EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning
*Equal Contribution.
Terminal Technology Department, Alipay, Ant Group.
📣 📣 Updates
[2024.08.02] 🔥 EchoMimic is now available on huggingface with A100 GPU. Thanks Wenmeng Zhou@ModelScope.
[2024.07.25] 🔥🔥🔥 Accelerated models and pipe on Audio Driven are released. The inference speed can be improved by 10x (from ~7mins/240frames to ~50s/240frames on V100 GPU)
[2024.07.23] 🔥 EchoMimic gradio demo on modelscope is ready.
[2024.07.23] 🔥 EchoMimic gradio demo on huggingface is ready. Thanks Sylvain Filoni@fffiloni.
[2024.07.17] 🔥🔥🔥 Accelerated models and pipe on Audio + Selected Landmarks are released. The inference speed can be improved by 10x (from ~7mins/240frames to ~50s/240frames on V100 GPU)
[2024.07.14] 🔥 ComfyUI is now available. Thanks @smthemex for the contribution.
[2024.07.13] 🔥 Thanks NewGenAI for the video installation tutorial .
[2024.07.13] 🔥 We release our pose&audio driven codes and models.
[2024.07.12] 🔥 WebUI and GradioUI versions are released. We thank @greengerong @Robin021 and @O-O1024 for their contributions.
[2024.07.12] 🔥 Our paper is in public on arxiv.
[2024.07.09] 🔥 We release our audio driven codes and models.
Gallery
Audio Driven (Sing)
Audio Driven (English)
Audio Driven (Chinese)
Landmark Driven
Audio + Selected Landmark Driven
（Some demo images above are sourced from image websites. If there is any infringement, we will immediately remove them and apologize.）
Installation
Download the Codes
1 git clone https://github.com/BadToBest/EchoMimic
2 cd EchoMimic
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):
1 conda create -n echomimic python = 3.8
2 conda activate echomimic
Install packages with pip
pip install -r requirements.txtDownload ffmpeg-static
Download and decompress
ffmpeg-static , then
export FFMPEG_PATH=/path/to/ffmpeg-4.4-amd64-staticDownload pretrained weights
1 git lfs install
2 git clone https://huggingface.co/BadToBest/EchoMimic pretrained_weights
The pretrained_weights is organized as follows.
./pretrained_weights/
├── denoising_unet.pth
├── reference_unet.pth
├── motion_module.pth
├── face_locator.pth
├── sd-vae-ft-mse
│ └── ...
├── sd-image-variations-diffusers
│ └── ...
└── audio_processor
└── whisper_tiny.ptIn 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:
Audio-Drived Algo Inference
Run the python inference script:
1 python -u infer_audio2vid.py
2 python -u infer_audio2vid_pose.py
Audio-Drived Algo Inference On Your Own Cases
Edit the inference config file ./configs/prompts/animation.yaml , and add your own case:
1 test_cases:
2 "path/to/your/image" :
3 - "path/to/your/audio"
The run the python inference script:
python -u infer_audio2vid.pyMotion Alignment between Ref. Img. and Driven Vid.
(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.pyAudio&Pose-Drived Algo Inference
Edit ./configs/prompts/animation_pose.yaml, then run
python -u infer_audio2vid_pose.pyPose-Drived Algo Inference
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.pyRun the Gradio UI
Thanks to the contribution from @Robin021:
1
2 python -u webgui.py --server_port = 3000
3
Release Plans
Status Milestone ETA ✅ The inference source code of the Audio-Driven algo meet everyone on GitHub 9th July, 2024 ✅ Pretrained models trained on English and Mandarin Chinese to be released 9th July, 2024 ✅ The inference source code of the Pose-Driven algo meet everyone on GitHub 13th July, 2024 ✅ Pretrained models with better pose control to be released 13th July, 2024 ✅ Accelerated models to be released 17th July, 2024 🚀 Pretrained models with better sing performance to be released TBD 🚀 Large-Scale and High-resolution Chinese-Based Talking Head Dataset TBD
Acknowledgements
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.
Citation
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}
}Star History
