LatentSync-1.5
| Property | Value |
|---|---|
| Author | ByteDance |
| Paper | Research Paper |
| Code | GitHub Repository |
| VRAM Requirement | 20GB |
What is LatentSync-1.5?
LatentSync-1.5 is an advanced AI model designed for high-quality video lip synchronization. This updated version represents a significant improvement over its predecessor, featuring enhanced temporal consistency and better performance on Chinese videos. The model has been optimized to run on consumer-grade hardware while maintaining professional-quality results.
Implementation Details
The model incorporates several technical improvements, including an optimized temporal layer implementation and efficient memory management through gradient checkpointing. It utilizes PyTorch's native FlashAttention-2 implementation and features streamlined training procedures that enable operation on a single RTX 3090 GPU.
- Implemented gradient checkpointing in U-Net, VAE, SyncNet and VideoMAE
- Replaced xFormers with PyTorch's native FlashAttention-2
- Optimized CUDA cache management
- Upgraded to diffusers version 0.32.2
Core Capabilities
- Enhanced temporal consistency through corrected temporal layer implementation
- Improved performance on Chinese video content
- Reduced VRAM requirements (20GB) through efficient optimization
- Streamlined stage2 training process
- Removed dependency on xFormers and Triton
Frequently Asked Questions
Q: What makes this model unique?
LatentSync-1.5's uniqueness lies in its ability to deliver professional-grade lip synchronization while requiring significantly less computational resources than previous versions. The corrected temporal layer implementation and improved Chinese video support make it particularly versatile.
Q: What are the recommended use cases?
The model is ideal for video content creators, dubbing studios, and multimedia professionals who need to synchronize lip movements with audio in both English and Chinese content. It's particularly suitable for users with access to RTX 3090 or similar GPUs.
