You can use EchoMimic & EchoMimic V2 in comfyui
Echomimic：Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning
Echomimic_v2: Towards Striking, Simplified, and Semi-Body Human Animation
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新增输入图片跟基准图片对齐功能（选择pose_normal_sapiens时自动开启，3种驱动方式都能使用，见下面的示例图），修复之前的蒙版对齐错误。
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Added the function of aligning the input image with the reference image (automatically turned on when selecting pose_normal_sapiens, all three driving methods can be used，See the example diagram below), fixed the previous mask alignment error.
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V2版现在跟V1一样，有三种pose驱动方式，第一种，infer_mode选择audio_drive,pose_dir 选择列表里的几个默认pose，则使用默认的npy pose文件，第二种，infer_mode选择audio_drive,pose_dir 选择已有的npy文件夹（位于...ComfyUI/input/tensorrt_lite目录下），第三种，infer_mode选择pose_normal_dwpose 或pose_normal_sapiens,video_images连接视频入口，确认...ComfyUI/models/echo_mimic 下有yolov8m.pt 和sapiens_1b_goliath_best_goliath_AP_639_torchscript.pt2 模型 （见图示和example里的工作流,下载地址见后附）；
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因为调用了sapiens的pose方法，所以需要安装yolo的库ultralytics ，安装方法： pip install ultralytics
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The V2 version now has three different pose driving methods, just like the V1 version. The first method is to select audio_drive for infer_mode and default poses from the list for pose_dir, using the default npy pose file. The second method is to select audio-drive for infer_mode and an existing npy folder (located in the... ComfyUI/input/tensorrt_lite directory) for pose_dir. The third method is to select 'pose_normal_dwpose' or 'pose_normal_sapiens' for infer_mode, connect to the video portal with video_images, and confirm Under ComfyUI/models/echo_mimic, there are 'YOLOV8m.pt' and 'sapiens_1b_goliath_best_goliath_AP_639_torchscript.pt2' models (see the workflow in the diagram and example,Please see the download link below)
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Because the pose method of ‘Sapiens’ was called, it is necessary to install YOLO's library ultralytics. Installation method： pip install ultralytics
In the ./ComfyUI /custom_node directory, run the following:
git clone https://github.com/smthemex/ComfyUI_EchoMimic.git
pip install -r requirements.txt
pip install --no-deps facenet-pytorch
- 如果安装facenet-pytorch后comfyUI奔溃，可以先卸载torch，然后再重新安装，以下版本只是示例：
- if comfyUI broken after pip install facenet-pytorch ,try this below:
pip uninstall torchaudio torchvision torch xformers
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install xformers
- 如果使用的是便携包版本在python_embeded目录下 打开CMD ;
- If it is a portable package comfyUI： open CMD in python_embeded dir
python -m pip uninstall torchaudio torchvision torch xformers
python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
python -m pip install xformers
- 如果ffmpeg 报错，if ffmpeg error：
pip uninstall ffmpeg
pip install ffmpeg-python
- 其他库缺啥装啥。。。
- If the module is missing, , pip install missing module.
If using conda & python >3.12
Uninstall all & downgrade python
pip uninstall torchaudio torchvision torch xformers ffmpeg
conda uninstall python
conda install python=3.11.9
pip install --upgrade pip wheel
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=11.8 -c pytorch -c nvidia
or install torch 2.4
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
Should have most of these packages if you install the custom nodes from git urls
pip install flash-attn spandrel opencv-python diffusers jwt diffusers bitsandbytes omegaconf decord carvekit insightface easydict open_clip ffmpeg-python taming onnxruntime
- 3.1 V1 & V2 Shared model v1 和 v2 共用的模型:
如果能直连抱脸,点击就会自动下载所需模型,不需要手动下载.
