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The digital solution for your professional 2D animation projects.

The next-generation professional face swapper and enhancer for high-fidelity synthetic media.

FaceFusion is a highly modular, Python-based framework designed for sophisticated facial reconstruction and enhancement. Architected to surpass the limitations of legacy projects like Roop, FaceFusion utilizes the ONNX runtime to provide high-performance execution across diverse hardware backends, including NVIDIA CUDA, AMD ROCm, and Apple CoreML. By 2026, it has matured into a critical asset for the 'Local-First' AI movement, allowing creators to perform complex face-swapping, temporal smoothing, and high-resolution upscaling without relying on cloud-based SaaS providers. The tool's technical architecture is built around a 'Processor' logic, where independent modules handle face-swapping, face-enhancement, frame-enhancement, and lip-syncing in a sequential or parallel pipeline. This approach ensures maximum flexibility for professional post-production workflows, enabling users to achieve cinema-grade temporal consistency and texture mapping. Its open-source nature ensures total privacy, making it a preferred choice for industries requiring strict data governance and for researchers pushing the boundaries of identity-preserving generative AI.
FaceFusion is a highly modular, Python-based framework designed for sophisticated facial reconstruction and enhancement.
Explore all tools that specialize in face enhancement. This domain focus ensures FaceFusion delivers optimized results for this specific requirement.
Allows stacking of Face Swapper, Face Enhancer, Frame Enhancer, and Lip Syncer in a single execution pass.
Supports CUDA, DirectML, CoreML, OpenVINO, and ROCm for cross-platform hardware optimization.
Uses regional masking and occlusion handling to ensure swapper faces appear correctly behind objects like hands or glasses.
Applies weighted averages across frames to eliminate the 'flickering' effect common in earlier deepfake technologies.
Advanced filtering based on gender, age, or specific facial similarity indexing.
Integrates GFPGAN, CodeFormer, and RestoreFormer++ for high-resolution facial reconstruction.
All GUI functions are available via Command Line Interface for headless server integration.
Ensure Python 3.10.x and Git are installed on the host system.
Install FFmpeg for media stream handling and codec support.
Clone the official FaceFusion repository from GitHub.
Create a dedicated Python virtual environment to isolate dependencies.
Install required packages using 'pip install -r requirements.txt'.
Configure the specific execution provider (e.g., pip install onnxruntime-gpu for NVIDIA users).
Launch the application via 'python run.py' to initiate the automated model download sequence.
Select the 'Face Swapper' and 'Face Enhancer' modules in the Gradio UI.
Upload source and target assets and set the reference face distance threshold.
Execute processing and monitor GPU/CPU utilization in the terminal.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its modularity and output quality. Users value the lack of censorship and local privacy."
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