EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
๐ EchoMimic Series
- EchoMimicV1: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning. GitHub
- EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation. GitHub
๐ฃ Updates
- [2024.11.27] ๐ฅ Thanks AiMotionStudio for the installation tutorial.
- [2024.11.22] ๐ฅ GradioUI is now available. Thanks @gluttony-10 for the contribution.
- [2024.11.22] ๐ฅ ComfyUI is now available. Thanks @smthemex for the contribution.
- [2024.11.21] ๐ฅ We release the EMTD dataset list and processing scripts.
- [2024.11.21] ๐ฅ We release our EchoMimicV2 codes and models.
- [2024.11.15] ๐ฅ Our paper is in public on arxiv.
๐ Gallery
Introduction
English Driven Audio
Chinese Driven Audio
โ๏ธ Installation
Download the Codes
git clone https://github.com/antgroup/echomimic_v2
cd echomimic_v2
Python Environment Setup
- Tested System Environment: Centos 7.2/Ubuntu 22.04, Cuda >= 11.7
- Tested GPUs: A100(80G) / RTX4090D (24G) / V100(16G)
- Tested Python Version: 3.8 / 3.10 / 3.11
Create conda environment (Recommended):
conda create -n echomimic python=3.10
conda activate echomimic
Install packages with pip
pip install pip -U
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 xformers==0.0.28.post3 --index-url https://download.pytorch.org/whl/cu124
pip install torchao --index-url https://download.pytorch.org/whl/nightly/cu124
pip install -r requirements.txt
pip install --no-deps facenet_pytorch==2.6.0
Download ffmpeg-static
Download and decompress ffmpeg-static, then
export FFMPEG_PATH=/path/to/ffmpeg-4.4-amd64-static
Download pretrained weights
git lfs install
git clone https://huggingface.co./BadToBest/EchoMimicV2 pretrained_weights
The pretrained_weights is organized as follows.
./pretrained_weights/
โโโ denoising_unet.pth
โโโ reference_unet.pth
โโโ motion_module.pth
โโโ pose_encoder.pth
โโโ sd-vae-ft-mse
โ โโโ ...
โโโ sd-image-variations-diffusers
โ โโโ ...
โโโ audio_processor
โโโ tiny.pt
In which denoising_unet.pth / reference_unet.pth / motion_module.pth / pose_encoder.pth are the main checkpoints of EchoMimic. Other models in this hub can be also downloaded from it's original hub, thanks to their brilliant works:
Inference on Demo
Run the gradio:
python app.py
Run the python inference script:
python infer.py --config='./configs/prompts/infer.yaml'
EMTD Dataset
Download dataset:
python ./EMTD_dataset/download.py
Slice dataset:
bash ./EMTD_dataset/slice.sh
Process dataset:
python ./EMTD_dataset/preprocess.py
๐ Release Plans
Status | Milestone | ETA |
---|---|---|
โ | The inference source code of EchoMimicV2 meet everyone on GitHub | 21st Nov, 2024 |
โ | Pretrained models trained on English and Mandarin Chinese on HuggingFace | 21st Nov, 2024 |
โ | Pretrained models trained on English and Mandarin Chinese on ModelScope | 21st Nov, 2024 |
โ | EMTD dataset list and processing scripts | 21st Nov, 2024 |
๐ | Accelerated models to be released | TBD |
๐ | Online Demo on ModelScope to be released | TBD |
๐ | Online Demo on HuggingFace to be released | TBD |
โ๏ธ 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.
๐๐ป Acknowledgements
We would like to thank the contributors to the MimicMotion and Moore-AnimateAnyone repositories, for their open research and exploration.
We are also grateful to CyberHost and Vlogger for their outstanding work in the area of audio-driven human animation.
If we missed any open-source projects or related articles, we would like to complement the acknowledgement of this specific work immediately.
๐ Citation
If you find our work useful for your research, please consider citing the paper :
@misc{meng2024echomimic,
title={EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation},
author={Rang Meng, Xingyu Zhang, Yuming Li, Chenguang Ma},
year={2024},
eprint={2411.10061},
archivePrefix={arXiv},
primaryClass={cs.CV}
}