EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
Terminal Technology Department, Alipay, Ant Group.
## 🚀 EchoMimic Series
* EchoMimicV1: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning. [GitHub](https://github.com/antgroup/echomimic)
* EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation. [GitHub](https://github.com/antgroup/echomimic_v2)
## 📣 Updates
* [2024.11.27] 🔥 Thanks [AiMotionStudio](https://www.youtube.com/@AiMotionStudio) for the [installation tutorial](https://www.youtube.com/watch?v=2ab6U1-nVTQ).
* [2024.11.22] 🔥 [GradioUI](https://github.com/antgroup/echomimic_v2/blob/main/app.py) is now available. Thanks @gluttony-10 for the contribution.
* [2024.11.22] 🔥 [ComfyUI](https://github.com/smthemex/ComfyUI_EchoMimic) is now available. Thanks @smthemex for the contribution.
* [2024.11.21] 🔥 We release the EMTD dataset list and processing scripts.
* [2024.11.21] 🔥 We release our [EchoMimicV2](https://github.com/antgroup/echomimic_v2) codes and models.
* [2024.11.15] 🔥 Our [paper](https://arxiv.org/abs/2411.10061) is in public on arxiv.
## 🌅 Gallery
### Introduction
### English Driven Audio
### Chinese Driven Audio
## ⚒️ Installation
### Download the Codes
```bash
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):
```bash
conda create -n echomimic python=3.10
conda activate echomimic
```
Install packages with `pip`
```bash
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](https://www.johnvansickle.com/ffmpeg/old-releases/ffmpeg-4.4-amd64-static.tar.xz), then
```
export FFMPEG_PATH=/path/to/ffmpeg-4.4-amd64-static
```
### Download pretrained weights
```shell
git lfs install
git clone https://huggingface.co/BadToBest/EchoMimicV2 pretrained_weights
```
The **pretrained_weights** is organized as follows.
```
./pretrained_weights/
├── denoising_unet.pth
├── reference_unet.pth
├── motion_module.pth
├── pose_encoder.pth
├── sd-vae-ft-mse
│ └── ...
├── sd-image-variations-diffusers
│ └── ...
└── audio_processor
└── 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:
- [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse)
- [sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers)
- [audio_processor(whisper)](https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt)
### Inference on Demo
Run the gradio:
```bash
python app.py
```
Run the python inference script:
```bash
python infer.py --config='./configs/prompts/infer.yaml'
```
### EMTD Dataset
Download dataset:
```bash
python ./EMTD_dataset/download.py
```
Slice dataset:
```bash
bash ./EMTD_dataset/slice.sh
```
Process dataset:
```bash
python ./EMTD_dataset/preprocess.py
```
## 📝 Release Plans
| Status | Milestone | ETA |
|:--------:|:-------------------------------------------------------------------------|:--:|
| ✅ | The inference source code of EchoMimicV2 meet everyone on GitHub | 21st Nov, 2024 |
| ✅ | Pretrained models trained on English and Mandarin Chinese on HuggingFace | 21st Nov, 2024 |
| ✅ | Pretrained models trained on English and Mandarin Chinese on ModelScope | 21st Nov, 2024 |
| ✅ | EMTD dataset list and processing scripts | 21st Nov, 2024 |
| 🚀 | Accelerated models to be released | TBD |
| 🚀 | Online Demo on ModelScope to be released | TBD |
| 🚀 | Online Demo on HuggingFace to be released | TBD |
## ⚖️ 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 would like to thank the contributors to the [MimicMotion](https://github.com/Tencent/MimicMotion) and [Moore-AnimateAnyone](https://github.com/MooreThreads/Moore-AnimateAnyone) repositories, for their open research and exploration.
We are also grateful to [CyberHost](https://cyberhost.github.io/) and [Vlogger](https://enriccorona.github.io/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.
## 📒 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}
}
```
## 🌟 Star History
[](https://star-history.com/#antgroup/echomimic_v2&Date)
