kernel.

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 0 -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 0 -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

The output format supports %s and %d(e.g. %06d). %s will be replaced the basename of the source filename. %d will be replaced a sequence number. For example, when input filename is piyo.png, %s_%03d.png will be replaced piyo_001.png.

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 libx264 -crf 16 video.mp4

Train Your Own Model

Note1: 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/rebuild.lua.

Note2: The command that was used to train for waifu2x's pretraind models is available at appendix/train_upconv_7_art.sh, appendix/train_upconv_7_photo.sh. Maybe it is helpful.

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
# 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
# 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 upconv_7 -model_dir models/my_model -method scale -scale 2 -test images/miku_small.png
# 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.

Train a 2x and noise reduction fusion model

th train.lua -model upconv_7 -model_dir models/my_model -method noise_scale -scale 2 -noise_level 1 -test images/miku_small.png
# usage
th waifu2x.lua -model_dir models/my_model -m noise_scale -scale 2 -noise_level 1 -i images/miku_small.png -o output.png

You can check the performance of model with models/my_model/noise1_scale2.0x_best.png.

Docker

Requires nvidia-docker.

docker build -t waifu2x .
nvidia-docker run -p 8812:8812 waifu2x th web.lua
nvidia-docker run -v `pwd`/images:/images waifu2x th waifu2x.lua -force_cudnn 1 -m scale -scale 2 -i /images/miku_small.png -o /images/output.png

Note that running waifu2x in without JIT caching is very slow, which is what would happen if you use docker. For a workaround, you can mount a host volume to the CUDA_CACHE_PATH, for instance,

nvidia-docker run -v $PWD/ComputeCache:/root/.nv/ComputeCache waifu2x th waifu2x.lua --help

rysinal/waifu2x