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Use AnimeGANv3 to make your own animation works, including turning photos or videos into anime.

Home Page: https://tachibanayoshino.github.io/AnimeGANv3/

Python 99.88% Shell 0.12%
animegan animeganv2 animeganv3 tensorflow onnx colab coreml huggingface tflite

animeganv3's Introduction

AnimeGANv3

Paper Title: A Novel Double-Tail Generative Adversarial Network for Fast Photo Animation.

Let's use AnimeGANv3 to produce our own animation.

manuscript Paper Project Page HuggingFace Video twitter LICENSE Github Open In Colab Visitor

📢 Updates

  • 2023-12-10 Added a new AnimeGANv3 model for Portrait to Oil-painting style. Its onnx is available here.
  • 2023-11-23 The code and the manuscript are released. 🦃
  • 2023-10-31 Added three new styles of AnimeGANv3: Portrait to Cute, 8bit and Sketch-0 style. 👻
  • 2023-09-18 Added a new AnimeGANv3 model for Face to Kpop style.
  • 2023-01-16 Added a new AnimeGANv3-photo.exe for the inference of AnimeGANv3's onnx model.
  • 2023-01-13 Added a new AnimeGANv3 model for Face to comic style.
  • 2022-12-25 Added the tiny model (2.4MB) of Nordic myth style and USA style 2.0. It can go upto 50 FPS on iphone14 with 512*512 input. 🎅
  • 2022-11-24 Added a new AnimeGANv3 model for Face to Nordic myth style. 🦃
  • 2022-11-06 Added a new AnimeGANv3 model for Face to Disney style V1.0.
  • 2022-10-31 Added a new AnimeGANv3 model for Face to USA cartoon and Disney style V1.0. 🎃
  • 2022-10-07 The USA cartoon Style of AnimeGANv3 is integrated to ProfileProfile with Core ML. Install it by the Apple Store and have a try.
  • 2022-09-26 Official online demo is integrated to Huggingface Spaces with Gradio. Hugging Face Spaces
  • 2022-09-24 Added a new great AnimeGANv3 model for Face to USA cartoon Style.
  • 2022-09-18 Update a new AnimeGANv3 model for Photo to Hayao Style.
  • 2022-08-01 Added a new AnimeGANv3 onnx model (Colab) for Face to Arcane style.
  • 2022-07-13 Added a new AnimeGANv3 onnx model (Colab) for Face to portrait sketch.
  • 2021-12-25 The paper of AnimeGANv3 will be released in 2022. 🎄

🎮 Usage

  • Official online demo is released in Hugging Face Spaces.

  • Download this repository and use AnimeGANv3's UI tool and pre-trained *.onnx to turn your photos or videos into anime. 😊

  • 🛠️ Installation

    1. Clone repo

      git clone https://github.com/TachibanaYoshino/AnimeGANv3.git
      cd AnimeGANv3   
    2. Install dependent packages

      pip install -r requirements.txt  
    3. Inference with *.onnx

      python deploy/test_by_onnx.py -i inputs/imgs/ -o output/results -m deploy/AnimeGANv3_Hayao_36.onnx  
    4. video to anime with *.onnx

      python tools/video2anime.py -i inputs/vid/1.mp4 -o output/results -m deploy/AnimeGANv3_Hayao_36.onnx  

🚀 Landscape Demos

🔥 Video to anime (Hayao Style)


🎨 Photo to Hayao Style


more surprise 👈







🎨 Photo to Shinkai Style


more surprise 👈






🚀 Portrait Style Demos

The paper has been completed in 2022. The study of portrait stylization is an extension of the paper.

Some exhibits  👈

🎨 Face to USA cartoon style


🎨 Face to Disney cartoon style


🎨 Face to USA cartoon + Disney style

more surprise 👈


🎨 Face to Arcane style


🎨 Portrait to comic style


🎨 Face to Kpop style


🎨 Portrait to Oil-painting style

more surprise 👈


🎨 Portrait to Cute style


🎨 Portrait to 8bit style


🎨 Portrait to Sketch-0 style


🎨 Face to portrait sketch

Open In Colab

input Face panoramic image
more surprise 👈


🔨 Train

1. Download dataset and pretrained vgg19

  1. vgg19
  2. Hayao dataset
  3. Shinkai dataset
  4. photo dataset

2. Do edge_smooth

    cd tools && python edge_smooth.py --dataset Hayao --img_size 256

3. Do superPixel

    cd tools && python visual_superPixel_seg_image.py

4. Train

    python train.py --style_dataset Hayao --init_G_epoch 5 --epoch 100

✒️ Citation

Consider citing as below if you find this repository helpful to your project:

