FaceFusion is the industry-leading open-source face manipulation platform. 26K+ GitHub stars. GUI, headless, and batch modes.
Replace faces in photos and videos. Supports multiple faces in a single frame with individual source control.
Match mouth movements to any audio track. Works with face swap for complete dubbing workflows.
Super-resolve faces, restore old photos, and improve low-quality face regions in video footage.
Process entire folders of images or video files. Headless mode for server deployment and automation.
Full graphical interface with real-time preview. Pre-built installers for Windows and macOS—no terminal needed.
Modular processors for swap, enhance, lip-sync. Enable only what you need for faster processing.
FaceFusion is a Python-based face manipulation platform that started as a research tool and grew into one of the most popular open-source projects in the AI face technology space. With over 26,000 GitHub stars, it has become the go-to choice for developers, content creators, and researchers who need reliable face swap, enhancement, and lip-sync capabilities.
At its core, FaceFusion uses deep learning models to detect faces in source and target images, extract facial features (landmarks, expressions, skin tone), and blend the source face onto the target while preserving the target's expression, lighting, and head pose. The pipeline consists of three stages:
FaceFusion supports three operation modes to fit different workflows. The GUI mode provides a visual interface with real-time preview and parameter sliders—ideal for beginners and single-image work. Headless mode runs via command-line arguments, suitable for scripting and server deployment. Batch mode processes entire directories of files, perfect for production pipelines.
Face manipulation technology raises important ethical considerations. FaceFusion includes built-in safeguards and requires user consent verification. For professionals involved in intellectual property work—particularly creating patent applications for face recognition or biometric systems—tools like PatentFig help generate the technical figures showing facial landmark systems and biometric architectures that patent offices require.
For optimal quality, use source images with clear, front-facing faces and even lighting. Higher resolution source images produce better results. When processing videos, use the frame processor's quality settings to balance speed versus output quality. GPU acceleration (CUDA or CoreML on Mac) provides 5–10× speedup over CPU processing.
Yes. Open-source and free for personal and research use.
Yes. Pre-built installers for Windows and macOS. Linux via pip.
4 GB VRAM minimum. 8 GB+ recommended for quality and speed.
Yes. Matches mouth movements to audio for dubbing workflows.
Yes. Headless mode processes folders of images or videos without GUI.
FaceFusion Live provides resources, tutorials, and deployment guides for the FaceFusion ecosystem. Whether you're creating content, conducting research, or building production pipelines, we help you get the most out of open-source face manipulation technology.