LiFT Critic 40b LoRA v1.5

🔧 Installation

1. Clone the github repository and navigate to LiFT folder

git clone https://github.com/CodeGoat24/LiFT.git
cd LiFT

2. Install packages

bash ./environment_setup.sh lift

🚀 Inference

Run

Please download this public LiFT-Critic-40b-lora-v1.5 checkpoints.

We provide some synthesized videos for quick inference in ./demo directory.

python LiFT-Critic/test/run_critic_40b.py --model-path ./LiFT-Critic-40b-lora-v1.5

🖊️ Citation

If you find our work helpful, please cite our paper.

@article{LiFT,
  title={LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment.},
  author={Wang, Yibin and Tan, Zhiyu, and Wang, Junyan and Yang, Xiaomeng and Jin, Cheng and Li, Hao},
  journal={arXiv preprint arXiv:2412.04814},
  year={2024}
}

--- license: mit language: - en base_model: - Efficient-Large-Model/VILA1.5-40b pipeline_tag: video-text-to-text --- # LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment ## Summary This is the model checkpoint proposed in our paper "LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment". LiFT-Critic is a novel Video-Text-to-Text Reward Model for synthesized video evaluation. Project: https://codegoat24.github.io/LiFT/ Code: https://github.com/CodeGoat24/LiFT ## 🔧 Installation 1. Clone the github repository and navigate to LiFT folder ```bash git clone https://github.com/CodeGoat24/LiFT.git cd LiFT ``` 2. Install packages ``` bash ./environment_setup.sh lift ``` ## 🚀 Inference ### Run Please download this public [LiFT-Critic-40b-lora-v1.5](https://huggingface.co/Fudan-FUXI/LiFT-Critic-13b-lora-v1.5) checkpoints. We provide some synthesized videos for quick inference in `./demo` directory. ```bash python LiFT-Critic/test/run_critic_40b.py --model-path ./LiFT-Critic-40b-lora-v1.5 ``` # 🖊️ Citation If you find our work helpful, please cite our paper. ```bibtex @article{LiFT, title={LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment.}, author={Wang, Yibin and Tan, Zhiyu, and Wang, Junyan and Yang, Xiaomeng and Jin, Cheng and Li, Hao}, journal={arXiv preprint arXiv:2412.04814}, year={2024} } ```