FastHunyuan
| Property | Value |
|---|---|
| Developer | Hao AI Lab |
| License | tencent-hunyuan-community |
| Repository | https://github.com/hao-ai-lab/FastVideo |
| Base Model | HunyuanVideo |
What is FastHunyuan?
FastHunyuan is an innovative acceleration of the HunyuanVideo model, specifically designed for efficient video generation. The model's primary achievement is its ability to produce high-quality videos using just 6 diffusion steps, representing an impressive 8X speedup compared to the original HunyuanVideo model that required 50 steps.
Implementation Details
The model was trained using consistency distillation on the MixKit dataset with specific configurations including a batch size of 16, resolution of 720x1280, and 125 frames per video. The training process utilized 32 GPUs with a learning rate of 1e-6 and employed Huber loss for optimization.
- Optimized for 720X1280X125 resolution videos
- Supports NF4 and LLM-INT8 quantization
- Can run on a single RTX 4090 with 20GB VRAM when using NF4 quantization
- Requires larger CFG scale for optimal performance
Core Capabilities
- High-speed video generation with just 6 diffusion steps
- Memory-efficient inference through quantization support
- Maintains high video quality despite reduced step count
- Flexible deployment options with various hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
FastHunyuan's primary distinction is its ability to generate high-quality videos with just 6 diffusion steps while maintaining quality comparable to models requiring many more steps. This efficiency is achieved through advanced consistency distillation techniques.
Q: What are the recommended use cases?
The model is ideal for applications requiring fast video generation where compute resources are limited. It's particularly suitable for environments with single GPU setups, especially when using quantization techniques. The model performs best with specific resolution settings (720x1280) and higher CFG scales.
