Roop AI face swap: status, risks & alternatives

Roop is no longer an actively developed face-swap project. The official GitHub repository was archived by its owner on May 17, 2026 and is read-only. It can still explain the one-image, no-training workflow that made Roop popular, but it is a poor starting point for a new production setup because updates, support, security fixes, and model licensing are unresolved.

If you want a supported browser workflow, use a current face-swap service. If you need local control, evaluate an active project and verify the license of every model it downloads. Do not assume that a website or app using “Roop” in its name is affiliated with the original maintainer.

This status guide was checked September 12, 2026. It is based on the official archived repository, current GitHub state, current InsightFace licensing guidance, and the official documentation for the alternatives below. We did not run a controlled output benchmark.

Roop AI at a glance

• Original purpose: one-image face replacement without training a custom model
• Official status: archived and read-only
• Current official surface: the `s0md3v/roop` GitHub repository
• Code license shown by GitHub: AGPL-3.0
• Model-license caveat: the InsightFace pretrained models commonly used in this workflow have separate restrictions
• Best next step: choose a supported browser tool or a current local project based on privacy, licensing, and maintenance needs

What is Roop AI?

Roop is an open-source face-replacement project that became popular because it reduced the workflow to one source image and a target image or video. Older deepfake pipelines often required collecting many images and training a subject-specific model. Roop used a pretrained face-swapping model, so users could begin without that training stage.

The repository visible today describes a Stable Diffusion WebUI extension for replacing faces in generated images. Its README covers importing one face, enabling the extension, selecting specific faces in group images, and optionally using restoration or upscaling. That historical workflow is useful context, but it should not be mistaken for a maintained consumer app or an official hosted Roop service.

Is Roop AI still available?

The source remains viewable, but the official project is archived. GitHub marks the repository read-only and says the owner archived it on May 17, 2026. The maintainer's notice says development stopped after the creator reconsidered the wider effects of software in this category.

Archival has practical consequences:

• The official maintainer is not shipping normal feature or security updates.
• The repository does not accept new pull requests.
• Old setup instructions can conflict with newer Python, Stable Diffusion WebUI, ONNX Runtime, GPU, or operating-system versions.
• Download links in tutorials may point to forks, mirrors, model files, or packaged apps controlled by other parties.
• A site using the Roop name is not automatically the original project.

The popular `roop-unleashed` GitHub repository is also not a dependable default: GitHub currently says that repository has been disabled by GitHub staff for a terms-of-service violation. That does not prove anything about every fork, but it is a clear reason not to treat the first downloadable “Roop” package you find as trusted.

Is Roop free and open source?

The official repository exposes source code and GitHub identifies its code license as AGPL-3.0. That does not mean every component in a working face-swap stack is unrestricted for commercial use.

Roop setup guides commonly reference InsightFace and the `inswapper_128.onnx` model. InsightFace's current license notice separates code from models: its code is MIT licensed, while its public pretrained models are generally limited to non-commercial research unless separately licensed. InsightFace specifically directs users of the INSwapper face-swap models to contact the company about licensing.

Before using any Roop-derived workflow commercially, identify:

1. The exact application or fork and its code license.
2. Every face-detection, recognition, swapping, restoration, and upscaling model it downloads.
3. The license attached to each model file, not only the repository license.
4. Whether the intended use includes advertising, client work, monetized content, an internal business workflow, or a hosted service.

An open repository and a downloadable model can have different permissions. If the model license is unclear, do not infer commercial rights from the code license.

Roop alternatives compared

The best replacement depends on whether you want a browser workflow or a local technical stack. This table compares current access and maintenance facts; it does not rank output quality.

Choose Magic Hour for a supported browser workflow

Magic Hour Face Swap removes the local installation step. You provide the source face and target video in the browser, then generate and review the result. This is the practical path when your priority is completing a video rather than maintaining Python packages, GPU runtimes, model files, and extensions.

A hosted workflow still requires judgment. Use media you have permission to process, get consent from recognizable people, inspect the result for artifacts, and review the current product terms for commercial work. For a broader category comparison, see our best AI face-swap tools.

Choose FaceFusion for a current local platform

FaceFusion is a current local face-manipulation project with its own installation documentation. The documented setup uses Conda, Python, and an ONNX Runtime option matched to the user's hardware. That is more work than a hosted tool, but it offers local control and a current documentation path.

Licensing still needs a component-level review. FaceFusion's license reference lists separate terms for the application and the models or assets it can use, including several items marked non-commercial. Select a processor only after confirming that its model license fits the project. Our FaceFusion setup guide covers the workflow and common configuration issues.

Choose ReActor for a Stable Diffusion WebUI extension

ReActor SFW is designed as a face-swap extension for A1111, SD.Next, and Cagliostro. Its README documents source and target face selection, multiple-face handling, restoration, upscaling, face models, and an SFW detector.

