Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
_{Official PyTorch Implementation}

Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
Xin Ma, Yaohui Wang*†, Gengyun Jia, Xinyuan Chen, Yuan-Fang Li, Cunjian Chen*, Yu Qiao
(*Corresponding author, †Project Lead)

This repo contains pre-trained weights, and sampling code for our paper exploring image animation with motion diffusion models (Cinemo). You can find more visualizations on our project page.

In this project, we propose a novel method called Cinemo, which can perform motion-controllable image animation with strong consistency and smoothness. To improve motion smoothness, Cinemo learns the distribution of motion residuals, rather than directly generating subsequent frames. Additionally, a structural similarity index-based method is proposed to control the motion intensity. Furthermore, we propose a noise refinement technique based on discrete cosine transformation to ensure temporal consistency. These three methods help Cinemo generate highly consistent, smooth, and motion-controlled image animation results. Compared to previous methods, Cinemo offers simpler and more precise user control and better generative performance.

News

• (🔥 New) Jul. 29, 2024. 💥 Add the online and local demo.

• (🔥 New) Jul. 23, 2024. 💥 Our paper is released on arxiv.

• (🔥 New) Jun. 2, 2024. 💥 The inference code is released. The checkpoint can be found here.

Setup

First, download and set up the repo:

git clone https://github.com/maxin-cn/Cinemo
cd Cinemo

We provide an environment.yml file that can be used to create a Conda environment. If you only want to run pre-trained models locally on CPU, you can remove the cudatoolkit and pytorch-cuda requirements from the file.

conda env create -f environment.yml
conda activate cinemo

Animation

You can sample from our pre-trained Cinemo models with animation.py. Weights for our pre-trained Cinemo model can be found here. The script has various arguments for adjusting sampling steps, changing the classifier-free guidance scale, etc:

bash pipelines/animation.sh

All related checkpoints will download automatically and then you will get the following results,

Input image	Output video	Input image	Output video
"People Walking"		"Sea Swell"
"Girl Dancing under the Stars"		"Dragon Glowing Eyes"

Gradio interface

We also provide a Gradio interface for a better experience, just run by:

python app.py

You can specify the --share and --server_name arguments to satisfy your needs!

Other Applications

You can also utilize Cinemo for other applications, such as motion transfer and video editing:

bash pipelines/video_editing.sh

All related checkpoints will download automatically and you will get the following results,

Input video	First frame	Edited first frame	Output video

Contact Us

Xin Ma: xin.ma1@monash.edu Yaohui Wang: wangyaohui@pjlab.org.cn

Citation

If you find this work useful for your research, please consider citing it.

@article{ma2024cinemo,
  title={Cinemo: Latent Diffusion Transformer for Video Generation},
  author={Ma, Xin and Wang, Yaohui and Jia, Gengyun and Chen, Xinyuan and Li, Yuan-Fang and Chen, Cunjian and Qiao, Yu},
  journal={arXiv preprint arXiv:2407.15642},
  year={2024}
}

Acknowledgments

Cinemo has been greatly inspired by the following amazing works and teams: LaVie and SEINE, we thank all the contributors for open-sourcing.

License

The code and model weights are licensed under LICENSE.