AccVideo
| Property | Value |
|---|---|
| Author | aejion |
| Paper | arXiv:2503.19462 |
| Environment | Python 3.10.0, CUDA 11.8 |
| Hardware Requirement | 80GB GPU Memory (Recommended) |
What is AccVideo?
AccVideo represents a breakthrough in video diffusion models, offering significantly faster inference speeds while maintaining high-quality output. The model employs a novel efficient distillation method utilizing synthetic datasets to accelerate video generation, achieving up to 8.5x faster performance compared to HunyuanVideo.
Implementation Details
The model is built on PyTorch and requires specific dependencies including Flash Attention 2.7.3. It supports various video resolutions and frame counts, with documented performance benchmarks for different configurations.
- Supports high-resolution video generation (up to 720px1280px)
- Configurable frame count (tested with 93 and 129 frames)
- Customizable inference parameters including guidance scale and flow shift
- Integrated with Hugging Face hub for easy model weight access
Core Capabilities
- Ultra-fast video generation (380s vs 3234s for comparable models)
- Support for multiple GPU configurations
- Flexible resolution and frame count settings
- Integration with synthetic dataset (SynVid)
Frequently Asked Questions
Q: What makes this model unique?
AccVideo's primary distinction is its remarkable speed improvement over existing video diffusion models, achieved through innovative distillation techniques and synthetic dataset utilization, while maintaining quality output.
Q: What are the recommended use cases?
The model is ideal for applications requiring fast video generation, particularly in scenarios where traditional video diffusion models would be too slow. It's especially suitable for high-resolution video generation tasks that need quick turnaround times.
