LatentSync vs SadTalker

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++.

SadTalker creates a talking-head video from a portrait image and speech audio. It derives 3D head-pose and facial-expression coefficients from the audio to animate the face. The project describes still, reference, and resize modes, along with full-image animation. Command-line examples include GFPGAN enhancement. You can use a local Gradio demo or command-line inference, with setup paths for Linux, Windows, and macOS; Hugging Face and Colab demos are also linked. The documented Linux setup includes Python 3.8, PyTorch, FFmpeg, and the repository requirements. Windows setup also calls for Python, Git, FFmpeg, and model files. Pre-trained models are linked from Google Drive, GitHub Releases, and Baidu. The project says its open-source code runs offline and collects no personal information or other data. SadTalker is free at free under Apache 2.0, except for listed third-party components. Output is 720p or lower and has no watermark. Integrations include Discord and a Stable Diffusion WebUI extension.