Diffusion Models

Diffusion models work by learning to reverse a gradual noising process, starting from pure noise and iteratively refining it into a coherent output. In the context of lip sync, diffusion-based approaches can generate higher-fidelity facial details and more natural mouth textures compared to earlier GAN-based methods.

Sync's lipsync-2-pro model leverages diffusion-based techniques to achieve state-of-the-art visual quality, producing lip sync results with fine-grained detail in teeth, tongue, and lip textures that were difficult for previous architectures to render convincingly.

Diffusion Models relates to several other concepts in the AI lip sync pipeline: GAN (Generative Adversarial Network) , and Neural Rendering .