3.11 unet link
3.12 V1 & V2 audio link
3.13 vae(stabilityai/sd-vae-ft-mse) link
3.14 optional (可选) hallo upscale huggingface # auto downlad
├── ComfyUI/models/ echo_mimic
| ├── unet
| ├── diffusion_pytorch_model.bin
| ├── config.json
| ├── audio_processor
| ├── whisper_tiny.pt
| ├── vae
| ├── diffusion_pytorch_model.safetensors
| ├── config.json
- 3.2 V1 models V1使用以下模型:
V1 address link
Audio-Drived Algo Inference 音频驱动
├── ComfyUI/models/echo_mimic
| ├── denoising_unet.pth
| ├── face_locator.pth
| ├── motion_module.pth
| ├── reference_unet.pth
Audio-Drived Algo Inference acc 音频驱动加速版
├── ComfyUI/models/echo_mimic
| ├── denoising_unet_acc.pth
| ├── face_locator.pth
| ├── motion_module_acc.pth
| ├── reference_unet.pth
Using Pose-Drived Algo Inference 姿态驱动
├── ComfyUI/models/echo_mimic
| ├── denoising_unet_pose.pth
| ├── face_locator_pose.pth
| ├── motion_module_pose.pth
| ├── reference_unet_pose.pth
Using Pose-Drived Algo Inference ACC 姿态驱动加速版
├── ComfyUI/models/echo_mimic
| ├── denoising_unet_pose_acc.pth
| ├── face_locator_pose.pth
| ├── motion_module_pose_acc.pth
| ├── reference_unet_pose.pth
3.2 v2 version
use model below V2, Automatic download, you can manually add it 使用以下模型,使用及自动下载,你可以手动添加:
模型地址address:huggingface
├── ComfyUI/models/echo_mimic/v2
| ├── denoising_unet.pth
| ├── motion_module.pth
| ├── pose_encoder.pth
| ├── reference_unet.pth
YOLOm8 download link
sapiens pose download link
sapiens的pose 模型可以量化为fp16的，详细见我的sapiens插件 地址
├── ComfyUI/models/echo_mimic
| ├── yolov8m.pt
| ├── sapiens_1b_goliath_best_goliath_AP_639_torchscript.pt2 or/或者 sapiens_1b_goliath_best_goliath_AP_639_torchscript_fp16.pt2
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自动对齐输入图片Automatically align input images；
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V2加载自定义视频驱动视频，V2 loads custom video driver videos
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V2选择自定义pose驱动视频，V2 Choose Custom Pose Driver Video
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Echomimic_v2 use default pose new version 使用官方默认的pose文件
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motion_sync Extract facial features directly from the video (with the option of voice synchronization), while generating a PKL model for the reference video ，The old version 直接从从视频中提取面部特征(可以选择声音同步),同时生成参考视频的pkl模型 旧版
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mormal Audio-Drived Algo Inference The old version workflow 音频驱动视频常规示例 旧版
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mormal Audio-Drived Algo Inference The old version workflow 音频驱动视频常规示例 2倍放大 1024*1024 旧版本
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pose from pkl，The old version, 基于预生成的pkl模型生成视频. 旧版
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示例的 VH node ComfyUI-VideoHelperSuite node: ComfyUI-VideoHelperSuite
--infer_mode：音频驱动视频生成，“audio_drived” 和"audio_drived_acc"；
--infer_mode：参考pkl模型文件视频pose生成 "pose_normal", "pose_acc"；
----motion_sync：如果打开且video_file有视频文件时，生成pkl文件，并生成参考视频的视频；pkl文件在input\tensorrt_lite 目录下，再次使用需要重启comfyUI。
----motion_sync：如果关闭且pose_mode不为none的时候，读取选定的pose_mode目录名的pkl文件，生成pose视频；如果pose_mode为空的时候，生成基于默认assets\test_pose_demo_pose的视频
特别的选项：
--save_video：如果不想使用VH节点时，可以开启，默认关闭；
--draw_mouse：你可以试试；
--length：帧数，时长等于length/fps；
--acc模型 ，6步就可以，但是质量略有下降；
--lowvram :低显存用户可以开启 lowvram users can enable it
--内置内置图片等比例裁切。
特别注意的地方：
--cfg数值设置为1，仅在turbo模式有效，其他会报错。
Infir_mode: Audio driven video generation, "audio-d rived" and "audio-d rived_acc";
Infer_rode: Refer to the PKL model file to generate "pose_normal" and "pose_acc" for the video pose;
Motion_Sync: If opened and there is a video file in videoFILE, generate a pkl file and generate a reference video for the video; The pkl file is located in the input \ sensorrt_lite directory. To use it again, you need to restart ComfyUI.
