LayerAnimate: Layer-level Control for Animation

Yuxue Yang^1,2, Lue Fan², Zuzeng Lin³, Feng Wang⁴, Zhaoxiang Zhang^1,2†

¹UCAS  ²CASIA  ³TJU  ⁴CreateAI  ^†Corresponding author

       

Official implementation of LayerAnimate: Layer-level Control for Animation, ICCV 2025

Videos on the project website vividly introduces our work and presents qualitative results for an enhanced view experience.

Updates

• [25-06-26] Our work is accepted by ICCV 2025! 🎉
• [25-05-29] We have extended LayerAnimate to the DiT (Wan2.1 1.3B) variant, enabling the generation of 81 frames at 480 × 832 resolution. It performs surprisingly well in the Real-World Domain shown in the project website.
• [25-03-31] Release the online demo on Hugging Face.
• [25-03-30] Release a gradio script app.py to run the demo locally. Please raise an issue if you encounter any problems.
• [25-03-22] Release the checkpoint and the inference script. We update layer curation pipeline and support trajectory control for a flexible composition of various layer-level controls.
• [25-01-15] Release the project page and the arXiv preprint.

Installation

git clone git@github.com:IamCreateAI/LayerAnimate.git
conda create -n layeranimate python=3.10 -y
conda activate layeranimate
pip install -r requirements.txt
pip install wan@git+https://github.com/Wan-Video/Wan2.1  # If you want to use DiT variant.

Models

Models	Download Link	Video Size
UNet variant	Huggingface 🤗	16 x 320 x 512
DiT variant	Huggingface 🤗	81 x 480 x 832

Download the pretrained weights and put them in checkpoints/ directory as follows:

checkpoints/
├─ LayerAnimate-Mix (UNet variant)
└─ LayerAnimate-DiT

Inference script

UNet variant (Paper version)

Run the following command to generate a video from input images:

python scripts/animate_Layer.py --config scripts/demo1.yaml --savedir outputs/sample1

python scripts/animate_Layer.py --config scripts/demo2.yaml --savedir outputs/sample2

python scripts/animate_Layer.py --config scripts/demo3.yaml --savedir outputs/sample3

python scripts/animate_Layer.py --config scripts/demo4.yaml --savedir outputs/sample4

python scripts/animate_Layer.py --config scripts/demo5.yaml --savedir outputs/sample5

Note that the layer-level controls are prepared in __assets__/demos.

Run demo locally

You can run the demo locally by executing the following command:

python scripts/app.py --savedir outputs/gradio

Then, open the link in your browser to access the demo interface. The output video and the video with trajectory will be saved in the outputs/gradio directory.

DiT variant (Wan2.1 1.3B)

Run the following command to generate a video from input images:

python scripts/infer_DiT.py --config __assets__/demos/realworld/config.yaml --savedir outputs/realworld

We take the config.yaml in demos/realworld/ as an example. You can also modify the config file to suit your needs.

Todo

• Release the code and checkpoint of LayerAnimate.
• Upload a gradio script to run the demo locally.
• Create a online demo in the huggingface space.
• DiT-based LayerAnimate.
• Release checkpoints trained under single control modality with better performance.
• Release layer curation pipeline.
• Training script for LayerAnimate.

Acknowledgements

We sincerely thank the great work ToonCrafter, LVCD, AniDoc, and Wan-Video for their inspiring work and contributions to the AIGC community.

Citation

Please consider citing our work as follows if it is helpful.

@article{yang2025layeranimate,
  author    = {Yang, Yuxue and Fan, Lue and Lin, Zuzeng and Wang, Feng and Zhang, Zhaoxiang},
  title     = {LayerAnimate: Layer-level Control for Animation},
  journal   = {arXiv preprint arXiv:2501.08295},
  year      = {2025},
}