LatentSync vs Lip Forcing
LatentSync
github.comLip Forcing
github.comSide by side
| Figure | LatentSync | Lip Forcing |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · AI Video Lip Sync Tools | 8.6 (#4) | 7.5 (#16) |
| Dependencies | Pinned dependencies include PyTorch 2.5.1, Diffusers 0.32.2, Transformers 4.48.0, Gradio 5.24.0 and GPU ONNX Runtime 1.21.0. | Inference requires external components including the Wan VAE, wav2vec audio encoder, text encoder or precomputed embeddings, decoder, and mouth mask. |
| License | The source repository is licensed under Apache License 2.0. | The repository states that Lip Forcing is released under the Apache License 2.0. |
| Purpose | LatentSync is an end-to-end lip-sync method based on audio-conditioned latent diffusion models. | Lip Forcing is an autoregressive diffusion method for video-to-video lip synchronization that animates a reference video from 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++.
Lip Forcing
Lip Forcing has no write-up on Specifiction yet; its figures are above.