DeepFaceLive

Overview

DeepFaceLive is a free, open-source tool that replaces your face in real-time during live video streams and calls. Rather than swapping faces in recorded footage after the fact, it processes webcam input frame-by-frame using GPU-accelerated AI, showing the processed result in a DeepFaceLive output window. For video calls or streaming, capture that window in OBS and use OBS Virtual Camera as the camera input for Zoom, Skype, Discord, or similar apps.

DeepFaceLive is a free, open-source tool that replaces your face in real-time during live video streams and calls. Rather than swapping faces in recorded footage after the fact, it processes webcam input frame-by-frame using GPU-accelerated AI, showing the processed result in a DeepFaceLive output window. For video calls or streaming, capture that window in OBS and use OBS Virtual Camera as the camera input for Zoom, Skype, Discord, or similar apps.

The project had about 30.8k GitHub stars when checked, and its author archived the repository on Nov 13, 2024. Because the GitHub repository is archived/read-only, users should not expect official commits, releases, or compatibility fixes unless the maintainer reopens development; existing releases remain downloadable and may still work on compatible Windows hardware. Active alternatives — notably Deep-Live-Cam and FaceFusion — now cover similar local or real-time face-swap workflows with ongoing updates. Do not describe them as official DeepFaceLive forks unless a direct repository-fork relationship is verified.

For users willing to configure GPU drivers and work within Windows-only constraints, DeepFaceLive remains one of the most capable free real-time AI face swap video tools available — with the caveat that it is no longer actively maintained.

Key Features

• Face Swap (DFM — Deep Face Model) — Swap faces using trained DFM models. The official README lists about 30 ready-to-use public face models and notes that most listed personas are fictional, with Keanu Reeves as an exception; users can also train custom DFM models using DeepFaceLab if they need a specific face not in the library.
• Face Swap (InsightFace) — Swap faces using a single uploaded photo rather than a trained model. Faster to set up than DFM; best suited for casual use cases where a pre-trained model is unavailable.
• Face Animator — Drive a static face image (photo or illustration) using webcam or video input; the official README describes ~25 fps in a hardware-dependent example using a 35 TFLOPS GPU. Useful for virtual avatar presentations and animated character streaming — an alternative to dedicated AI avatar generator tools for live use cases.
• OBS Studio Integration — DeepFaceLive outputs a processed preview window that OBS can capture as a Window Capture source; OBS Virtual Camera can then expose the result to streaming or video-call apps without platform-level changes.
• Multi-GPU Support — Leverages DirectX12 for rendering, with support for multiple GPU configurations for users with higher performance requirements.
• Multiple Face Detectors — Ships with several face detection algorithms selectable per session, allowing users to optimize for accuracy versus speed depending on their hardware.

How to Get Started

DeepFaceLive requires a compatible GPU, Windows 10, and a video driver update before the application runs correctly. The setup process is straightforward but not zero-configuration:

1. Update GPU drivers — Install the latest DirectX12-compatible driver for your graphics card (NVIDIA or AMD). The official README lists any DirectX12-compatible graphics card as the minimum and recommends RTX 2070+ or Radeon RX 5700 XT+.
2. Download the release — Go to the GitHub releases page and download the latest self-extracting archive. No Python installation or dependency management is needed — the release is portable and self-contained.
3. Extract and launch — Run the extracted executable. DeepFaceLive opens a multi-panel interface where you configure the video source, face detector, swap mode (DFM or InsightFace), and output settings.
4. Load a face model — For DFM mode, download a pre-trained .dfm model from the community model library and load it into the application. For InsightFace mode, upload a clear frontal photo of the target face.
5. Configure virtual camera — Set DeepFaceLive's Stream output to a window/merged-frame preview. In OBS, add the DeepFaceLive output window as a Window Capture source, start OBS Virtual Camera, and select OBS Virtual Camera in your video-call or streaming app.

System RAM requirement is 4GB minimum with a 32GB+ Windows paging file recommended for stable extended sessions.

How It Compares

DeepFaceLive's primary disadvantage in 2026 is that the repository is archived — no bug fixes, no new model support, no compatibility updates. Deep-Live-Cam and FaceFusion are active alternatives that cover similar use cases with continued development. For users who need a browser-based workflow without GPU setup, FaceHub handles face swaps in photos and videos without local hardware requirements.

Pricing & Plans

DeepFaceLive is completely free. No subscription, no credits, no paid tier. The GPL-3.0 license allows free use, modification, and distribution for personal and commercial purposes, subject to the license's copyleft terms (derivative works must also be GPL-3.0).

There are no cloud processing costs — all inference runs locally on your GPU. The only cost is the hardware itself.

Community & Ecosystem

At 30.8k GitHub stars, DeepFaceLive accumulated one of the largest communities in the open-source face swap space before archival. That community has largely migrated to active forks:

• Deep-Live-Cam — An active open-source alternative focused on single-image real-time swapping, with documented Windows, Linux, and macOS setup paths and continued updates.
• FaceFusion — A more advanced pipeline with multiple face detection and swap backends, active development, and a web UI option.
• Community model libraries — Third-party repositories and forums distribute pre-trained DFM models for DeepFaceLive, extending its usable face library well beyond the 30+ official models.

The original project's issues and discussions remain accessible on GitHub as a historical reference. For current troubleshooting, check the archived GitHub issues and discussions for historical context. Broader face-swap communities on Reddit and Discord may offer help, though official DeepFaceLive support is limited given the archived status.

Best For

• Streamers and content creators who want real-time face replacement in OBS-based workflows on Windows hardware with an RTX 2070+ or equivalent GPU
• Developers and researchers who need an open-source, locally-runnable face swap baseline for experimentation or building derivative tools
• Users who want zero ongoing cost and are comfortable with manual GPU driver configuration and parameter tuning
• AI enthusiasts exploring the best AI face swap tools who prefer locally-executed, privacy-preserving inference over cloud-based services
• Legacy users already running DeepFaceLive who don't need updates and want to continue using a stable, known setup

FAQ