Skip to content

hutx/roop-unleashed

 
 

Repository files navigation

roop-unleashed

ChangelogInstallationUsageExampleFAQ

Uncensored Deepfakes for images and videos without training and an easy-to-use GUI.

Screen

Features

  • Platform-independant Browser GUI
  • Selection of multiple input/output faces in one go
  • Many different swapping modes, first detected, face selections, by gender
  • Batch processing of images/videos
  • Masking of face occluders using text prompts
  • Optional Face Restoration using different enhancers
  • Preview swapping from different video frames
  • Live Fake Cam using your webcam
  • Extras Tab for cutting videos etc.
  • Settings - storing configuration for next session
  • Theme Support

and lots more...

Disclaimer

This project is for technical and academic use only. Users of this software are expected to use this software responsibly while abiding the local law. If a face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users. Please do not apply it to illegal and unethical scenarios.

In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability

Installation

For Windows, you need to download and install Visual Studio (in theory build-tools might work too but in my experience so far they don't). During the install, make sure to include the C++ package.

Besides that, just use the 1-click installer in releases. This will download and install everything in a handy conda environment. This not only installs the application but also runs it, once installed.

For other OS or if you know what you're doing:

  • git clone https://github.com/C0untFloyd/roop-unleashed
  • preferably create a venv or conda environment
  • cd roop-unleashed
  • pip install -r requirements.txt

Depending on your available GPU there are additional packages you need to install. Here are the instructions from the original roop page:

Using GPU Acceleration

The used GPU Provider is configured in the settings tab, no need to use cmdline arguments any more. Default is CUDA (for NVIDIA). If you change it, please restart roop-unleashed completely to allow for model reloading.

For Video face-swapping you also need to have ffmpeg properly installed (having it in your PATH Env). The windows installer tries to do this automatically.

Usage

  • Windows: run the windows_run.bat from the Installer.
  • Linux: python run.py
Open In Colab

Additional commandline arguments are currently unsupported and settings should be done via the UI.

Note: When you run this program for the first time, it will download some models roughly ~2Gb in size.

Example

Coming soon

Changelog

11.8.2023 v2.7.0

Initial Gradio Version - old TkInter Version now deprecated

  • Re-added unified padding to face enhancers
  • Fixed DMDNet for all resolutions
  • Selecting target face now automatically switches swapping mode to selected
  • GPU providers are correctly set using the GUI (needs restart currently)
  • Local output folder can be opened from page
  • Unfinished extras functions disabled for now
  • Installer checks out specific commit, allowing to go back to first install
  • Updated readme for new gradio version
  • Updated Colab

Acknowledgements

Lots of ideas, code or pre-trained models used from the following projects:

https://github.com/deepinsight/insightface https://github.com/s0md3v/roop https://github.com/AUTOMATIC1111/stable-diffusion-webui https://github.com/Hillobar/Rope https://github.com/janvarev/chain-img-processor https://github.com/TencentARC/GFPGAN
https://github.com/kadirnar/codeformer-pip https://github.com/csxmli2016/DMDNet

Thanks to all developers!

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 96.7%
  • Jupyter Notebook 2.1%
  • Batchfile 1.2%