Diff2Lip vs LatentSync

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.

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++.