LatentSync vs VisualDub
LatentSync
github.com
VisualDub
visualdub.aiSide by side
| Figure | LatentSync | VisualDub |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | — | Yes |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 1 |
| Spec score · AI Video Lip Sync Tools | 8.6 (#4) | 6.6 (#26) |
| What it does | LatentSync is an end-to-end lip-sync system that generates mouth movements from audio with latent diffusion and no intermediate motion representation. | VisualDub uses generative AI to sync an actor’s lip and facial movements with dubbed audio for native-feeling visual dubbing. |
Plans
In their own words
LatentSync
LatentSync is a free, open-source lip-sync method for Linux and self-hosted deployment. It uses audio-conditioned latent diffusion models: Whisper converts mel spectrograms into audio embeddings, which enter a U-Net through cross-attention layers. The project offers a Gradio application and command-line inference, plus checkpoints, training code and a data-processing pipeline. LatentSync 1.6 was trained on 512 × 512 videos to address blurry teeth and lips. Minimum inference memory is listed as 8 GB of VRAM for version 1.5 and 18 GB for version 1.6. More inference steps can improve visual quality but slow generation; increasing guidance scale can improve lip-sync accuracy while introducing distortion or jitter. Training configurations for the U-Net require 20–55 GB of VRAM, depending on configuration. The documented pipeline uses PySceneDetect for scene detection and InsightFace landmarks for face transformation. The repository lists Apache-2.0, while the model card lists openrail++.