LatentSync

LatentSync stands out because ByteDance released not just weights but the full method, data pipeline and training code for a high-quality lip-sync model. It models the link between audio and video directly in latent space instead of relying on an intermediate motion representation, which the authors credit for natural-looking results. Having the training code public also makes it a useful reference for anyone studying lip-sync methods.

• Audio-conditioned latent diffusion built on Stable Diffusion
• Whisper-based audio embeddings fed into the U-Net through cross-attention
• Temporal layer added in version 1.5 for better consistency between frames
• Version 1.6 trained at 512 by 512 resolution for sharper output
• Gradio interface, CLI script and tunable settings for inference steps and guidance scale

LatentSync is best for developers, researchers and video creators comfortable running Python models locally who want dubbing or lip-sync without a paid service. A practical tip: begin with a moderate number of inference steps and a low guidance scale, then raise them gradually, since the project notes that higher guidance can improve sync but may introduce jitter or distortion.