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README.md
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README.md
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# waifu2x
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Image Super-Resolution for anime/fan-art using Deep Convolutional Neural Networks.
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Demo-Application can be found at http://waifu2x.udp.jp/ .
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## Summary
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Click to see the slide show.
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![slide](https://raw.githubusercontent.com/nagadomi/waifu2x/master/images/slide.png)
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## References
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waifu2x is inspired by SRCNN [1]. 2D character picture (HatsuneMiku) is licensed under CC BY-NC by piapro [2].
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- [1] Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, "Image Super-Resolution Using Deep Convolutional Networks", http://arxiv.org/abs/1501.00092
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- [2] "For Creators", http://piapro.net/en_for_creators.html
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## Dependencies
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### Platform
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- [Torch7](http://torch.ch/)
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- [NVIDIA CUDA](https://developer.nvidia.com/cuda-toolkit)
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- [NVIDIA cuDNN](https://developer.nvidia.com/cuDNN)
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### Packages (luarocks)
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- [cudnn](https://github.com/soumith/cudnn.torch)
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- [graphicsmagick](https://github.com/clementfarabet/graphicsmagick)
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- [turbo](https://github.com/kernelsauce/turbo)
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- md5
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- uuid
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NOTE: Turbo 1.1.3 has bug in file uploading. Please install from the master branch on github.
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## Running The Web Application
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Please edit the first line in `web.lua`.
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```
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local ROOT = '/path/to/waifu2x/dir'
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```
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Run.
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```
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th web.lua
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```
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View at: http://localhost:8812/
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## Command line tools
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### Noise Reduction
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```
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th waifu2x.lua -m noise -noise_level 1 -i input_image.png -o output_image.png
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```
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```
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th waifu2x.lua -m noise -noise_level 2 -i input_image.png -o output_image.png
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```
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### 2x Upscaling
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```
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th waifu2x.lua -m scale -i input_image.png -o output_image.png
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```
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### Noise Reduction + 2x Upscaling
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```
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th waifu2x.lua -m noise_scale -noise_level 1 -i input_image.png -o output_image.png
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```
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```
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th waifu2x.lua -m noise_scale -noise_level 2 -i input_image.png -o output_image.png
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```
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See also `images/gen.sh`.
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## Training Your Own Model
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### Data Preparation
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Genrating a file list.
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```
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find /path/to/image/dir -name "*.png" > data/image_list.txt
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```
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(You should use PNG! In my case, waifu2x is trained by 3000 PNG images.)
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Converting training data.
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```
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th convert_data.lua
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```
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### Training a Noise Reduction(level1) model
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```
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th train.lua -method noise -noise_level 1 -test images/miku_noise.png
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th cleanup_model.lua -model models/noise1_model.t7 -oformat ascii
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```
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You can check the performance of model with `models/noise1_best.png`.
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### Training a Noise Reduction(level2) model
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```
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th train.lua -method noise -noise_level 2 -test images/miku_noise.png
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th cleanup_model.lua -model models/noise2_model.t7 -oformat ascii
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```
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You can check the performance of model with `models/noise2_best.png`.
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### Training a 2x Upsclaing model
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```
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th train.lua -method scale -scale 2 -test images/miku_small.png
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th cleanup_model.lua -model models/scale2.0x_model.t7 -oformat ascii
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```
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You can check the performance of model with `models/scale2.0x_best.png`.
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