🤗 Introduction

This repository reproduces AnimateAnyone. To align the results demonstrated by the original paper, we adopt various approaches and tricks, which may differ somewhat from the paper and another implementation.

It's worth noting that this is a very preliminary version, aiming for approximating the performance (roughly 80% under our test) showed in AnimateAnyone.

We will continue to develop it, and also welcome feedbacks and ideas from the community. The enhanced version will also be launched on our MoBi MaLiang AIGC platform, running on our own full-featured GPU S4000 cloud computing platform.

📝 Release Plans

Inference codes and pretrained weights
Training scripts

Note The training code involves private data and packages. We will organize this portion of the code as soon as possible and then release it.

🎞️ Examples

Here are some results we generated, with the resolution of 512x768.

https://github.com/MooreThreads/Moore-AnimateAnyone/assets/138439222/f0454f30-6726-4ad4-80a7-5b7a15619057

https://github.com/MooreThreads/Moore-AnimateAnyone/assets/138439222/337ff231-68a3-4760-a9f9-5113654acf48

Limitation: We observe following shortcomings in current version:

The background may occur some artifacts, when the reference image has a clean background
Suboptimal results may arise when there is a scale mismatch between the reference image and keypoints. We have yet to implement preprocessing techniques as mentioned in the paper.
Some flickering and jittering may occur when the motion sequence is subtle or the scene is static.

These issues will be addressed and improved in the near future. We appreciate your anticipation!

⚒️ Installation

Build Environtment

We Recommend a python version >=3.10 and cuda version =11.7. Then build environment as follows:

# [Optional] Create a virtual env
python -m venv .venv
source .venv/bin/activate
# Install with pip:
pip install -r requirements.txt

Download weights

Download our trained weights, which include four parts: denoising_unet.pth, reference_unet.pth, pose_guider.pth and motion_module.pth.

Download pretrained weight of based models and other components:

Download dwpose weights (dw-ll_ucoco_384.onnx, yolox_l.onnx) following this.

Put these weights under a directory, like ./pretrained_weights, and orgnize them as follows:

./pretrained_weights/
|-- DWPose
|   |-- dw-ll_ucoco_384.onnx
|   `-- yolox_l.onnx
|-- image_encoder
|   |-- config.json
|   `-- pytorch_model.bin
|-- denoising_unet.pth
|-- motion_module.pth
|-- pose_guider.pth
|-- reference_unet.pth
|-- sd-vae-ft-mse
|   |-- config.json
|   |-- diffusion_pytorch_model.bin
|   `-- diffusion_pytorch_model.safetensors
`-- stable-diffusion-v1-5
    |-- feature_extractor
    |   `-- preprocessor_config.json
    |-- model_index.json
    |-- unet
    |   |-- config.json
    |   `-- diffusion_pytorch_model.bin
    `-- v1-inference.yaml

Note: If you have installed some of the pretrained models, such as StableDiffusion V1.5, you can specify their paths in the config file (e.g. ./config/prompts/animation.yaml).

🚀 Inference

Here is the cli command for running inference scripts:

python -m scripts.pose2vid --config ./configs/prompts/animation.yaml -W 512 -H 784 -L 64

You can refer the format of animation.yaml to add your own reference images or pose videos. To convert the raw video into a pose video (keypoint sequence), you can run with the following command:

python tools/vid2pose.py --video_path /path/to/your/video.mp4

🎨 Gradio Demo

You can run a local gradio app via following commands:

python app.py

🖌️ Try on Mobi MaLiang

We will launched this model on our MoBi MaLiang AIGC platform, running on our own full-featured GPU S4000 cloud computing platform. Mobi MaLiang has now integrated various AIGC applications and functionalities (e.g. text-to-image, controllable generation...). You can experience it by clicking this link or scanning the QR code bellow via WeChat!

⚖️ Disclaimer

This project is intended for academic research, and we explicitly disclaim any responsibility for user-generated content. Users are solely liable for their actions while using the generative model. The project contributors have no legal affiliation with, nor accountability for, users' behaviors. It is imperative to use the generative model responsibly, adhering to both ethical and legal standards.

🙏🏻 Acknowledgements

We first thank the authors of AnimateAnyone. Additionally, we would like to thank the contributors to the majic-animate, animatediff and Open-AnimateAnyone repositorities, for their open research and exploration. Furthermore, our repo incorporates some codes from dwpose and animatediff-cli-prompt-travel, and we extend our thanks to them as well.