DeepFaceLab
The industry-leading open-source framework for high-fidelity neural face swapping and video synthesis.
Professional-grade deep learning face replacement with localized, hardware-accelerated orchestration.

FaceSwap-WebUI is a high-performance, open-source orchestration layer for the deepfakes/faceswap project and related InsightFace/Gradio implementations. As of 2026, it serves as the industry standard for local-first synthetic media generation, leveraging Python-based backends and CUDA/ROCm acceleration. The technical architecture follows a three-stage pipeline: Extraction (identifying and aligning faces using MTCNN or S3FD), Training (utilizing GAN-based architectures such as DFaker, Villian, or RealFace to learn identity mappings), and Conversion (applying the learned model to target footage with advanced masking). Unlike cloud-based SaaS alternatives, FaceSwap-WebUI provides granular control over latent space dimensions, epsilon values, and temporal consistency filters. In the 2026 landscape, it has evolved to support Real-Time Neural Textures and Zero-Shot swapping via pre-trained transformers, making it a critical tool for VFX studios and privacy-conscious researchers who require air-gapped processing. The interface is primarily Gradio-based, allowing for remote browser-based control of localized compute clusters, effectively bridging the gap between CLI-based research tools and professional creative suites.
FaceSwap-WebUI is a high-performance, open-source orchestration layer for the deepfakes/faceswap project and related InsightFace/Gradio implementations.
Explore all tools that specialize in automated face extraction. This domain focus ensures FaceSwap-WebUI delivers optimized results for this specific requirement.
Explore all tools that specialize in neural network model training. This domain focus ensures FaceSwap-WebUI delivers optimized results for this specific requirement.
Explore all tools that specialize in video-to-video face replacement. This domain focus ensures FaceSwap-WebUI delivers optimized results for this specific requirement.
Explore all tools that specialize in occlusion-aware masking. This domain focus ensures FaceSwap-WebUI delivers optimized results for this specific requirement.
Explore all tools that specialize in temporal consistency smoothing. This domain focus ensures FaceSwap-WebUI delivers optimized results for this specific requirement.
Explore all tools that specialize in identity swapping. This domain focus ensures FaceSwap-WebUI delivers optimized results for this specific requirement.
A custom-trained segmentation model that allows users to manually label occlusions and teach the AI what parts of a face to ignore.
Generative Adversarial Network post-processing to restore skin texture and high-frequency details lost during initial swapping.
Frame-to-frame alignment stabilization using optical flow algorithms.
Supports Histogram, Seamless, and Reinhard color matching to blend the source face's skin tone with target lighting.
Advanced ID-tracking to independently swap multiple subjects in a single frame.
Native support for TensorRT (NVIDIA) and DirectML (Windows/AMD) for 2x-3x speed improvements.
Standardized .p format for sharing trained weights across the community.
Verify NVIDIA/AMD GPU drivers are updated to support CUDA 12.x or ROCm 6.x.
Install Python 3.10+ and Git on the host machine.
Clone the official repository from GitHub using 'git clone'.
Initialize a virtual environment to prevent dependency conflicts.
Run the setup.py script to install Keras, TensorFlow/PyTorch, and OpenCV dependencies.
Download required pre-trained weights for alignment models (S3FD/MTCNN).
Launch the WebUI using the 'python faceswap.py gui' command.
Configure the 'Extract' tab by pointing to the source and target video directories.
Execute the training phase (optional) or select a pre-trained model for conversion.
Initiate the 'Convert' process with chosen color correction and masking settings.
All Set
Ready to go
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"Highly praised for its technical depth and lack of censorship, though criticized for a steep learning curve and high hardware requirements."
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