Diff2Lip vs EchoMimic
Diff2Lip
github.comEchoMimic
github.comSide by side
| Figure | Diff2Lip | EchoMimic |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 0 |
| Spec score · AI Video Lip Sync Tools | 7.9 (#10) | 7.5 (#15) |
| Integrations | The repository links Google Colab for trying the project; it does not list other product integrations. | The repository links a ComfyUI implementation contributed by a community member. |
| License | The repository states that its text and code are under CC BY-NC 4.0, with attribution required and commercial use prohibited. | The repository's LICENSE file contains the Apache License, Version 2.0. |
| Purpose | Diff2Lip generates lip-synchronized videos from speech audio and face videos. | EchoMimic generates portrait videos from audio, facial landmarks, or a combination of audio and selected facial landmarks. |
| Requirements | The setup instructions use Python 3.9 and FFmpeg 5.0.1, and inference can use one or more GPUs. | The repository lists tested environments as CentOS 7.2 or Ubuntu 22.04 with CUDA 11.7 or later, Python 3.8, 3.10, or 3.11, and tested GPUs A100 80G, RTX4090D 24G, or V100 16G. |
| Research results | The project reports reconstruction and cross-audio-video results on VoxCeleb2 and LRW datasets. | The project page says EchoMimic was compared with alternative algorithms on public and collected datasets and showed superior quantitative and qualitative performance. |
In their own words
Diff2Lip
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.