Diff2Lip vs SadTalker

Diff2Lip is a free, open-source Linux and self-hosted project that synchronizes speech with face videos using an audio-conditioned diffusion model. It accepts speech and face-video material and generates lip-synced video while aiming to preserve the identity shown in source frames. The repository includes inference scripts for audio-video pairs and for a single video. Cross mode drives a video with an audio source; reconstruction mode drives the video's first frame with audio. The project lists movies, education and virtual avatars as applications, with video conferencing described as a possible future use. Diff2Lip is not real time yet. Reported evaluations use the VoxCeleb2 and LRW datasets, and the repository identifies the work as a WACV 2024 paper. Setup instructions use Python 3.9, FFmpeg 5.0.1 and the requirements file. Inference supports one or more GPUs through NUM_GPUS, with values above one running distributed generation. A Google Colab notebook is linked for trying it.

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.