InfiniteTalk vs LatentSync
InfiniteTalk
github.comLatentSync
github.comSide by side
| Figure | InfiniteTalk | LatentSync |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · AI Video Lip Sync Tools | 7.4 (#18) | 8.6 (#4) |
| Inference options | The repository documents single-GPU and multi-GPU inference, plus a quantized mode that supports only a single GPU. | The project provides a Gradio app and a command-line interface for inference. |
| Intended users | The project page presents InfiniteTalk as a research framework for long-sequence audio-driven human animation and video dubbing. | The README describes an efficient stage-two training configuration as suitable for consumer-grade GPUs such as the RTX 3090. |
| License | The model weights are licensed under Apache 2.0, and the repository says users are responsible for lawful use of generated content. | The source repository is licensed under Apache License 2.0. |
| Purpose | InfiniteTalk generates audio-synchronized talking videos from a source video and audio, or animates a single image using audio. | LatentSync is an end-to-end lip-sync method based on audio-conditioned latent diffusion models. |
| What it does | InfiniteTalk generates audio-synchronized talking videos from a source video or a single image. | LatentSync is an end-to-end lip-sync system that generates mouth movements from audio with latent diffusion and no intermediate motion representation. |
Plans
In their own words
InfiniteTalk
InfiniteTalk has no write-up on Specifiction yet; its figures are above.
LatentSync
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++.