Diff2Lip vs Wav2Lip
Diff2Lip
github.comWav2Lip
github.comSide by side
| Figure | Diff2Lip | Wav2Lip |
|---|---|---|
| Price from | — | $50.05/mo |
| Free plan | — | — |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | — |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 5 |
| Spec score · AI Video Lip Sync Tools | 7.9 (#10) | 7.4 (#21) |
| 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. | The inference script can lip-sync a video to audio from supported audio files or a video file, from which it automatically extracts audio. |
| Purpose | Diff2Lip generates lip-synchronized videos from speech audio and face videos. | Wav2Lip provides code for accurately lip-syncing videos to speech, as described in the paper “A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild.” |
Plans
Wav2Lip
- Hobbyist$50.05 a month
- Creator$190.05 a month
- Growth$490.05 a month
- Scale$2,490.04 a month
- EnterprisePriced on request
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.