οΌSome demo images above are sourced from image websites. If there is any infringement, we will immediately remove them and apologize.οΌ
git clone https://github.com/BadToBest/EchoMimic
cd EchoMimic
Create conda environment (Recommended):
conda create -n echomimic python=3.8
conda activate echomimic
Install packages with pip
pip install -r requirements.txt
Download and decompress ffmpeg-static, then
export FFMPEG_PATH=/path/to/ffmpeg-4.4-amd64-static
git lfs install
git clone https://huggingface.co/BadToBest/EchoMimic pretrained_weights
The pretrained_weights is organized as follows.
./pretrained_weights/
βββ denoising_unet.pth
βββ reference_unet.pth
βββ motion_module.pth
βββ face_locator.pth
βββ sd-vae-ft-mse
β βββ ...
βββ sd-image-variations-diffusers
β βββ ...
βββ audio_processor
βββ whisper_tiny.pt
In which denoising_unet.pth / reference_unet.pth / motion_module.pth / face_locator.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:
Run the python inference script:
python -u infer_audio2vid.py
python -u infer_audio2vid_pose.py
Edit the inference config file ./configs/prompts/animation.yaml, and add your own case:
test_cases:
"path/to/your/image":
- "path/to/your/audio"
The run the python inference script:
python -u infer_audio2vid.py
(Firstly download the checkpoints with '_pose.pth' postfix from huggingface)
Edit driver_video and ref_image to your path in demo_motion_sync.py, then run
python -u demo_motion_sync.py
Edit ./configs/prompts/animation_pose.yaml, then run
python -u infer_audio2vid_pose.py
Set draw_mouse=True in line 135 of infer_audio2vid_pose.py. Edit ./configs/prompts/animation_pose.yaml, then run
python -u infer_audio2vid_pose.py
Thanks to the contribution from @Robin021:
python -u webgui.py --server_port=3000
Status | Milestone | ETA |
---|---|---|
β | The inference source code of the Audio-Driven algo meet everyone on GitHub | 9th July, 2024 |
β | Pretrained models trained on English and Mandarin Chinese to be released | 9th July, 2024 |
β | The inference source code of the Pose-Driven algo meet everyone on GitHub | 13th July, 2024 |
β | Pretrained models with better pose control to be released | 13th July, 2024 |
β | Accelerated models to be released | 17th July, 2024 |
π | Pretrained models with better sing performance to be released | TBD |
π | Large-Scale and High-resolution Chinese-Based Talking Head Dataset | TBD |
We would like to thank the contributors to the AnimateDiff, Moore-AnimateAnyone and MuseTalk repositories, for their open research and exploration.
We are also grateful to V-Express and hallo for their outstanding work in the area of diffusion-based talking heads.
If we missed any open-source projects or related articles, we would like to complement the acknowledgement of this specific work immediately.
If you find our work useful for your research, please consider citing the paper :
@misc{chen2024echomimic,
title={EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning},
author={Zhiyuan Chen, Jiajiong Cao, Zhiquan Chen, Yuming Li, Chenguang Ma},
year={2024},
archivePrefix={arXiv},
primaryClass={cs.CV}
}