waifu2x/README.md

# waifu2x

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

The demo application can be found at https://waifu2x.udp.jp/ (Cloud version), https://unlimited.waifu2x.net/ (In-Browser version).

## 2023/02 PyTorch version

[nunif](https://github.com/nagadomi/nunif)

waifu2x development has already been moved to the repository above.

## Summary

Click to see the slide show.

![slide](https://raw.githubusercontent.com/nagadomi/waifu2x/master/images/slide.png)

## 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", https://piapro.net/intl/en_for_creators.html
## Public AMI

TODO

## 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), [waifu2x-ncnn-vulkan](https://github.com/nihui/waifu2x-ncnn-vulkan) or [waifu2x-conver-cpp](https://github.com/DeadSix27/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
- csvigo
- [turbo](https://github.com/kernelsauce/turbo)

## Installation

### Setting Up the Command Line Tool Environment
 (on Ubuntu 16.04)

#### 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_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
sudo apt-get install libssl1.0-dev # for web server
```

Note: waifu2x requires little-cms2 linked graphicsmagick. if you use macOS/homebrew, See [#174](https://github.com/nagadomi/waifu2x/issues/174#issuecomment-384466451).

#### Install Torch7

See: [Getting started with Torch](http://torch.ch/docs/getting-started.html).

- For CUDA9.x/CUDA8.x, see [#222](https://github.com/nagadomi/waifu2x/issues/222)
- For CUDA10.x, see [#253](https://github.com/nagadomi/waifu2x/issues/253#issuecomment-445448928)

#### Getting waifu2x

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

and install lua modules.

```
cd waifu2x
./install_lua_modules.sh
```

#### Validation

Testing the waifu2x command line tool.
```
th waifu2x.lua
```

## Web Application
```
th web.lua
```

View at: http://localhost:8812/

## 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. If you got GPU out of memory error, you can avoid it with `-crop_size` option (e.g. `-crop_size 128`).

### 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`.

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 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 pretrained 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

( Docker image is available at https://hub.docker.com/r/nagadomi/waifu2x )

Requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).

```
docker build -t waifu2x .
docker run --gpus all -p 8812:8812 waifu2x th web.lua
docker run --gpus all -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](https://devblogs.nvidia.com/parallelforall/cuda-pro-tip-understand-fat-binaries-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,

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