Arc2Face Model Card

Project Page | Paper (ArXiv) | Code | 🤗 Gradio demo

Introduction

Arc2Face is an ID-conditioned face model, that can generate diverse, ID-consistent photos of a person given only its ArcFace ID-embedding. It is trained on a restored version of the WebFace42M face recognition database, and is further fine-tuned on FFHQ and CelebA-HQ.

Model Details

It consists of 2 components:

• encoder, a finetuned CLIP ViT-L/14 model
• arc2face, a finetuned UNet model

both of which are fine-tuned from runwayml/stable-diffusion-v1-5. The encoder is tailored for projecting ID-embeddings to the CLIP latent space. Arc2Face adapts the pre-trained backbone to the task of ID-to-face generation, conditioned solely on ID vectors.

Usage

The models can be downloaded directly from this repository or using python:

from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="arc2face/config.json", local_dir="./models")
hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="arc2face/diffusion_pytorch_model.safetensors", local_dir="./models")
hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="encoder/config.json", local_dir="./models")
hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="encoder/pytorch_model.bin", local_dir="./models")

Please check our GitHub repository for complete inference instructions.

Limitations and Bias

• Only one person per image can be generated.
• Poses are constrained to the frontal hemisphere, similar to FFHQ images.
• The model may reflect the biases of the training data or the ID encoder.

Citation

BibTeX:

@misc{paraperas2024arc2face,
      title={Arc2Face: A Foundation Model of Human Faces}, 
      author={Foivos Paraperas Papantoniou and Alexandros Lattas and Stylianos Moschoglou and Jiankang Deng and Bernhard Kainz and Stefanos Zafeiriou},
      year={2024},
      eprint={2403.11641},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}