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storyimager's Introduction

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

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

  1. 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}
}

storyimager's People

Contributors

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Stargazers

Bote Huang avatar Maki Nishikino avatar avatar Jinlu Zhang avatar Junjie avatar avatar Ethan Chern avatar Dongyang Li avatar avatar avatar Zhang, Di avatar glowworm avatar avatar TracelessLe avatar Fei Shen avatar avatar Zhoujingming Gao avatar avatar AndyS avatar avatar Xiefan Guo avatar Martin avatar Hao Tang avatar avatar avatar avatar avatar Qinghe Wang avatar fenneishi avatar xixnin luo avatar avatar avatar avatar

Watchers

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Forkers

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