You can apply makeup to the characters in comfyui Stable_Makeup From: a/Stable_Makeup
You can apply makeup to the characters in comfyui
Stable_Makeup From: Stable_Makeup
2024/09/06
Previous updates
*剔除diffuser模型,改成单体的模型 “v1-5-pruned-emaonly.safetensors”,
*可以尝试不同的数据集,当然,意味着你要多下载几个SPIGA模型;
--- You can try different datasets, of course, which means you need to download a few more SPIGA models;
---Fix the error where models that were not downloaded in advance cannot be loaded;
In the ./ComfyUI /custom_node directory, run the following:
git clone https://github.com/smthemex/ComfyUI_Stable_Makeup.git
按理是不需要装特别的库,因为内置了,如果还是库丢失,请单独安装。或者打开no need requirements.txt,查看缺失的库是否在里面。
秋叶包因为是沙箱模式,所以把缺失的库安装在系统的python库里。
官方的整合包,用pip install AAA --target="BBB/python_embeded/Lib/site-packages" AAA替换为缺失的库,BBB是本地路径。
If the module is missing, please open "no need requirements.txt" , pip install missing module.
模型的下载地址比较杂,所以使用前请下下载,并存放在ComfyUI/models/stable_makeup 文件夹下:
The download address for the model is quite miscellaneous, so please download it before use and store it in the ComfyUI/models/table_makeup folder:
3.1 spiga_300wpublic.pt or other models link
3.2 pytorch_model.bin
pytorch_model_1.bin
pytorch_model_2.bin link
3.3 mobilenet0.25_Final.pth link
or
resnet50.pth link
3.4 clip模型,外置为输入格式,可以引导至本地其他路径。
"openai/clip-vit-large-patch14" clip models
3.5 SD1.5 “v1-5-pruned-emaonly.safetensors” link
├── ComfyUI/models/
| ├──stable_makeup
| ├── mobilenet0.25_Final.pth
| ├── pytorch_model.bin
| ├── pytorch_model_1.bin
| ├── pytorch_model_2.bin
| ├── spiga_300wpublic.pt
| ├── resnet50.pth
| ├──checkpoints
| ├── v1-5-pruned-emaonly.safetensors
首次使用需要下载“runwayml/stable-diffusion-v1-5”的配套config文件,及openai/clip-vit-large-patch14
@article{zhang2024stable,
title={Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model},
author={Zhang, Yuxuan and Wei, Lifu and Zhang, Qing and Song, Yiren and Liu, Jiaming and Li, Huaxia and Tang, Xu and Hu, Yao and Zhao, Haibo},
journal={arXiv preprint arXiv:2403.07764},
year={2024}
}
SPIGA From: SPIGA
@inproceedings{Prados-Torreblanca_2022_BMVC,
author = {Andrés Prados-Torreblanca and José M Buenaposada and Luis Baumela},
title = {Shape Preserving Facial Landmarks with Graph Attention Networks},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0155.pdf}
}
FaceLib From: FaceLib