deepfakes/faceswap
Deepfakes Software For All
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
Loading star history...
Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 319 days ago, with 81 contributors on GitHub.
Peaked at #41 in the weekly ranking the week of Jul 26, its best in the past year.
Use Cases & Benefits
- Utilizes deep learning to detect and swap faces in images and videos through extraction, training, and conversion processes.
- Offers an open-source, cross-platform solution with a GUI and multiple models, enabling accessible and customizable face swapping.
- Use for creating ethical face swap videos or images for educational and entertainment purposes with user-controlled training.
- Use for experimenting with AI and neural network techniques to learn and develop generative face swapping models.
- Use for integrating face swapping capabilities into multimedia projects requiring realistic face replacement on Windows, Linux, or macOS.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
GPL 3.0
Copyleft: distributing software that includes it generally requires releasing your source under the same license.
Community files
- README: present
- Code of conduct: present
Watch-outs
- GPL-style copyleft: including it in distributed software generally requires releasing your source under the same license.
General guidance from repository metadata, not legal advice. How we score
From the README
FaceSwap is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.
Emma Stone/Scarlett Johansson FaceSwap using the Phaze-A model
Jennifer Lawrence/Steve Buscemi FaceSwap using the Villain model
Make sure you check out INSTALL.md before getting started.
When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. It ran, it worked, and as is so often the way with new technology emerging on the internet, it was immediately used to create inappropriate content. Despite the inappropriate uses the software was given originally, it was the first AI code that anyone could download, run and learn by experimentation without having a Ph.D. in math, computer theory, psychology, and more. Before "deepfakes" these techniques were like black magic, only practiced by those who could understand all of the inner workings as described in esoteric and endlessly complicated books and papers.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
13.4K-71
Open issues
21+10
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| rohitg00/ai-engineering-from-scratch | 78.3K | +1.6K | 75 | 1 day ago |
| deepfakes/faceswap | 57.6K | — | 43 | 319 days ago |
| SYSTRAN/faster-whisper | 26.5K | — | 40 | 368 days ago |
| GeeeekExplorer/nano-vllm | 16.3K | — | 12 | — |
| Lightning-AI/litgpt | 13.8K | — | 58 | 129 days ago |
| data-privacy-stack/presidio | 11.8K | — | 64 | 10 days ago |
| Category median | 16.3K | — | 58 | — |
Discover Repositories
Search across tracked repositories by name or description