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  license: apache-2.0
 
 
 
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  license: apache-2.0
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+ language:
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+ - en
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+ library_name: diffusers
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+
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+ # Model Card for Arc2Face
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+
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+ <div align="center">
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+
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+ [**Project Page**](https://arc2face.github.io/) **|** [**Paper (ArXiv)**]() **|** [**Code**](https://github.com/foivospar/Arc2Face)
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+
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+
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+ </div>
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+
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+ ## Introduction
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+
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+ Arc2Face is an ID-conditioned face model, that can generate diverse, ID-consistent photos of a person given only its ArcFace ID-embedding.
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+ It is trained on a restored version of the WebFace42M face recognition database, and is further fine-tuned on FFHQ and CelebA-HQ.
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+
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+ <div align="center">
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+ <img src='assets/samples_short.jpg'>
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+ </div>
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+
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+ ## Model Details
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+
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+ It consists of 2 components:
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+ - encoder, a finetuned CLIP ViT-L/14 model
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+ - arc2face, a finetuned UNet model
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+
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+ both of which are fine-tuned from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
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+ The encoder is tailored for projecting ID-embeddings to the CLIP latent space.
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+ Arc2Face adapts the pre-trained backbone to the task of ID-to-face generation, conditioned solely on ID vectors.
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+
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+ ## Usage
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+
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+ The models can be downloaded directly from this repository or using python:
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+ ```python
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+ from huggingface_hub import hf_hub_download
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+
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+ hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="arc2face/config.json", local_dir="./models/arc2face")
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+ hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="arc2face/diffusion_pytorch_model.safetensors", local_dir="./models/arc2face")
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+ hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="encoder/config.json", local_dir="./models/encoder")
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+ hf_hub_download(repo_id="FoivosPar/Arc2Face", filename="encoder/pytorch_model.bin", local_dir="./models/encoder")
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+ ```
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+
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+ Please check our [GitHub repository](https://arc2face.github.io/) for complete inference instruction.
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+
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+ ## Limitations and Bias
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+
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+ - Only one person per image can be generated.
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+ - Poses are constrained to the frontal hemisphere, similar to FFHQ images.
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+ - The model may reflect the biases of the training data or the ID encoder.
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+
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+ ## Citation
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+
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+
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+ **BibTeX:**
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+
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+ ```bibtex
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+ ```