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FaceSwap

Name: FaceSwap
Author: IndiAI Tools Editorial Team

Local, open-source AI avatars & video face swapping

Free | Freemium | Paid | Enterprise ⭐⭐⭐⭐☆ 4.2/5 🎭 AI Avatars & Video 🕒 Updated April 21, 2026

IA Reviewed by the IndiAI Tools editorial team How we review →

Visit FaceSwap ↗ Official website

Quick Verdict

FaceSwap is an open-source desktop app for training and applying face-swapping models locally. It is best for technical users—researchers, VFX artists, and hobbyists—with a dedicated GPU who need fully controllable, non-cloud face-swap workflows. FaceSwap is free to use (open-source), with community support and optional paid third-party consulting for enterprises needing integration.

FaceSwap is an open-source face-swapping toolkit that trains and applies autoencoder-based models to replace faces in images and video. The project emphasizes local GPU training and per-frame control, serving users who need reproducible deepfake pipelines rather than turnkey cloud services. Key capabilities include multi-detector face extraction (MTCNN/Dlib/RetinaFace), XSeg mask-based segmentation, and a Convert module for blending and color correction. FaceSwap sits in the AI Avatars & Video category for creators who need offline, auditable workflows. Pricing is accessible: core software is free, with only optional paid third-party support.

About FaceSwap

FaceSwap is an open-source desktop toolkit for face swapping and related deepfake workflows, maintained by a community on faceswap.dev and GitHub. Launched as a community-driven alternative to closed tools, it positions itself as a reproducible research and creator platform rather than a consumer mobile app. The project’s core value proposition is full local control: users download datasets, train autoencoder models on their GPU, and run conversions locally, avoiding cloud uploads.

That approach appeals to people who require transparency, custom model training, and the ability to tune every stage of the pipeline.

What makes FaceSwap different

Three capabilities that set FaceSwap apart from its nearest competitors.

✨ Open-source GPL-style project focused on local training and full pipeline transparency.
✨ Includes XSeg mask generation for per-pixel segmentation unlike many simple swap apps.
✨ Provides both GUI and scriptable CLI workflows to suit research and batch production.

Is FaceSwap right for you?

✅ Best for

VFX artists who need local face-replacement for short production clips
Machine learning researchers who need trainable, auditable face-swap pipelines
Independent filmmakers who must avoid cloud uploads and retain footage control
Advanced hobbyists who want to run custom models on personal GPUs

❌ Skip it if

Skip if you need a hosted, one-click cloud SaaS for instant swaps.
Skip if you lack a CUDA-compatible GPU or technical ability to manage training.

✅ Pros

Fully open-source — source code and models available on GitHub
XSeg masks reduce visible seam artifacts compared with naive swapping
Local workflows avoid cloud uploads and provide reproducible training

❌ Cons

Steep technical learning curve; requires GPU, drivers, and dependency management
No official paid support or hosted option — enterprises need third-party services

FaceSwap Pricing Plans

Current tiers and what you get at each price point. Verified against the vendor's pricing page.

Plan	Price	What you get	Best for
Free	Free	Full desktop toolkit; local GPU required; community support only	Researchers and hobbyists with a local GPU
Community Support / Donations	Optional donations	Donations fund development; no paid SLA or commercial features	Casual users who want to support the project
Third‑party Consulting	Custom	Custom model builds, GPU training, integration (priced per project)	Enterprises needing bespoke integration and support

Best Use Cases

VFX artist using it to swap faces in a 60-second shot saving reshoot costs
Researcher using it to benchmark segmentation accuracy on a 5,000-image dataset
Indie filmmaker using it to produce a stunt double composite within days

Integrations

GitHub OpenCV youtube-dl (YouTube inputs)

How to Use FaceSwap

1
Download the latest release

Visit faceswap.dev or the GitHub Releases page and download the Windows/Linux installer or source. Verify the release notes and checksum; success looks like a local faceswap folder with GUI and scripts.
2
Prepare and extract faces

Open the GUI or run 'python faceswap.py extract' to add source and target videos. Choose detector (MTCNN/Dlib/RetinaFace) and run extraction; success is folders of aligned face images.
3
Train a model on your dataset

Use the Train tab or 'python faceswap.py train' selecting your model type and image size. Monitor loss and checkpoints; success is saved checkpoints you can convert from.
4
Convert and export swapped video

Run the Convert module or 'python faceswap.py convert' selecting checkpoints and masks (XSeg). Adjust color-correction and blending then export to MP4; success is a per-frame swapped video.

FaceSwap vs Alternatives

Bottom line

Choose FaceSwap over DeepFaceLab if you need open-source, community-driven local workflows and XSeg segmentation for production control.

Frequently Asked Questions

How much does FaceSwap cost?+

FaceSwap is free to use (open-source project). The core software and modules are available at no charge from faceswap.dev and GitHub. Costs arise from your own hardware (GPU) or if you buy third-party services like prebuilt models, cloud GPU time, or commercial consulting for integration and production pipelines.

Is there a free version of FaceSwap?+

FaceSwap is entirely free and open-source. You can download releases and source code without subscription, and core features (Extract, Train, Convert, XSeg) are included. Community support is available via Discord and GitHub; the only expenses are optional: GPU hardware, cloud training time, or paid consulting if you require turnkey production help.

How does FaceSwap compare to DeepFaceLab?+

FaceSwap is open-source with local-first tooling and XSeg segmentation. DeepFaceLab is another research-focused toolset with different model variants and a larger Windows user base; choose FaceSwap for multi-detector support and CLI+GUI parity, or DeepFaceLab if you prefer its specific model presets and larger prebuilt model community.

What is FaceSwap best used for?+

FaceSwap is best for production-grade local face replacements and research experiments. Use it to train custom autoencoders for specific subjects, produce masked-blend conversions for short VFX shots, or benchmark segmentation and swap quality on controlled datasets where you need full control over training and conversion parameters.

How do I get started with FaceSwap?+

Download the release from faceswap.dev or GitHub Releases and follow the Quick Start. Extract faces with the Extract module, train with Train (monitor checkpoints), then Convert using XSeg masks. Expect initial setup and GPU driver work; success looks like aligned face folders, saved checkpoints, and an exported swapped video.