Deep-Live-Cam

Quick Verdict

Deep-Live-Cam is an actively maintained open-source project for swapping a source face into a video file or live camera feed from one image. Its authoritative surface is the hacksider/Deep-Live-Cam GitHub repository, not an unaffiliated review or download page. On July 20, 2026, the main documentation identifies version 2.1.6, while the repository also exposes a May 2026 release candidate in the 2.7 line. The project includes mouth masking, multiple-face mapping, file processing, webcam preview, and several ONNX Runtime execution backends.

The repository is AGPL-3.0, but that license does not grant commercial rights to every model or media asset. The project credits InsightFace and explicitly reminds users that the relevant model is for non-commercial research. A commercial team must separately verify code, model, source-face, target-performance, music, and output rights. A paid prebuilt package may simplify installation; it cannot grant permission to use another person’s identity.

This is a high-risk synthesis tool, not a harmless filter for experimenting with strangers’ photos. Use it only when the source-face owner has given explicit, purpose-specific permission, all target media and performances are licensed, and realistic output carries a prominent synthetic-media disclosure. Never use it to impersonate a public figure, relative, executive, bank, government body, or colleague; defeat identity checks; obtain money or credentials; harass or defame; or create sexualized non-consensual media. Built-in content checks are not a substitute for consent, access controls, review, watermarking, complaint response, and takedown.

Best For

• Technical artists creating performances or experiments with their own face, licensed actors, or authorized fictional characters.
• Researchers studying real-time synthesis, deepfake detection, or media literacy under an ethics and data-governance process.
• Developers comfortable with Python 3.11, virtual environments, FFmpeg, model files, ONNX Runtime, and hardware drivers.
• Production teams prototyping their own intellectual property in a controlled environment with no deceptive representation.
• Not appropriate for minors acting independently, anonymous public services, dating or meeting disguise, KYC testing, public-figure imitation, or any non-consensual face source.

Key Features

• Single-image swapping: A source face can be applied to a target image, video, or camera without training a dedicated model for every identity.
• File and live modes: File mode renders output, while webcam mode shows a preview. Official instructions note that live preview may take roughly 10 to 30 seconds to appear and can be captured through software such as OBS.
• Mouth mask and face mapping: A mouth mask can preserve original mouth motion, and mapping can assign sources to multiple subjects. Occlusion, side profiles, fast movement, crossings, and changing light remain difficult.
• Execution backends: The baseline CPU path is easier but slower. NVIDIA CUDA, Apple CoreML, Windows DirectML, and Intel OpenVINO require compatible runtime packages, drivers, and platform-specific setup.
• Local processing and encoding: Options cover frame rate, audio, temporary frames, memory, threads, and H.264, H.265, or VP9 encoding. Local execution can reduce cloud exposure but still requires secure handling of models, frames, logs, and output.
• Content checks: The maintainers describe checks for nudity, graphic content, and sensitive war media. Those checks do not determine whether a face was licensed, a disclosure is sufficient, or a use is fraudulent.

Use Cases

A defensible creative project begins with an asset register. Record the person represented by the source face, the target performer, footage, audio, model license, output channels, approval owner, and retention period. The release must specifically cover synthetic face replacement; permission to publish a photograph is not automatically permission to animate or transplant that identity. Start with short, low-resolution, local tests. After approval, inspect final frames for face boundaries, teeth, glasses, hair, hands crossing the face, multiple people, lighting changes, and sudden head turns.

Disclosure must be visible to the audience, not buried in a distant hashtag. Label recorded media as AI-generated or face-swapped in the frame and accompanying description. A live performance should provide a persistent indicator and an opening verbal notice where appropriate. Preserve consent and production records, but delete temporary frames and source material according to the agreed schedule. Limit upload, model, and export access to named team members.

