🟡 Intermediate ⚙️ Type: Real-Time Face Swap / AI Webcam 💸 Free & Open Source ⭐ 30,000+ GitHub Stars
What is Deep-Live-Cam?
Deep-Live-Cam is a viral, open-source AI tool that allows you to perform highly realistic, real-time face swaps using just a single photograph. Unlike older generation deepfakes that required hundreds of source images and days of training, this software can instantly map a face onto a live webcam feed or a pre-recorded video with just one click.
The magic happens completely offline on your own hardware. It uses a combination of advanced machine learning models: inswapper (developed by InsightFace) to infer 3D facial structures and map them onto moving subjects, and GFPGAN to upscale and enhance the final swapped face so it looks crisp and natural.
Because it hooks directly into your system, it can output a virtual camera feed, allowing you to use your AI-swapped face live in Zoom meetings, Discord video calls, or OBS studio streams.
Who is it for?
- Streamers and VTubers who want to broadcast under a completely different realistic persona without investing in complex 3D rigging or motion-capture gear.
- VFX Artists and Video Editors who need a rapid prototyping tool to test face replacements on actors in post-production.
- Privacy Advocates who want to obscure their real identity on public web calls while maintaining natural human expressions.
- AI Enthusiasts and Researchers exploring the current state of zero-shot facial rendering running on consumer hardware.
What makes it special?
- Zero-Shot Generation — You literally only need one clear, front-facing photograph. The AI automatically understands how to rotate, light, and animate the face in 3D space.
- Live Webcam Mode — Most tools require you to wait minutes or hours to process a video clip. Deep-Live-Cam processes frames dynamically, tracking your head movements in real-time.
- Cross-Platform Hardware Support — It isn’t locked exclusively to expensive NVIDIA GPUs. The developer has optimized execution providers to run on standard CPUs, NVIDIA CUDA, and Apple Silicon (M1/M2/M3/M4) Macs.
- Built-in Face Enhancer — The integrated GFPGAN model acts like a real-time beauty filter, sharpening blurry textures and removing the “smudged” look typical of older AI face swappers.
- Virtual Camera Integration — It natively bridges with standard OS virtual camera drivers, meaning Skype, Teams, or Google Meet will simply recognize it as a standard plug-and-play webcam.
Requirements before you start
Because processing live video feeds is incredibly demanding, ensure your system is prepared:
- Python 3.10 — It is highly recommended to use exactly version 3.10. Newer versions (like 3.12) often break the ONNX runtime dependencies.
- FFmpeg — This is a mandatory system library required for decoding and encoding video frames. Download it and add it to your system PATH.
- Git — Required to fetch the codebase.
- Visual Studio C++ Build Tools (Windows Only) — Required to compile some of the background Python modules.
- Hardware — A dedicated NVIDIA GPU (RTX 3060 or better) or an Apple Silicon Mac is heavily recommended. It will run on a CPU, but the framerate will be extremely choppy.
Step-by-step installation
Step 1 — Clone the repository
Open your terminal or Command Prompt and download the code:
git clone https://github.com/hacksider/Deep-Live-Cam.git
cd Deep-Live-CamStep 2 — Set up a Virtual Environment
To prevent dependency conflicts, create a clean Python sandbox. Make sure you are using Python 3.10!
python -m venv venvActivate it:
- Windows:
venv\Scripts\activate - Mac/Linux:
source venv/bin/activate
Step 3 — Download the Core AI Models
The repository does not include the heavy neural network weights. You must download two specific files manually:
- GFPGANv1.4.pth (For face enhancement)
- inswapper_128_fp16.onnx (The core face swap brain)
You can find direct links to these on the project’s GitHub README. Once downloaded, drag and drop both files directly into the models/ folder inside your Deep-Live-Cam directory.
Step 4 — Install the dependencies
Now, install the required Python packages:
pip install -r requirements.txt(Note for NVIDIA users: To get fast real-time speeds, you must also install the CUDA toolkit and the specific ONNX-GPU runtime: pip install onnxruntime-gpu)
Step 5 — Run the Application
Once everything is installed, launch the graphical interface:
python run.pyIf you set up an NVIDIA GPU, launch it with the execution provider flag to activate hardware acceleration:
python run.py --execution-provider cudaThe GUI will pop up on your screen. Select your source face image, click “Live”, and wait 10-20 seconds for the models to load into memory. You’re now swapping faces in real-time!
Common errors and fixes
| Error | What it means | How to fix it |
|---|---|---|
No face detected in source image | The AI cannot find the mathematical landmarks of the face you uploaded. | Use a clear, front-facing portrait photo with good lighting. Do not use images with glasses, extreme angles, or heavy shadows. |
ModuleNotFoundError: No module named 'onnxruntime' | The Python ONNX engine failed to install during the requirements step. | Run pip install onnxruntime (or onnxruntime-gpu if you have an NVIDIA card) manually in your active virtual environment. |
| The webcam feed is extremely laggy (1-5 FPS) | The software is defaulting to your CPU instead of utilizing your graphics card. | Ensure you launch the app with --execution-provider cuda (NVIDIA) or --execution-provider coreml (Mac). You may also need to turn off the “Face Enhancer” checkbox in the UI, as it doubles the rendering workload. |
Free vs Paid comparison
| Feature | Deep-Live-Cam (Free Open Source) | Commercial Face Swap Apps (e.g., Reface) |
|---|---|---|
| Cost | $0 Forever | $10–$30+ per month |
| Offline Privacy | ✅ Absolute — no data leaves your PC | ❌ Your photos are uploaded to corporate servers |
| Live Streaming Capability | ✅ Yes — via virtual webcam output | ❌ Usually limited to pre-recorded video clips |
| Setup Difficulty | ⚠️ High — requires Python and model management | 🟢 Low — just download from the App Store |
Bottom line: If you want to mask your identity on live video calls or create highly realistic swapped videos for free, Deep-Live-Cam is the most powerful local tool available. However, because the setup requires manually dealing with Python and hardware drivers, it is not for the faint of heart. If you just want to put your face on a funny GIF for social media, stick to a simple mobile app.
Alternatives — 3 similar tools
1. FaceFusion
Often considered the direct “rival” to Deep-Live-Cam, FaceFusion features an arguably cleaner UI and easier installation process (using an automated pinokio installer). It supports a wider variety of enhancement models but can sometimes be heavier on system resources.
🔗 github.com/facefusion/facefusion
2. Roop (Discontinued)
The original one-click face swapper that started the zero-shot trend. While the original repository was officially discontinued by its creator due to ethical concerns, it laid the groundwork and mathematical models that Deep-Live-Cam and FaceFusion currently build upon.
3. DeepFaceLive
An older, highly complex tool designed specifically for power users. It lacks the “one-click” ease of Deep-Live-Cam and requires you to train specific face models manually. However, if you spend the days required to train a custom model, the visual tracking fidelity is often superior for professional streaming.
🔗 github.com/iperov/DeepFaceLive
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