ReActor makes sense if Stable Diffusion WebUI is already part of your local process. It is a weaker choice if you only want to swap one face in a video and export, because the host application and extension add setup and maintenance work. Its repository license also does not replace the license of INSwapper, CodeFormer, GFPGAN, or any other model selected in the pipeline.

Roop vs FaceFusion

FaceFusion is the stronger starting point for a new local setup because it has a current repository and dedicated documentation. Roop is better treated as an archived reference or a legacy dependency that an experienced operator has chosen to maintain.

That recommendation is about project status, not proven output superiority. We have not run Roop and FaceFusion on a controlled set of source images and target videos. If output quality is decisive, test the exact processors and models you are licensed to use with consented media.

Roop vs a browser face-swap tool

A local Roop-derived stack gives you more direct control over where files and models run, but it also makes you responsible for installation, updates, dependencies, security, storage, and licensing. A hosted tool moves that operational work to the provider and gives you a guided workflow, but you need to review upload retention, privacy, plan limits, and terms.

Choose local when you need controlled infrastructure and have someone who can maintain it. Choose a browser tool when speed to a finished asset and support matter more than configuring the stack yourself.

How to evaluate a Roop download or fork

Search results contain many packages and websites that reuse the Roop name. Before downloading anything, use this checklist:

• Provenance: does the page link to an identifiable repository and maintainer?
• Status: is the repository active, archived, or disabled?
• Release integrity: are builds published from the same repository, with hashes or signed releases where available?
• Dependencies: does the installer explain what models and executable files it downloads?
• Model licenses: are commercial and non-commercial restrictions stated per model?
• Security: are open issues, dependency updates, and install instructions current?
• Privacy: does a hosted version explain storage, retention, deletion, and subprocessors?
• Consent controls: does the workflow require users to confirm rights and consent?

Do not enter credentials, payment details, or private media into an unrelated “Roop AI” site solely because it ranks for the project name. Verify the operator and its terms independently.

How to test any face-swap alternative

Use the same small evaluation set for every option:

1. One front-facing source portrait with even lighting.
2. One target close-up with similar angle and lighting.
3. One target with head movement, partial profile, hair, glasses, or a hand crossing the face.
4. One short video with multiple faces if that is part of your real workflow.
5. The same export resolution and restoration setting where possible.

Score identity resemblance, edge blending, skin tone, temporal stability, occlusions, eye and mouth detail, processing time, setup time, and total cost. Count only results you could actually publish. A tool that renders quickly but needs extensive cleanup may be slower for the full job.

Responsible-use checklist

Face replacement can mislead people and can involve biometric, publicity, copyright, employment, or election rules depending on the use and location. For a practical production workflow:

• Get explicit permission from the people whose faces appear.
• Use source and target media you have rights to process.
• Label synthetic or materially altered media when context could mislead viewers.
• Avoid impersonation, fraud, harassment, sexual content without consent, or deceptive endorsements.
• Protect source images and delete them when they are no longer needed.
• Keep records of consent and asset licenses for commercial work.
• Review the platform, code, and model terms separately.

Frequently asked questions

Is the original Roop AI discontinued?

Yes. The official `s0md3v/roop` repository is archived and read-only, and its maintainer says development stopped. The source can still be viewed, but that is different from an actively supported release.

What is the official Roop website?

The authoritative source we could verify is the official GitHub repository. We found commercial sites and apps using the Roop name, but the archived repository does not establish them as official successors.

Is Roop safe to download?

The archived source is publicly inspectable, but safety depends on the exact fork, build, installer, dependencies, and model files you use. Avoid unofficial binaries whose provenance or downloads you cannot verify.

Can Roop run locally?

Yes. Roop was built as a local Python and Stable Diffusion WebUI workflow. That local control comes with dependency and maintenance work, and the official repository is no longer maintained.

Can Roop be used commercially?

Do not assume so from the repository license alone. The code is marked AGPL-3.0, while InsightFace states that its public pretrained models are generally non-commercial unless separately licensed. Confirm the exact model and obtain the required authorization before commercial use.

What is the best Roop alternative?

For a supported browser workflow, use Magic Hour. For a current local platform, start with FaceFusion. If you already use Stable Diffusion WebUI and want an extension, evaluate ReActor SFW. The right answer depends on whether you prioritize convenience, local control, or integration with an existing generation stack.

Final verdict

Roop mattered because it made one-image face replacement accessible without subject-specific training. In 2026, the official project is best treated as an archived reference, not the default recommendation for a new workflow.

The practical replacement decision is straightforward: use a supported browser product when you want to finish an asset quickly, or choose a current local project when infrastructure control justifies the setup and maintenance. In both cases, verify consent, privacy, and every model license before using real people's faces or publishing commercial work.