Prohibit workflows that simulate a bank, family member, executive, celebrity, official, or other trusted person in a call; request payment, passwords, one-time codes, or sensitive data; target a minor; fabricate an endorsement or news event; facilitate stalking, retaliation, defamation, or sexualized manipulation; or bypass an exam, interview, KYC, liveness, or security check. A reporting process should freeze the project, preserve necessary evidence, stop distribution, remove source and derivative assets, notify affected people, and request platform takedown. Escalate credible safety or financial threats to legal, security, and appropriate authorities.

Pricing

Do not reuse stale subscription prices or claims about exclusive version lead without checking the current checkout. More importantly, distinguish the code license from the model license. The repository’s AGPL terms govern the code, while the credited InsightFace model carries a non-commercial research notice. Legal commercial deployment may require a different model, separate permission, or a product whose commercial rights are explicitly documented.

Pros

• Large open-source community and a locally inspectable file and webcam workflow.
• One source image is enough to begin; no identity-specific training cycle is required.
• CPU, CUDA, CoreML, DirectML, and OpenVINO options cover several hardware families.
• Mouth masking, multiple-face mapping, and encoding controls support controlled visual experiments.
• The official documentation explicitly tells users to obtain consent and label deepfake output.
• Local execution can avoid sending identity media to a hosted generation service.

Cons

• The same accessibility creates severe impersonation, fraud, harassment, and non-consensual-media risk.
• Manual setup spans Python, virtual environments, FFmpeg, models, runtimes, drivers, and exact package versions.
• CPU operation is slow, while accelerated performance varies with hardware, resolution, face count, enhancement, and encoding.
• AGPL source availability does not override the non-commercial research restriction associated with a credited face model.
• Built-in sensitive-content checks cannot verify consent or reliably understand public figures, minors, fraud, defamation, or harassment context.
• Side views, occlusion, lighting changes, and rapid multi-person motion can create visible artifacts.

Alternatives

FAQ

Is Deep-Live-Cam free?

The source code is free, but installation, hardware, electricity, storage, maintenance, and optional prebuilt access have costs. The code license also does not establish commercial rights to the underlying face model or to any input identity and media.

Can it run without a GPU?

Yes, the CPU path can verify functionality, but it is slower. Test the actual camera, resolution, face count, enhancement, encoding, latency, temperature, memory, and dropped frames rather than inferring performance from a demonstration.

May I use a face just because its photo is publicly available?

No. Public visibility is not synthetic-media permission. Obtain explicit consent from the person represented and all necessary rights to the target performance, footage, audio, and distribution. The same rule applies to public figures.

Can I use it in a video call or livestream?

Only in an authorized performance where participants and viewers are not misled, the platform permits it, and a prominent live disclosure remains visible. Do not use it to conceal identity in dating, exams, recruitment, KYC, financial activity, or private communication.

Do the built-in checks prevent all abuse?

No. They target some categories of sensitive media. They cannot authenticate a consent release, infer fraudulent intent, or resolve all public-figure, minor, harassment, defamation, and disclosure situations. Human and organizational controls remain essential.

Can I use the face of a minor or celebrity?

Avoid both by default. A minor requires a strict necessity assessment, valid guardian authority, minimal retention, and enhanced review. A public figure must not be synthesized into false endorsements, news, private acts, or financial messages. “Parody” does not excuse foreseeable deception or harm.

What if the represented person requests removal?

Stop generation and distribution immediately, preserve only evidence needed for investigation, freeze related accounts, remove the source, cache, model references, and derivatives, and submit takedown requests to publishing platforms. Confirm remediation to the complainant and escalate threats, fraud, or sexualized content appropriately.

Bottom Line

Deep-Live-Cam is technically capable and unusually high risk. A responsible evaluation starts with model and media licensing, documented face-specific consent, and an isolated test. Only then should a team benchmark CPU or GPU performance and establish disclosure, access control, logs, retention, complaints, and takedown. Any plan involving impersonation, minors, public figures, fraud, harassment, identity-check bypass, or non-consensual media should stop before implementation.