LC_NeoRefacer/README.md

# Refacer: One-Click Deepfake Multi-Face Swap Tool

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/xaviviro/refacer/blob/master/notebooks/Refacer_colab.ipynb)

👉 [Watch demo on Youtube](https://youtu.be/mXk1Ox7B244)

Refacer, a simple tool that allows you to create deepfakes with multiple faces with just one click! This project was inspired by [Roop](https://github.com/s0md3v/roop) and is powered by the excellent [Insightface](https://github.com/deepinsight/insightface). Refacer requires no training - just one photo and you're ready to go.

:warning: Please, before using the code from this repository, make sure to read the [disclaimer](https://github.com/xaviviro/refacer/tree/main#disclaimer).

## Demonstration

![demonstration](demo.gif)

[![Watch the video](https://img.youtube.com/vi/mXk1Ox7B244/maxresdefault.jpg)](https://youtu.be/mXk1Ox7B244)


## System Compatibility

Refacer has been thoroughly tested on the following operating systems:

| Operating System | CPU Support | GPU Support |
| ---------------- | ----------- | ----------- |
| MacOSX           | ✅         | :warning:         |
| Windows          | ✅         | ✅         |
| Linux            | ✅         | ✅         |

The application is compatible with both CPU and GPU (Nvidia CUDA) environments, and MacOSX(CoreML) 

:warning: Please note, we do not recommend using `onnxruntime-silicon` on MacOSX due to an apparent issue with memory management. If you manage to compile `onnxruntime` for Silicon, the program is prepared to use CoreML.

## Prerequisites

Ensure that you have `ffmpeg` installed and correctly configured. There are many guides available on the internet to help with this. Here are a few (note: I did not create these guides):

- [How to Install FFmpeg](https://www.hostinger.com/tutorials/how-to-install-ffmpeg)


## Installation

Refacer has been tested and is known to work with Python 3.10.9, but it is likely to work with other Python versions as well. It is recommended to use a virtual environment for setting up and running the project to avoid potential conflicts with other Python packages you may have installed.

Follow these steps to install Refacer:

1. Clone the repository:
    ```bash
    git clone https://github.com/xaviviro/refacer.git
    cd refacer
    ```

2. Download the Insightface model:
   You can manually download the model created by Insightface from this [link](https://huggingface.co/deepinsight/inswapper/resolve/main/inswapper_128.onnx) and add it to the project folder. Alternatively, if you have `wget` installed, you can use the following command:
    ```bash
    wget --content-disposition https://huggingface.co/deepinsight/inswapper/resolve/main/inswapper_128.onnx
    ```

3. Install dependencies:

    * For CPU (compatible with Windows, MacOSX, and Linux):
    ```bash
    pip install -r requirements.txt
    ```

    * For GPU (compatible with Windows and Linux only, requires a NVIDIA GPU with CUDA and its libraries):
    ```bash
    pip install -r requirements-GPU.txt
    ```

     * For CoreML (compatible with MacOSX, requires Silicon architecture):
    ```bash
    pip install -r requirements-COREML.txt
    ```

    For more information on installing the CUDA necessary to use `onnxruntime-gpu`, please refer directly to the official [ONNX Runtime repository](https://github.com/microsoft/onnxruntime/).

For more details on using the Insightface model, you can refer to their [example](https://github.com/deepinsight/insightface/tree/master/examples/in_swapper).


## Usage

Once you have successfully installed Refacer and its dependencies, you can run the application using the following command:

```bash
python app.py
```

Then, open your web browser and navigate to the following address:

```
http://127.0.0.1:7680
```


## Questions?

If you have any questions or issues, feel free to [open an issue](https://github.com/xaviviro/refacer/issues/new) or submit a pull request.


## Recognition Module

The `recognition` folder in this repository is derived from Insightface's GitHub repository. You can find the original source code here: [Insightface Recognition Source Code](https://github.com/deepinsight/insightface/tree/master/web-demos/src_recognition)

This module is used for recognizing and handling face data within the Refacer application, enabling its powerful deepfake capabilities. We are grateful to Insightface for their work and for making their code available.


## License

Note: This project uses a Custom MIT License. See LICENSE for full terms.

The generated content does not represent the views, beliefs, or attitudes of the authors of this Software. Please use the Software and its outputs responsibly, ethically, and with respect toward others.