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What is latentsync?

bytedance/latentsync — explained in plain English

Analysis updated 2026-06-26

5,675PythonAudience · researcherComplexity · 4/5Setup · hard

In one sentence

LatentSync is a ByteDance research tool that automatically replaces lip movements in a video to match a new audio track using Stable Diffusion and Whisper, useful for AI dubbing experiments and creative video editing.

Mindmap

mindmap
  root((latentsync))
    What it does
      Lip-sync from audio
      Video face editing
      AI dubbing
    How it works
      Whisper audio model
      Stable Diffusion
      Single-stage pipeline
    Requirements
      GPU 8GB minimum
      CUDA required
      Python setup
    Interfaces
      Gradio browser UI
      Command line
    Use cases
      Research
      Creative video
      Custom training
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What do people build with it?

USE CASE 1

Replace the audio in a talking-head video and have the lips automatically sync to the new words

USE CASE 2

Dub a video into another language with AI-generated lip movements that match the translated audio

USE CASE 3

Experiment with the full training pipeline to fine-tune a lip-sync model on a custom dataset

USE CASE 4

Run batch inference from the command line to process multiple videos with the same replacement audio

What is it built with?

PythonStable DiffusionWhisperGradioCUDAPyTorch

How does it compare?

bytedance/latentsyncagiresearch/aiosgoogle/gemma_pytorch
Stars5,6755,6765,674
LanguagePythonPythonPython
Setup difficultyhardhardhard
Complexity4/55/54/5
Audienceresearcherresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires a GPU with at least 8 GB of VRAM and CUDA drivers, the 512px model needs 18 GB of VRAM.

Open-source research tool from ByteDance, specific license terms are in the repository.

So what is it?

LatentSync is a research project from ByteDance that automatically makes a person's lip movements in a video match a different audio track. You give it a video of someone talking and a new audio clip, and it rewrites the mouth area so the lips appear to be saying the new words. This is sometimes called lip-sync or dubbing automation. The system works by combining two existing AI components. It uses Whisper, an audio recognition model, to convert the sound into a format that carries timing and phonetic information. That information is then fed into a modified version of Stable Diffusion, a popular image generation model, which regenerates the face frame by frame so the mouth matches the audio. The whole process happens in one stage rather than two separate steps, which the authors say reduces certain visual artifacts. To run it, you need a computer with a dedicated graphics card. The lighter version (1.5) requires at least 8 GB of video memory, while the higher-resolution version (1.6, which produces 512x512 pixel output) requires 18 GB. You can run inference either through a simple browser-based interface built with Gradio or from the command line. A setup script downloads the required model checkpoints automatically. The repository also includes the full training pipeline for researchers who want to train their own version. This covers data preparation steps such as video segmentation, face alignment, audio resampling, and quality filtering. Training the model from scratch requires substantially more GPU memory, ranging from 23 GB to 55 GB depending on the configuration. LatentSync is released as an open-source research tool. It is intended for research and creative experimentation rather than production deployment.

Copy-paste prompts

Prompt 1
Using LatentSync, take a video of a person talking and replace the audio with this new recording, generating synchronized lip movements for the new words.
Prompt 2
Set up LatentSync on a machine with an RTX 3080 and run the Gradio demo interface using the 1.5 checkpoint.
Prompt 3
Batch process 10 videos through LatentSync from the command line using the same replacement audio track for each.
Prompt 4
Walk me through how LatentSync uses Whisper audio features to drive Stable Diffusion for frame-by-frame lip generation.
Prompt 5
Prepare a custom dataset for LatentSync retraining using the provided data preparation scripts, starting from raw MP4 files.

Frequently asked questions

What is latentsync?

LatentSync is a ByteDance research tool that automatically replaces lip movements in a video to match a new audio track using Stable Diffusion and Whisper, useful for AI dubbing experiments and creative video editing.

What language is latentsync written in?

Mainly Python. The stack also includes Python, Stable Diffusion, Whisper.

What license does latentsync use?

Open-source research tool from ByteDance, specific license terms are in the repository.

How hard is latentsync to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is latentsync for?

Mainly researcher.

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