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.

ControlNet (pose)

We also provide a ControlNet model trained on top of Arc2Face for pose control.

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")
hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="controlnet/config.json", local_dir="./models")
hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="controlnet/diffusion_pytorch_model.safetensors", 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}
}

FoivosPar
/

Arc2Face