Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Mukosame/Anime2Sketch

A sketch extractor for anime/illustration.
https://github.com/Mukosame/Anime2Sketch

anime comic computer-vision deep-learning gan gans generative-adversarial-network gradio image-generation manga pytorch sketch wacv

Last synced: 3 months ago
JSON representation

A sketch extractor for anime/illustration.

Host: GitHub
URL: https://github.com/Mukosame/Anime2Sketch
Owner: Mukosame
License: mit
Created: 2021-04-09T00:26:49.000Z (over 3 years ago)
Default Branch: master
Last Pushed: 2023-08-16T06:44:46.000Z (about 1 year ago)
Last Synced: 2024-04-18T20:37:59.590Z (7 months ago)
Topics: anime, comic, computer-vision, deep-learning, gan, gans, generative-adversarial-network, gradio, image-generation, manga, pytorch, sketch, wacv
Language: Python
Homepage:
Size: 16.8 MB
Stars: 1,887
Watchers: 25
Forks: 158
Open Issues: 6
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-acg - Anime2Sketch - A sketch extractor for anime/illustration. [English] (Image Generation)
StarryDivineSky - Mukosame/Anime2Sketch

README

        # Anime2Sketch

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

By [Xiaoyu Xiang](https://engineering.purdue.edu/people/xiaoyu.xiang.1)

![teaser demo](demos/vinland_saga.gif)

## Updates

- 2022.1.14: Add Docker environment by [**kitoria**](https://github.com/kitoriaaa)

- 2021.12.25: Update README. Merry Christmas!

- 2021.5.24: Fix an interpolation error and a GPU inference error.

- 2021.5.12: [Web Demo](https://gradio.app/g/AK391/Anime2Sketch) by [**AK391**](https://github.com/AK391)

![gradio_web_demo](figures/gradiodemo.png)

- 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"](https://arxiv.org/abs/2104.05703).

## Prerequisites

- Linux, macOS, Docker

- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))

- CPU or NVIDIA GPU + CUDA CuDNN

- [Pillow](https://pillow.readthedocs.io/en/stable/), [PyTorch](https://pytorch.org/)

## Get Started

### Installation 

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

### Download Pretrained Weights

Please download the weights from [GoogleDrive](https://drive.google.com/drive/folders/1Srf-WYUixK0wiUddc9y3pNKHHno5PN6R?usp=sharing), and put it into the [weights/](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](https://drive.google.com/file/d/1cf90_fPW-elGOKu5mTXT5N1dum-XY_46/view?usp=sharing), and put it into [weights/](weights/) folder.

### Test

```Shell

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:

```Shell

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  

```Shell

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  

```Shell

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](https://github.com/Mukosame/AODA). Please check it for the training instructions.

## More Results

Our model works well on illustration arts:

![madoka demo](demos/madoka_in_out.png)

![demo1](demos/demo1_in_out.png)

Turn handrawn photos to clean linearts:

![demo2](demos/demo2_in_out.png)

Simplify freehand sketches:

![demo3](demos/demo3_in_out.png)

And more anime results:

![demo4](demos/vinland_3.gif)

![demo5](demos/vinland_1.gif)

## Contact

[Xiaoyu Xiang](https://engineering.purdue.edu/people/xiaoyu.xiang.1).

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](LICENSE).

## Citations

```BibTex

@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}

}

```