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LatentSync

ByteDance lip-sync model that re-syncs mouths in real video to new audio using latent diffusion.

Lip-sync real video to new audio

Latent diffusion lip-sync from ByteDance, with an 8 GB and an 18 GB VRAM release.

Free forever · open source · Apache-2.0
✓ Needs IT✓ Browser-based✓ Real-time collaboration
5.5
ZEKAI
Open lip-sync for footage
Free & open source · Video
Ease of setup4
Usefulness7
Maintenance3.5
Community7.5
★ 6,276
GITHUB
Jun 2025
UPDATED
Apache-2.0
LICENSE
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⚡ Quick answer

LatentSync is an end-to-end lip-sync method from ByteDance based on audio-conditioned latent diffusion. It suits developers who need dubbing or translation of existing footage with accurate mouth movement.

Runs on
Cross-platform
Hosting
Self-host
Languages
Python
License
Apache-2.0

What it is

The README says it models audio-visual correlations directly with Stable Diffusion, with no intermediate motion representation. It ships v1.5 (8 GB VRAM inference) and v1.6 (18 GB), a Gradio app, command-line inference and training configs.

Who it's for

Suits developers and post-production teams with an NVIDIA GPU who already have footage of a speaker. It does not create a presenter from a script, and the weights are under a different licence from the code.

Key features

Latent diffusion lip-sync

End-to-end method without intermediate motion representation.

Two VRAM tiers

Inference needs 8 GB for v1.5 or 18 GB for v1.6.

Gradio app

Run the demo UI or the command-line script.

Training code

Stage 1 and stage 2 training configs are published.

Temporal layer

v1.5 adds a temporal layer for consistency.

Hugging Face weights

Checkpoints download from the ByteDance Hugging Face repo.

How to start

Up and running in 3 steps

1
Run source setup_env.sh to create the Python environment with CUDA dependencies.
2
Download latentsync_unet.pt and tiny.pt from the ByteDance LatentSync-1.6 Hugging Face repo into the checkpoints folder.
3
Launch the Gradio app, or run the inference script with a video and an audio file.
Real talk

Our honest verdict

What's great

  • Apache-2.0 code from a large lab with training configs included.
  • Offers an 8 GB option (v1.5) for mid-range GPUs.
  • Works on real footage, so identity and background are preserved.
  • Gradio and CLI inference paths are both provided.

Where it falls short

  • Needs an NVIDIA GPU and a CUDA environment from setup_env.sh; v1.6 asks for 18 GB VRAM.
  • The model weights use OpenRAIL++ on Hugging Face, which restricts some uses; read it before commercial work.
  • It only changes lips in existing video; it does not generate a talking presenter.
  • Training needs 20 GB or more of VRAM per the README.
Cost

Pricing & license

Free to run: Apache-2.0 code and OpenRAIL++ weights. The README lists no hosted plan, so the cost is your own or rented GPU time.

Paid work

Open remote AI-training roles

767 open remote AI-training roles, updated November 14, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.

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Questions

FAQ

How much VRAM does LatentSync need?

The README says inference needs a minimum of 8 GB with v1.5 and 18 GB with v1.6.

Can I use LatentSync commercially?

The code is Apache-2.0, but the Hugging Face weights are listed under OpenRAIL++, which allows use with restrictions. Read that licence first.

Does it generate a new video from a photo?

No. It re-syncs the mouth in footage you provide.

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