How we make this list: we compare pricing pages, product documentation and public user reviews. Ratings come from G2, Capterra or Trustpilot, with the date we checked them.
Key takeaways
- Open source face swap tools run on your own computer, so source photos and videos do not need to go to a cloud server.
- FaceFusion is the easiest starting point for most users, while DeepFaceLab offers deeper control for trained models.
- A capable GPU makes video work practical, while CPU-only machines will be slow and are best for single photos.
- Every tool on this list is free to download, but hardware, setup time, and learning time are the real costs.
- Only use face swap tools with clear consent from every identifiable person in the source and target material, and check local laws before publishing results.
Quick comparison
| Tool | Best for | Starting price | Free plan |
|---|
 FaceFusion | Local photo and video swaps | $0 per month | Yes |
 DeepFaceLab | Trained, high-quality models | $0 per month | Yes |
 DeepFaceLive | Live camera swaps | $0 per month | Yes |
 Deep-Live-Cam | Single-photo live swaps | $0 per month | Yes |
 InsightFace | Developer face swap library | $0 per month | Yes |
 FaceSwap | Community deepfake training | $0 per month | Yes |
 Roop | Simple single-image swaps | $0 per month | Yes |
 SimSwap | Research and identity transfer | $0 per month | Yes |
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Starting price$0 per month
Free planYes
FaceFusion is the best starting point for most people interested in open source face swapping. It runs on Windows, macOS, and Linux, offers a graphical interface, and handles photos and videos without sending files to a server. Models for swapping, enhancement, and related face tasks are included as processors you can combine.
Setup is simpler than older training tools, with pre-built installers for some systems, though a GPU with CUDA or Apple Silicon support makes a large difference to speed. CPU-only processing works for a single photo but will feel slow for video. FaceFusion suits creators, researchers, and developers who want local control and are willing to learn its settings.
What we like- Runs locally with no cloud upload
- Photo and video support
- Graphical interface plus batch workflows
Watch out for- Needs a capable GPU for smooth video work
- Many settings can overwhelm new users
Starting price$0 per month
Free planYes
DeepFaceLab has powered many professional and research face swap projects for years. Unlike one-click swappers, it lets you extract faces, train a model on a specific person, and refine the result through multiple stages. That control is why its output can look more consistent across a long video than a single-image swap.
The cost is effort. You need a Windows machine with a strong GPU, patience for training, and time to learn extraction, training, and merging steps. DeepFaceLab fits filmmakers, VFX learners, and researchers who want maximum quality and control, not casual users who want a result in a minute.
What we like- Deep control over training and model quality
- Large community and tutorials
- Strong results for video
Watch out for- Steep learning curve
- Training a good model takes time and GPU power
Starting price$0 per month
Free planYes
DeepFaceLive takes a trained face model and applies it to a live camera feed in real time. Streamers and presenters can feed the output into OBS or video calls as a virtual camera, which makes it useful for character work, privacy-preserving presentation, and creative streaming with consenting participants.
You still need a trained model first, usually from DeepFaceLab, and a GPU strong enough to hold frame rate. Lighting changes, fast head turns, and occlusions can break the illusion, so test before a live session. DeepFaceLive is a specialist tool, but for live use, few open source options match it.
What we like- Real-time face swap for camera feeds
- Works with streaming tools
- Uses trained DeepFaceLab models
Watch out for- Needs a trained model and a strong GPU
- Setup is technical
Starting price$0 per month
Free planYes
Deep-Live-Cam became popular because it removes the training step. Upload one photo of a face, point a camera at yourself, and the tool swaps the face in real time. That simplicity makes it useful for quick demos, character streams, and creative projects where training a full model would be excessive.
Quality depends heavily on the source photo and live conditions. A clear, front-facing photo works best, while side angles, glasses, and harsh lighting reduce realism. The project is community-maintained, so check the current repository status and instructions before installing. Deep-Live-Cam fits users who want live swapping without training.
What we like- Works from a single source photo
- Real-time camera output
- No model training step
Watch out for- Single-photo quality varies with pose and lighting
- Project maintenance can change over time
Starting price$0 per month
Free planYes
InsightFace is a face analysis library rather than a finished consumer app. Developers use its recognition, detection, and swapping models inside their own tools, pipelines, and research projects. That flexibility makes it a foundation for many face swap applications, including some commercial products built on open models.
Non-developers will find InsightFace hard to use alone, since it requires Python, model setup, and code to produce a finished swap. For engineers building a product, testing detection, or studying face models, it is a strong, well-documented base. For a quick photo swap, FaceFusion is a better first stop.
What we like- Strong face recognition and swapping models
- Well suited to code projects
- Active research use
Watch out for- Developer-focused, with no simple consumer app
- Integration work is required
Starting price$0 per month
Free planYes
FaceSwap is a community deepfake project with a full pipeline for extracting faces, training models, and converting video. It runs on Windows, macOS, and Linux, and its guides cover each stage in detail. Users who want to understand how deepfake training works, step by step, will learn a lot from this project.
Like DeepFaceLab, FaceSwap rewards patience and GPU power. Training takes hours for good results, and the interface feels more like a research tool than a consumer app. It suits learners, researchers, and creators who want an open training workflow and are comfortable following technical guides.
