Swap faces in photos and videos using deep learning
You can watch the process of swapping two people's faces in real time. FaceSwap is software that uses deep learning to detect faces in photos or videos and replace them with another person's face. It provides an environment where you can learn the principles of artificial intelligence through experimentation, even without complex formulas or specialized knowledge.
Three stages: extraction, training, and conversion
FaceSwap operates in three stages: extracting faces from the original image, generating a new face with a trained model, and converting the final image. First, it cuts faces out of photos in the src folder and saves them to the extract folder. Next, it trains a model using folders containing photos of two people, leaving the results in the models folder. Finally, it applies the trained model to photos in the original folder to generate swapped images in the modified folder. You can run each stage by appending extract, train, or convert to the python faceswap.py command.
Execution environment and video processing tips
This tool is written in Python and runs on major operating systems such as Windows, Linux, and MacOS. For optimal performance, you need a modern GPU that supports CUDA, while Linux environments also support AMD GPUs via ROCm. You must check the INSTALL.md file before installation. If you prefer a GUI, you can launch the graphical interface with the python faceswap.py gui command. For video processing, you can use ffmpeg to convert videos into photos, process them, and then combine them back into a video.
Ethical boundaries set by the development team
The development team is aware of the potential for FaceSwap to be misused and has established strict ethical standards. They explicitly state that the software should not be used to change faces without consent or to create inappropriate content. Instead, they encourage use for legal and constructive purposes such as learning artificial intelligence technology, social or political criticism, and film production. Developers are working to maintain the tool as a venue for technical experimentation and education, applying a zero-tolerance policy to unethical use.
Checklist before starting your experiment
Since FaceSwap results vary significantly depending on the quantity and quality of training data, it is important to prepare sufficient photo materials. It is more efficient to start training with a model of a person with a similar appearance and gradually change the data, rather than expecting perfect results from the start. If you need support, use the Discord server or official forums, but it is best not to leave general questions in the repository issues. As this tool is still maturing technically, the process of sharing problems encountered during use in the forums and finding solutions can itself be a learning experience.
