LatentSync vs X-Dub
LatentSync
github.comX-Dub
github.comSide by side
| Figure | LatentSync | X-Dub |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · AI Video Lip Sync Tools | 8.6 (#4) | 6.6 (#27) |
| Hardware requirement | The stated minimum inference VRAM is 8 GB for LatentSync 1.5 and 18 GB for LatentSync 1.6. | The repository says inference typically requires about 21 GB of VRAM. |
| Intended users | The README describes an efficient stage-two training configuration as suitable for consumer-grade GPUs such as the RTX 3090. | The project provides code, model weights, and command-line inference instructions for users setting up a local Python environment. |
| License | The source repository is licensed under Apache License 2.0. | The repository lists the Apache-2.0 license, which grants no-charge, royalty-free copyright permissions subject to its terms. |
| Purpose | LatentSync is an end-to-end lip-sync method based on audio-conditioned latent diffusion models. | X-Dub synchronizes a character’s lip movements in a video to match input audio. |
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++.