@article{Liu2024dtgan,
  title={A Novel Double-Tail Generative Adversarial Network for Fast Photo Animation},
  author={Gang LIU and Xin CHEN and Zhixiang GAO},
  journal={IEICE Transactions on Information and Systems},
  volume={E107.D},
  number={1},
  pages={72-82},
  year={2024},
  doi={10.1587/transinf.2023EDP7061}
}

📜 License

This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGANv3 given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

📧 Author

Asher Chan [email protected]

animeganv3's People

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animeganv3's Issues

use request

I am a graduate student of Beijing University of Information Technology, I am particularly interested in your AnimeGANv3, can you provide me with your unzip password? I would be grateful!

Releases?

When will, usa style onnx available?
Also where can i use new comic styles demo?

linux version

this is a feature request for linux version

thanks for your time!

[Need Info] Train ai?

Hi, so I found this awesome repo and decided to try it. But I'm currently using linux and there isn't and documentation about training the ai?. Is it possible for the collaborators to add and documentation about training?. thanks

onnxruntime failed to load library

Thanks for your great work!
There is onnxruntime error when I running the exe. Even though I can process the images, but the results seem to be not as good as hope.
Then I run "pip install onnxruntime" in the cmd window with conda base virtual environment.
But the error still exists. It's as the following:

2022-05-18 16:19:56.9664601 [E:onnxruntime:Default, provider_bridge_ort.cc:901 onnxruntime::ProviderSharedLibrary::Ensure] Failed to load library, error code: 126
2022-05-18 16:19:56.9666273 [W:onnxruntime:Default, onnxruntime_pybind_state.cc:1624 onnxruntime::python::pybind11_init_onnxruntime_pybind11_state] Init provider bridge failed.

AnimeGANv3_Trump v1.5

can anyone share AnimeGANv3_Trump v1.5 onnx cause i tested some other which are not correct animation

Could you give me demo code?

I try to use this model in browser but I do not know the tensor size and what I should do when I got a picture. I need your python demo code, I will transform to onnxjs or tensorflowjs.

关于预训练模型

作者你好,请问可以再次发布预训练模型吗,您提供的链接已经失效,万分感谢!

Dependencies Conflict

Which python version has you used ?? Dependencies conflict is encountered again and again. The conflict between libraries is: onnx==1.10.1
onnxruntime>=1.16.6
tensorflow-gpu==2.10.0

GPU与onnxruntime报错问题

哥,真神人也,小弟拜服!!

有2个问题请帮忙解惑

  • 不能够使用GPU。处理时候用的是cpu,这个怎么解决。
  • 这个报错怎么处理。。。
    2023-04-03 11:29:55.0674790 [E:onnxruntime:Default, provider_bridge_ort.cc:901 onnxruntime::ProviderSharedLibrary::Ensure] Failed to load library, error code: 126 2023-04-03 11:29:55.0675858 [W:onnxruntime:Default, onnxruntime_pybind_state.cc:1624 onnxruntime::python::pybind11_init_onnxruntime_pybind11_state] Init provider bridge failed. Making: videoplayback_AnimeGANv3.mp4: 100%|███| 158/158 [03:00<00:00, 1.14s/it] Total time : 181.23930430412292 s

About the .zip archive

I have tried several times for decomplie the models but unfortunately failed. I have realized that the magic head in the file "struct" is different from the modules you mentioned ending with with test_by_onnxZIP in HEX-editor, although I have fixed them, it still comes
bugs.
1
2

@TachibanaYoshino

Dataset Release

Thanks for the work. Will you releasing the dataset used to train the models ?

Inquiry About [Portrait to Cute style] Implementation

Hello,

Firstly, I'd like to express my appreciation for the effort you've put into AnimeGANv3.

While exploring the project, I became particularly interested in how [Portrait to Cute style] is implemented.(https://github.com/TachibanaYoshino/AnimeGANv3?tab=readme-ov-file#art-portrait-to-cute-style).

I've gone through the documentation and the code but I was wondering if you could provide a bit more insight or point me to the relevant parts of the documentation/code that would help me understand the process better.

Thank you for your time and effort. Looking forward to your response!

Best regards,

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