What we like- Full training pipeline for photos and video
- Cross-platform support
- Large community guides
Watch out for- Setup and training take time
- Interface is less polished than FaceFusion
Starting price$0 per month
Free planYes
Roop popularized the one-image face swap: give it a source face and a target photo or video, and it swaps without training. That simplicity made it widely forked and adapted, and many later tools borrowed its approach. For a single photo, Roop can still produce a usable result quickly on modest hardware.
The original repository is archived, so new users should check active forks and community guidance for current installation steps and fixes. Video results can flicker or lose consistency across frames compared with trained models. Roop fits quick photo experiments and developers studying single-image swapping, with consent and care.
What we like- Very simple single-image workflow
- Fast for photos
- Low setup compared with training tools
Watch out for- Original project is archived, so check forks for updates
- Video quality is limited
Starting price$0 per month
Free planYes
SimSwap is an academic project for high-fidelity face swapping that preserves the target pose and expression while transferring identity from a source image. Researchers cite it for its approach to identity transfer, and developers use its code to test swapping ideas and compare methods. It is valuable as a research reference, not as a one-click product.
Setup requires Python, dependencies, and a GPU for practical use, and output quality depends on model weights and input images. SimSwap fits students, researchers, and engineers who want to study or extend face swap methods. Casual users should start with FaceFusion instead.
What we like- Research-grade identity transfer model
- Useful for studying face swapping methods
- Code available for experiments
Watch out for- Research code, not a polished app
- Needs developer setup and GPU for best results
How we chose
We reviewed project repositories, documentation, and public discussion on GitHub and Reddit, and we compared local processing, photo and video support, GPU needs, setup difficulty, and maintenance status. We favored projects that are genuinely open source, still usable in 2026, and distinct in purpose, from live swapping to trained models to developer libraries. Where a project is archived or fork-dependent, we noted that limit rather than hiding it.
Face swapping can be used for film, research, accessibility, character performance, and creative work with consent, but it can also harm people when used without permission. Only swap faces of people who have clearly agreed, keep records of consent for commercial work, and do not use these tools to impersonate someone, mislead viewers, or create intimate imagery of a real person. Laws on synthetic media differ by country and state, and some require disclosure of AI-generated content.
Hardware and setup basics
A GPU with at least 8 GB of video memory makes video work far more practical, while Apple Silicon machines handle many local tools well through their own acceleration. Install Python, drivers, and dependencies exactly as the project guide states, and use a separate folder for source material and outputs. Start with a short clip or single photo, check quality and consent, and only then scale to longer videos.
When to pick a trained model
Single-image tools are fast but can flicker in video and struggle with side angles. Trained models from DeepFaceLab or FaceSwap take longer to build but hold identity more steadily across frames, expressions, and lighting changes. For a short social clip, a single-image tool may be enough. For a film scene or a long presentation, training is worth the time.
Frequently asked questions
Are open source face swap tools really free?
Yes, the code is free to download and use under its license, with no subscription for local processing. The real costs are hardware, electricity, and your time for setup and learning. Some projects offer paid installers or hosting, but the core code remains free.
Do I need a GPU for face swapping?
A GPU is strongly recommended for video and live work, since CPU processing is slow and can take many times longer. Single photos can run on a CPU, though still slowly on older machines. A GPU with 8 GB or more of video memory is a practical minimum for regular video use.
Is face swapping legal?
Legality depends on consent, purpose, and local law. Swapping your own face, or a face with clear consent, for creative or research work is different from impersonating someone or creating non-consensual imagery, which is illegal in many places. Check local rules and platform policies before publishing any result.
What is the difference between FaceFusion and DeepFaceLab?
FaceFusion focuses on fast local swaps from a source image or video, with a graphical interface and batch options. DeepFaceLab focuses on training a model for a specific person, which takes longer but can give more consistent video results. Start with FaceFusion for quick work, and move to DeepFaceLab for high-quality video projects.
Can I run these tools on a Mac?
Yes, several tools support macOS, including FaceFusion, FaceSwap, and InsightFace-based projects, often with Apple Silicon acceleration. Performance varies by model and chip, and some Windows-only steps in older guides may need adaptation. Check the current repository instructions for your Mac model.
Why does my swap look unrealistic?
Common causes are poor source photos, mismatched lighting, side angles, occlusions such as hands or glasses, and low resolution. Use a clear, front-facing source photo with similar lighting to the target, and test a short clip before processing a full video. Trained models also handle difficult shots better than single-image swaps.
Sources we checked (8)
- https://github.com/facefusion/facefusion
- https://github.com/iperov/DeepFaceLab
- https://github.com/iperov/DeepFaceLive
- https://github.com/hacksider/Deep-Live-Cam
- https://github.com/deepinsight/insightface
- https://github.com/deepfakes/faceswap
- https://github.com/s0md3v/roop
- https://github.com/neuralchen/SimSwap
Is your product missing or out of date?
We review this list regularly. Vendors can request a listing, send updated pricing or ask about featured placement.
Shihab Mia · Founder, Rankite
Shihab has 5+ years in B2B and SaaS SEO. He writes Rankite's software buying guides from pricing pages, documentation and public user reviews. More about Shihab.