Motion_Sync: If turned off and pose_mode is not 'none', read the pkl file of the selected pose_mode directory name and generate a pose video; If pose_mode is empty, generate a video based on the default assets \ test_pose_demo_pose
Special options:
--Save_video: If you do not want to use VH nodes, it can be turned on and turned off by default;
--Draw_mause: You can try it out;
--Length: frame rate, duration equal to length/fps;
--The ACC model only requires 6 steps, but the quality has slightly decreased;
--Built in image proportional cropping.
Special attention should be paid to:
--The cfg value is set to 1, which is only valid in turbo mode, otherwise an error will be reported.
既往更新：
- 增加detection_Resnet50_Final.pth 和RealESRGAN_x2plus.pth自动下载的代码，首次使用，保持realesrgan和face_detection_model菜单为‘none’（无）时就会自动下载，如果菜单里已有模型，请选择模型。
- 新增hallo2的2倍放大节点，输入视频的尺寸必须是512 * 512方形，输出为1024 * 1024
- 当你用torch 2.2.0+cuda 成功安装最新的facenet-pytorch库后，可以卸载掉基于 2.2.0版本的torch torchvision torchaudio xformers 然后重新安装更高版本的torch torchvision torchaudio xformers，以下是卸载和安装的示例（假设安装torch2.4）：
- 添加lowvram模式，方便6G或者8G显存用户使用，注意，开启之后会很慢，而且占用内存较大，请谨慎尝试。
- 修改vae模型的加载方式，移至ComfyUI/models/echo_mimic/vae路径（详细见下方模型存放地址指示图），降低hf加载模型的优先级，适用于无梯子用户。
Previous updates：
- The magnification factor of 'facecrop-ratio' is '1/facecrop-ratio'. If set to 0.5, the face will be magnified twice. It is recommended to adjust facecrop-ratio to a smaller value only when the proportion of faces in the reference image or driving video is very small,Do not cut when it is 1 or 0;
- facecrop_ratio的放大系数为1/facecrop_ratio，如果设置为0.5，面部会得到2倍的放大，建议只在参考图片或者驱动视频中的人脸占比很小的时候，才将facecrop_ratio调整为较小的值.为1 或者0 时不裁切
- Add upscale model and Resnet model auto download codes（if had ，they in comfyUI/models/upscale_models/RealESRGAN_x2plus.pth and comfyUI/models/Hallo/facelib/detection_Resnet50_Final.pth）， first use ，keep “realesrgan” and “face_detection_model” ‘none’ will auto download..
- After successfully installing the latest ‘facenet-pytorch’ library using torch 2.2.0+CUDA, you can uninstall torch torch vision torch audio xformers based on version 2.2.0 and then reinstall a higher version of torch、 torch vision、 torch audio xformers. Here is an example of uninstallation and installation (installing torch 2.4):
- Add lowvram mode for convenient use by 6G or 8G video memory users. Please note that it will be slow and consume a large amount of memory when turned on. Please try carefully
EchoMimici
@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}
}EchoMimici-V2
@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}
}hallo2
@misc{cui2024hallo2,
title={Hallo2: Long-Duration and High-Resolution Audio-driven Portrait Image Animation},
author={Jiahao Cui and Hui Li and Yao Yao and Hao Zhu and Hanlin Shang and Kaihui Cheng and Hang Zhou and Siyu Zhu and️ Jingdong Wang},
year={2024},
eprint={2410.07718},
archivePrefix={arXiv},
primaryClass={cs.CV}
}sapiens
@article{khirodkar2024sapiens,
title={Sapiens: Foundation for Human Vision Models},
author={Khirodkar, Rawal and Bagautdinov, Timur and Martinez, Julieta and Zhaoen, Su and James, Austin and Selednik, Peter and Anderson, Stuart and Saito, Shunsuke},
journal={arXiv preprint arXiv:2408.12569},
year={2024}
}