All roads lead to Rome!

This reposioty contains our official implementation for Vico. Vico provides a unified solution for compositional video generation by equalizing the information flow of text tokens.

Compositional Video Generation as Flow Equalization

🥯[Project Page] 📝[Paper] </>[code]

Xingyi Yang, Xinchao Wang

National University of Singapore

We introduce Vico, a generic framework for compositional video generation that explicitly ensures all concepts are represented properly. At its core, Vico analyzes how input tokens influence the generated video, and adjusts the model to prevent any single concept from dominating. We apply our method to multiple diffusion-based video models for compositional T2V and video editing. Empirical results demonstrate that our framework significantly enhances the compositional richness and accuracy of the generated videos.

Prompt	Baseline	Vico
A crab DJing at a beach party during sunset.
A falcon as a messenger in a sprawling medieval city.
A confused panda in calculus class.

• Enviroments

  pip install diffusers==0.26.3

• For VideoCrafterv2, it is recommanded to download the diffusers checkpoints first on (adamdad/videocrafterv2_diffusers)[https://huggingface.co/adamdad/videocrafterv2_diffusers]. I do it by convering the official checkpoint to the diffuser format.

  git lfs install
  git clone https://huggingface.co/adamdad/videocrafterv2_diffusers

export PYTHONPATH="$PWD"
python videocrafterv2_vico.py \
    --prompts XXX \
    --unet_path $PATH_TO_VIDEOCRAFTERV2 \
    --attribution_mode "latent_attention_flow_st_soft" 

• [2024.07.09]: Release arxiv paper and code for Vico on Videocrafterv2.

We are mostly inspired by Attend&Excite for text-to-image generation. We thank the valuable disscussion with @Yuanshi9815.

@misc{yang2024compositional,
    title={Compositional Video Generation as Flow Equalization},
    author={Xingyi Yang and Xinchao Wang},
    year={2024},
    eprint={2407.06182},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}