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[ECCV 2024] StoryImager: A Unified and Efficient Framework for Coherent Story Visualization and Completion

<p align="center"> <img src="pipeline.png" > </p>

A high-quality, unified, and efficient framework for story visualization and completion

Official Pytorch implementation for our paper StoryImager: A Unified and Efficient Framework for Coherent Story Visualization and Completion by Ming Tao, Bing-Kun Bao, Hao Tang, Yaowei Wang, Changsheng Xu.

Requirements

python 3.9
Pytorch 1.13

Preparation

Datasets

Download the preprocessed data for PororoSV FlintstonesSV and extract them to data/

Training

Evaluation

Download Pretrained Model

Sampling

Synthesize images from your story descriptions

the sample.ipynb can be used to sample

Citing StoryImager

If you find StoryImager useful in your research, please consider citing our paper:


@article{tao2024storyimager,
  title={StoryImager: A Unified and Efficient Framework for Coherent Story Visualization and Completion},
  author={Tao, Ming and Bao, Bing-Kun and Tang, Hao and Wang, Yaowei and Xu, Changsheng},
  journal={arXiv preprint arXiv:2404.05979},
  year={2024}
}