: Restores fine details like skin texture, hair, and eyes from low-quality inputs.
It typically belongs in the models/facerestore/ directory. Step 3: Execution via Python (For Developers)
The file is a pre-trained model weight file used for Blind Face Restoration (BFR) . It is part of the GAN Prior Embedded Network (GPEN) framework, which was introduced in the CVPR 2021 paper GAN Prior Embedded Network for Blind Face Restoration in the Wild . 🧪 Technical Overview gpen-bfr-2048.pth
Place GPEN-BFR-2048.pth in the weights directory. Run the script:
. When used locally, it is often placed in specific cache folders (e.g., ~/.cache/modelscope/hub/damo ) or within the folder of a specific AI tool. GPEN/README.md at main - GitHub : Restores fine details like skin texture, hair,
Unlike older GAN models that would completely change a person's appearance, GPEN is highly optimized to keep the restored face looking like the original person. Common Use Cases
GPEN was introduced in the CVPR 2021 paper GAN Prior Embedded Network for Blind Face Restoration in the Wild by researcher yangxy . It is part of the GAN Prior Embedded
If you are building a custom pipeline, you can load the model programmatically using PyTorch. Below is a simplified conceptual snippet of how the model is called in a Python workflow:
wget "[URL_TO_MODEL]" -O weights/GPEN-BFR-2048.pth