LiFT Critic 13b LoRA

--- license: mit language: - en base_model: - Efficient-Large-Model/VILA1.5-13b 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-13b-lora](https://huggingface.co/Fudan-FUXI/LiFT-Critic-13b-lora) checkpoints. We provide some synthesized videos for quick inference in `./demo` directory. ```bash python LiFT-Critic/test/run_critic_13b.py --model-path ./LiFT-Critic-13b-lora ``` # 🖊️ 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} } ```

Source context: 11 downloads · 1 likes · Pipeline video-text-to-text · Repo Fudan-FUXI/LiFT-Critic-13b-lora