Diff2Lip vs DreamTalk
Diff2Lip
github.comDreamTalk
dreamtalk-project.github.ioSide by side
| Figure | Diff2Lip | DreamTalk |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 0 |
| Spec score · AI Video Lip Sync Tools | 7.9 (#10) | 7.6 (#13) |
| Inference inputs | The repository includes inference scripts for audio-video pairs and supports running on a single video with specified video, audio, and output paths. | Inference accepts WAV, MP3, M4A, or MP4 audio, a portrait image, and reference 3DMM sequences for speaking style and head pose. |
| 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 includes an MIT license, while its README says the method is intended for research and non-commercial use only. |
| Purpose | Diff2Lip generates lip-synchronized videos from speech audio and face videos. | DreamTalk is a diffusion-based framework for generating expressive talking-head videos from audio. |
| Setup | The README setup instructions use Python 3.9, FFmpeg 5.0.1, and the repository's requirements file. | The repository provides inference code and installation instructions for a Python 3.7 environment with PyTorch 1.8.0 and CUDA 11.1 packages. |
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.
DreamTalk
DreamTalk has no write-up on Specifiction yet; its figures are above.