Mukosame/Anime2Sketch
PythonA sketch extractor for anime/illustration.
pip install anime2sketchAnime2Sketch
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
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}
}