Anime2Sketch

Anime2Sketch: A sketch extractor for illustration, anime art, manga

By Xiaoyu Xiang

Updates

• 2022.1.14: Add Docker environment by kitoria
• 2021.12.25: Update README. Merry Christmas!
• 2021.5.24: Fix an interpolation error and a GPU inference error.
• 2021.5.12: Web Demo by AK391
• 2021.5.2: Upload more example results of anime video.
• 2021.4.30: Upload the test scripts. Now our repo is ready to run!
• 2021.4.11: Upload the pretrained weights, and more test results.
• 2021.4.8: Create the repo.

Introduction

The repository contains the testing codes and pretrained weights for Anime2Sketch.

Anime2Sketch is a sketch extractor that works well on illustration, anime art, and manga. It is an application based on the paper "Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis".

Prerequisites

• Linux, macOS, Docker
• Python 3 (Recommend to use Anaconda)
• CPU or NVIDIA GPU + CUDA CuDNN
• Pillow, PyTorch

Get Started

Installation

Install the required packages: pip install -r requirements.txt

Download Pretrained Weights

Please download the weights from GoogleDrive, and put it into the weights/ folder.

We also have an artifact-free version of the model which works with dark / low contrast images. You can download the weights from GoogleDrive, and put it into weights/ folder.

Test

python3 test.py --dataroot /your_input/dir --load_size 512 --output_dir /your_output/dir

The above command includes three arguments:

• dataroot: your test file or directory
• load_size: due to the memory limit, we need to resize the input image before processing. By default, we resize it to 512x512.
• output_dir: path of the output directory

Run our example:

python3 test.py --dataroot test_samples/madoka.jpg --load_size 512 --output_dir results/

Docker

If you want to run on Docker, you can easily do so by customizing the input/output images directory.
Build docker image

make docker-build

Setting input/output directory
You can customize mount volumes for input/output images by Makefile. Please setting your target directory.

docker run -it --rm --gpus all -v `pwd`:/workspace -v {your_input_dir}:/input -v {your_output_dir}:/output anime2sketch

example:

docker run -it --rm --gpus all -v `pwd`:/workspace -v `pwd`/test_samples:/input -v `pwd`/output:/output anime2sketch

Run

make docker-run

if you want to run cpu only, you will need to fix two things (remove gpu options).

• Dockerfile CMD line to CMD [ "python", "test.py", "--dataroot", "/input", "--load_size", "512", "--output_dir", "/output" ]
• Makefile docker-run line to docker run -it --rm -v `pwd`:/workspace -v `pwd`/images/input:/input -v `pwd`/images/output:/output anime2sketch

Train

This project is a sub-branch of AODA. Please check it for the training instructions.

More Results

Our model works well on illustration arts: Turn handrawn photos to clean linearts: Simplify freehand sketches: And more anime results:

Contact

Xiaoyu Xiang.

You can also leave your questions as issues in the repository. I will be glad to answer them!

License

This project is released under the MIT License.

Citations

@misc{Anime2Sketch,
  author = {Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen},
  title = {Anime2Sketch: A Sketch Extractor for Anime Arts with Deep Networks},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Mukosame/Anime2Sketch}}
}

@inproceedings{xiang2022adversarial,
  title={Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis},
  author={Xiang, Xiaoyu and Liu, Ding and Yang, Xiao and Zhu, Yiheng and Shen, Xiaohui and Allebach, Jan P},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  year={2022}
}