Face-Adapter is an efficient and effective face editing adapter for pre-trained diffusion models, specifically targeting face reenactment and swapping tasks.
- [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}
}