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---
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title: Cinemo
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app_file: demo.py
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sdk: gradio
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sdk_version: 4.37.2
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---
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## Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models<br><sub>Official PyTorch Implementation</sub>
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[](https://arxiv.org/abs/2407.15642)
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[](https://maxin-cn.github.io/cinemo_project/)
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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](https://maxin-cn.github.io/cinemo_project/).
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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.
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<div align="center">
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<img src="visuals/pipeline.svg">
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</div>
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## News
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- (🔥 New) Jul. 23, 2024. 💥 Our paper is released on [arxiv](https://arxiv.org/abs/2407.15642).
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- (🔥 New) Jun. 2, 2024. 💥 The inference code is released. The checkpoint can be found [here](https://huggingface.co/maxin-cn/Cinemo/tree/main).
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## Setup
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First, download and set up the repo:
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```bash
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git clone https://github.com/maxin-cn/Cinemo
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cd Cinemo
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```
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We provide an [`environment.yml`](environment.yml) file that can be used to create a Conda environment. If you only want
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to run pre-trained models locally on CPU, you can remove the `cudatoolkit` and `pytorch-cuda` requirements from the file.
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```bash
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conda env create -f environment.yml
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conda activate cinemo
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```
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## Animation
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You can sample from our **pre-trained Cinemo models** with [`animation.py`](pipelines/animation.py). Weights for our pre-trained Cinemo model can be found [here](https://huggingface.co/maxin-cn/Cinemo/tree/main). The script has various arguments for adjusting sampling steps, changing the classifier-free guidance scale, etc:
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```bash
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bash pipelines/animation.sh
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```
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All related checkpoints will download automatically and then you will get the following results,
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<table style="width:100%; text-align:center;">
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<tr>
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<td align="center">Input image</td>
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<td align="center">Output video</td>
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<td align="center">Input image</td>
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<td align="center">Output video</td>
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</tr>
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<tr>
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<td align="center"><img src="visuals/animations/people_walking/0.jpg" width="100%"></td>
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<td align="center"><img src="visuals/animations/people_walking/people_walking.gif" width="100%"></td>
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<td align="center"><img src="visuals/animations/sea_swell/0.jpg" width="100%"></td>
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<td align="center"><img src="visuals/animations/sea_swell/sea_swell.gif" width="100%"></td>
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</tr>
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<tr>
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<td align="center" colspan="2">"People Walking"</td>
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<td align="center" colspan="2">"Sea Swell"</td>
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</tr>
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<tr>
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<td align="center"><img src="visuals/animations/girl_dancing_under_the_stars/0.jpg" width="100%"></td>
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<td align="center"><img src="visuals/animations/girl_dancing_under_the_stars/girl_dancing_under_the_stars.gif" width="100%"></td>
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<td align="center"><img src="visuals/animations/dragon_glowing_eyes/0.jpg" width="100%"></td>
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<td align="center"><img src="visuals/animations/dragon_glowing_eyes/dragon_glowing_eyes.gif" width="100%"></td>
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</tr>
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<tr>
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<td align="center" colspan="2">"Girl Dancing under the Stars"</td>
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<td align="center" colspan="2">"Dragon Glowing Eyes"</td>
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</tr>
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</table>
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## Other Applications
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You can also utilize Cinemo for other applications, such as motion transfer and video editing:
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```bash
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bash pipelines/video_editing.sh
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```
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All related checkpoints will download automatically and you will get the following results,
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<table style="width:100%; text-align:center;">
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<tr>
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<td align="center">Input video</td>
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<td align="center">First frame</td>
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<td align="center">Edited first frame</td>
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<td align="center">Output video</td>
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</tr>
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<tr>
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<td align="center"><img src="visuals/video_editing/origin/a_corgi_walking_in_the_park_at_sunrise_oil_painting_style.gif" width="100%"></td>
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<td align="center"><img src="visuals/video_editing/origin/0.jpg" width="100%"></td>
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<td align="center"><img src="visuals/video_editing/edit/0.jpg" width="100%"></td>
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<td align="center"><img src="visuals/video_editing/edit/editing_a_corgi_walking_in_the_park_at_sunrise_oil_painting_style.gif" width="100%"></td>
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</tr>
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</table>
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## Citation
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If you find this work useful for your research, please consider citing it.
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```bibtex
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@article{ma2024cinemo,
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title={Cinemo: Latent Diffusion Transformer for Video Generation},
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author={Ma, Xin and Wang, Yaohui and Jia, Gengyun and Chen, Xinyuan and Li, Yuan-Fang and Chen, Cunjian and Qiao, Yu},
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journal={arXiv preprint arXiv:2407.15642},
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year={2024}
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}
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```
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## Acknowledgments
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Cinemo has been greatly inspired by the following amazing works and teams: [LaVie](https://github.com/Vchitect/LaVie) and [SEINE](https://github.com/Vchitect/SEINE), we thank all the contributors for open-sourcing.
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## License
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The code and model weights are licensed under [LICENSE](LICENSE).
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