Waifu2x - ArchWiki

# Waifu2x

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This article covers installing, using and training waifu2x, image super-resolution for anime-style art using deep convolutional neural networks.

## Installation

To directly use waifu2x, install waifu2x-gitAUR package. There are other alternates for using waifu2x, just search `waifu2x` in AUR.

Tip: If you have an NVIDIA GPU, you can install cuda to significantly speed up the conversion process.

## Usage

waifu2x is avaliable with command `waifu2x`. For detailed options, run `waifu2x --help`

### Upscaling

Use `--scale_ratio` parameter to specify scale ratio you want. And `-i` with input file name, `-o` with output file name:

    waifu2x --scale_ratio 2 -i my_waifu.png -o 2x_my_waifu.png

### Noise Reduction

Use `--noise_level` parameter(`1` or `2`) to specify noise reduction level:

    waifu2x --noise_level 1 -i my_waifu.png -o lucid_my_waifu.png

And you can use `--jobs` to specify number of threads launching at same time, benifit for multi-core CPU :

    waifu2x --jobs 4 --noise_level 1 -i my_waifu.png -o lucid_my_waifu.png

### Upscaling & Noise Reduction

`--scale_ratio` and `--noise_level` can be combined, so you can:

    waifu2x --scale_ratio 2 --noise_level 1 -i my_waifu.png -o 2x_lucid_my_waifu.png

Tip: If you are finding a batch operation interface, you can have a look at this waifu2x wrapper script

## Training

This section is being considered for removal.

Reason: This should be a PKGBUILD, most of it is also just copy-pasted from the README on Github (Discuss in Talk:Waifu2x#)

To train custom models, an NVIDIA graphical card is required because waifu2x uses CUDA for computing. Then you need to prepare below develop dependencies and waifu2x source.

### Dependencies

Install:

- lua51
- cuda
- snappy
- graphicsmagick
- torch7-gitAUR
- torch7-trepl-gitAUR
- torch7-sys-gitAUR
- torch7-cutorch-gitAUR
- torch7-nn-gitAUR
- torch7-cunn-gitAUR
- torch7-image-gitAUR
- torch7-xlua-gitAUR
- torch7-dok-gitAUR
- torch7-optim-gitAUR
- lua51-graphicsmagick-gitAUR
- lua51-cjsonAUR
- lua51-csvigo-gitAUR
- lua51-snappy-gitAUR

It is recommended to install below optional cuDNN library and bindings package. With them you can enable cuDNN backend for training, which have a significant speed up.

You need to manually download a cudnn binary pack from NVIDIA cuDNN site during installing cudnn.

- (optional)cudnn
- (optional)torch7-cudnn-gitAUR:

### waifu2x source

Fetch waifu2x source code from GitHub:

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

Enter source directory. Now you can test waifu2x command line tool:

    th waifu2x.lua

### Command line tools

Note: If you have installed cuDNN library, 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

For more, see waifu2x#command-line-tools.

### Train your own models

Note: If you have installed 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`.
Note: 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

Note: You should use noise free 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`.

For latest information, see waifu2x#train-your-own-model.

## Docker

See waifu2x#docker.

## See also

- waifu2x GitHub repository

Category:

- Image

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