AnimeGAN - A deep learning-based image style transfer algorithm for transforming real-world photos and videos into anime-style outputs.

## Introduction to AnimeGAN AnimeGAN is a deep learning-based image style transfer algorithm that transforms real-world photos or videos into anime-style images or videos instantly. It is designed to produce high-quality, hand-drawn-like results, making it useful for creative professionals and enthusiasts looking to apply anime aesthetics to their projects. ## Key Features of AnimeGAN The key features of AnimeGAN include: - **Fast Generation**: The algorithm processes inputs quickly, enabling users to see results in seconds. - **High-Quality Results**: It produces outputs that mimic hand-drawn anime styles, enhancing visual appeal. - **Support for Images and Videos**: While the web interface focuses on image uploads, the full project supports video conversion through Python scripts. - **Multiple Styles**: AnimeGAN offers various anime styles, such as Miyazaki Hayao, Makoto Shinkai, and Kon Satoshi. - **Open-Source**: The project is open-source for non-commercial purposes, such as academic research and teaching. - **Lightweight Model**: The generator network is lightweight, making it accessible for users with limited computational resources. ## Accessing AnimeGAN for General Users General users can access AnimeGAN through the web interface at [AnimeGAN.js](https://animegan.js.org). This platform allows users to upload photos and generate anime-style images quickly and easily. However, the web interface appears to support only image uploads and not video conversion. ## Developer Functionalities in AnimeGAN Developers can access advanced functionalities of AnimeGAN through its GitHub repository. These include: - **Inference**: Running Python scripts to generate anime-style images from real photos. - **Video Conversion**: Converting videos into anime-style outputs using specific Python scripts. - **Training**: Training custom models by downloading pretrained models, datasets, and running training scripts. Developers need Python 3.7, TensorFlow-GPU 1.15.0, and specific hardware like Ubuntu with GPU 2080Ti, CUDA 10.0.130, and cuDNN 7.6.0, along with libraries like OpenCV, tqdm, numpy, glob, and argparse. ## Supported Anime Styles in AnimeGAN AnimeGAN supports multiple anime styles, including: - **Miyazaki Hayao**: Inspired by works like "The Wind Rises." - **Makoto Shinkai**: Inspired by works like "Your Name" and "Weathering with You." - **Kon Satoshi**: Inspired by works like "Paprika." These styles are derived from high-quality data, often sourced from Blu-ray (BD) movies. ## Open-Source and Licensing of AnimeGAN Yes, AnimeGAN is open-source for non-commercial purposes. It is available for academic research, teaching, and scientific publications. The licensing terms ensure that the project remains free for non-commercial use, and users should avoid commercial misuse. ## Introduction to AnimeGANv2 AnimeGANv2 is an improved version of AnimeGAN that addresses issues like high-frequency artifacts and reduces the generator network's parameters, making it easier to train and achieve paper-level effects. While it is not directly tied to the web interface [AnimeGAN.js](https://animegan.js.org), it represents an expansion of the project's scope with enhanced features. ## System Requirements for AnimeGAN To run AnimeGAN locally, developers need: - **Python 3.7** - **TensorFlow-GPU 1.15.0** - **Hardware**: Ubuntu with GPU 2080Ti, CUDA 10.0.130, and cuDNN 7.6.0 - **Libraries**: OpenCV, tqdm, numpy, glob, and argparse These requirements ensure robust performance for tasks like inference, video conversion, and model training. ### Citation sources: - [AnimeGAN](https://animegan.js.org) - Official URL Updated: 2025-03-27