waifu2x

v1.0 branch

This branch is under construction. This would break backwards compatibility sometimes.

waifu2x

Image Super-Resolution for anime-style-art using Deep Convolutional Neural Networks.

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

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

Third Party Software

Third-Party

Dependencies

Hardware

• NVIDIA GPU

Platform

• Torch7
• NVIDIA CUDA

lualocks packages (excludes torch7's default packages)

• md5
• uuid
• turbo

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_7.0-28_amd64.deb
sudo apt-get update
sudo apt-get install cuda

Install Torch7

See: Getting started with Torch

Validation

Test the waifu2x command line tool.

th waifu2x.lua

Setting Up the Web Application Environment (if you needed)

Install packages

luarocks install md5
luarocks install uuid
PREFIX=$HOME/torch/install luarocks install turbo

Web Application

Run.

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

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

See also images/gen.sh.

Video Encoding

* 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

Training Your Own Model

Data Preparation

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

Training 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.

Training 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.

Training 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.