metadata
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
π§ Installation
- Clone the github repository and navigate to LiFT folder
git clone https://github.com/CodeGoat24/LiFT.git
cd LiFT
- Install packages
bash ./environment_setup.sh lift
π 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
ποΈ 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}
}