Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models

This version is immigrated from a internal implementation of Alibaba Group, feel free to open an issue to address any problem!

Environment

conda create -n arldm python=3.8
conda activate arldm
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts
git clone https://github.com/Flash-321/ARLDM.git
cd ARLDM
pip install -r requirements.txt

Data Preparation

• Download the PororoSV dataset here.
• Download the FlintstonesSV dataset here.
• Download the VIST-SIS url links here
• Download the VIST-DII url links here
• Download the VIST images running

python data_script/vist_img_download.py
--json_dir /path/to/dii_json_files
--img_dir /path/to/save_images
--num_process 32

• To accelerate I/O, using the following scrips to convert your downloaded data to HDF5

python data_script/pororo_hdf5.py
--data_dir /path/to/pororo_data
--save_path /path/to/save_hdf5_file

python data_script/flintstones_hdf5.py
--data_dir /path/to/flintstones_data
--save_path /path/to/save_hdf5_file

python data_script/vist_hdf5.py
--sis_json_dir /path/to/sis_json_files
--dii_json_dir /path/to/dii_json_files
--img_dir /path/to/vist_images
--save_path /path/to/save_hdf5_file

Training

Specify your directory and device configuration in config.yaml and run

python main.py

Sample

Specify your directory and device configuration in config.yaml and run

python main.py

Acknowledgment

Thanks a lot to @adymaharana for kindly sharing FlintstonesSV and PororoSV datasets (and the code), as well as the PororoSV pretrained checkpoint and Flintstones sampled results of StoryDALL·E.

Citation

If you find this code useful for your research, please cite our paper:

@article{pan2022synthesizing,
  title={Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models},
  author={Pan, Xichen and Qin, Pengda and Li, Yuhong and Xue, Hui and Chen, Wenhu},
  journal={arXiv preprint arXiv:2211.10950},
  year={2022}
}