This is the code from deepfakes' faceswap project.

You'll find here the code for the project. The original code is in the first commits of the master.

• Read this README entirely
• Fork the repo
• Download the data with the link provided below
• Play with it
• Check issues with the 'dev' tag

• Read this README entirely
• Clone the repo
• Download the data with the link provided below
• Play with it
• Check issues with the 'advuser' tag

Sadly the project is not yet ready to use for non technical people, it will improve over time, so check for updates regularly and discuss with other. What you can do for now is collect the photos you want to see processed and shere them with others. Submit them with a comment here

Sorry no time for you

The project has multiple entry points. You will have to:

• Gather photos (or use the one provided in the training data provided below)
• Extract faces from your raw photos
• Train a model on your photos (or use the one provided in the training data provided below)
• Convert your sources with the model

From your setup folder, run python extract.py. This will take photos from src folder and extract faces into extract folder.

From your setup folder, run python train.py. This will take photos from data/trump and data/cage folder and train a model that will be saved inside the models folder.

From your setup folder, run python convert_photo.py. This will take photos from original folder and apply new faces into modified folder.

Note: there is no conversion for video yet. You can use MJPG to convert video into photos,, process images, and convert images back to video

Whole project with training images and trained model (~300MB):
https://anonfile.com/p7w3m0d5be/face-swap.zip or click here to download

Clone the repo and setup you environment. There is a Dockerfile that should kickstart you. Otherwise you can setup things manually, see in the Dockerfiles for dependencies

Main Requirements: Python 3 Opencv 3 Tensorflow 1.3+(?) Keras 2

You also need a modern GPU with CUDA support for best performance

Some tips:

Reuse existing models will train much faster than start from nothing.
If there are not enough training data, start with someone looks similar, then switch the data.