Face-Adapter is an efficient and effective face editing adapter for pre-trained diffusion models, specifically targeting face reenactment and swapping tasks.
- [2024/5/25] ?? We release the gradio demo.
- [2024/5/24] ?? We release the code and models.
# Torch >= 2.0 recommended for acceleration without xformers
pip install accelerate diffusers==0.26.0 insightface onnxruntime
You can download models of FaceAdapter directly from here or download using python script:
# Download all files
from huggingface_hub import snapshot_download
snapshot_download(repo_id="FaceAdapter/FaceAdapter", local_dir="./checkpoints")
# If you want to download one specific file
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="FaceAdapter/FaceAdapter", filename="controlnet/config.json", local_dir="./checkpoints")To run the demo, you should also download the pre-trained SD models below:
python infer.py You can adjust the cropping size with the --crop_ratio （default:0.81）parameter. But be careful not to set the crop range too large, as this can decrease the quality of the generated images due to the limit of the training data size.
?? FaceAdapter can be seamlessly plugged into community models:
python infer.py --base_model "frankjoshua/toonyou_beta6"
This project strives to positively impact the domain of AI-driven image generation. Users are granted the freedom to create images using this tool, but they are expected to comply with local laws and utilize it in a responsible manner. The developers do not assume any responsibility for potential misuse by users.
If you find Face-Adapter useful for your research and applications, please cite using this BibTeX:
@article{han2024face,
title={Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control},
author={Han, Yue and Zhu, Junwei and He, Keke and Chen, Xu and Ge, Yanhao and Li, Wei and Li, Xiangtai and Zhang, Jiangning and Wang, Chengjie and Liu, Yong},
journal={arXiv preprint arXiv:2405.12970},
year={2024}
}