LipGAN
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
Enterprise-grade high-throughput face swapping optimized for NVIDIA TensorRT acceleration.
FaceSwap-TensorRT represents the pinnacle of high-performance face replacement technology for the 2026 AI landscape. Built on NVIDIA's TensorRT SDK, this tool is designed to bypass the latency bottlenecks common in standard ONNX or PyTorch implementations. It utilizes the InsightFace (inswapper_128) architecture, converted into highly optimized .engine files that leverage FP16 and INT8 quantization for maximum throughput. This allows for real-time, 60+ FPS face swapping on consumer-grade NVIDIA hardware (RTX 30/40/50 series). The architecture is decoupled into a modular pipeline: face detection via SCRFD, landmark extraction, and the swap inference, all managed within a shared GPU memory space to minimize PCIe overhead. As a critical component in production-scale generative video workflows, it serves developers building live-streaming applications, VFX pipelines, and privacy-focused data obfuscation tools. By 2026, it has become the standard for low-latency identity modification in decentralized compute environments and high-end creative studios seeking to scale their video processing without the heavy VRAM footprint of traditional GAN-based models.
Converts 32-bit floating-point weights to 16-bit, halving memory usage and significantly increasing throughput on Tensor Core GPUs.
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
Photorealistic 4k upscaling via iterative latent space reconstruction.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Enables the engine to handle varying input resolutions without re-compiling the TensorRT model.
Passes image buffers between detection and swapping stages within GPU VRAM.
Uses Sample and Computation Redistribution for Efficient Face Detection for sub-millisecond face localization.
Pre-compiles the neural network into a hardware-specific binary file (.engine).
Applies a Kalman filter to facial landmarks across video frames to prevent 'jitter'.
Utilizes Poisson blending and alpha-masking to integrate the swapped face into the target lighting environment.
Whistleblowers or high-profile individuals needing to stream video while maintaining complete facial anonymity.
Registry Updated:2/7/2026
Matching the lip movements of an actor to a different language's audio track in post-production.
Enhancing or 'de-aging' historical figures in archive footage for educational media.