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--- |
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pipeline_tag: text-to-video |
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license: other |
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license_name: tencent-hunyuan-community |
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license_link: LICENSE |
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--- |
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<p align="center"> |
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<img src="assets/logo.jpg" height=30> |
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</p> |
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# FastHunyuan Model Card |
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## Model Details |
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FastHunyuan is an accelerated [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo) model. It can sample high quality videos with 6 diffusion steps. That brings around 8X speed up compared to the original HunyuanVideo with 50 steps. |
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- **Developed by**: [Hao AI Lab](https://hao-ai-lab.github.io/) |
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- **License**: tencent-hunyuan-community |
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- **Distilled from**: [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo) |
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- **Github Repository**: https://github.com/hao-ai-lab/FastVideo |
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## Usage |
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- Clone [Fastvideo](https://github.com/hao-ai-lab/FastVideo) repository and follow the inference instructions in the README. |
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- Alternatively, you can inference FastHunyuan using the official [Hunyuan Video repository](https://github.com/Tencent/HunyuanVideo) by **setting the shift to 17 and steps to 6, resolution to 720X1280X125, and cfg bigger than 6**. |
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We find that a large CFG scale generally leads to faster videos. |
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## Training details |
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FastHunyuan is consistency distillated on the [MixKit](https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0/tree/main) dataset with the following hyperparamters: |
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- Batch size: 16 |
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- Resulotion: 720x1280 |
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- Num of frames: 125 |
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- Train steps: 320 |
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- GPUs: 32 |
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- LR: 1e-6 |
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- Loss: huber |
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## Evaluation |
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We provide some qualitative comparison between FastHunyuan 6 step inference v.s. the original Hunyuan with 6 step inference: |
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| FastHunyuan 6 step | Hunyuan 6 step | |
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| --- | --- | |
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## Memory requirements |
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Please check our github repo for details. https://github.com/hao-ai-lab/FastVideo |
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For inference, we can inference FastHunyuan on single RTX4090. We now support NF4 and LLM-INT8 quantized inference using BitsAndBytes for FastHunyuan. With NF4 quantization, inference can be performed on a single RTX 4090 GPU, requiring just 20GB of VRAM. |
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For Lora Finetune, minimum hardware requirement |
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- 40 GB GPU memory each for 2 GPUs with lora |
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- 30 GB GPU memory each for 2 GPUs with CPU offload and lora. |
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