Gpen-bfr-2048.pth [2021] Access

For instance, if you are using the , you would typically place this file in the models/GFPGAN or models/GPEN directory to enable the "Face Restoration" checkbox in your interface.

is a high-performance, pre-trained PyTorch weight file for the GAN Prior Embedded Network (GPEN) , specifically designed for Blind Face Restoration (BFR) at a 2048x2048 resolution . As AI-driven image enhancement becomes standard in creative workflows, this model stands out for its capability to restore extremely high-definition details in faces that are blurred, damaged, or low-resolution. gpen-bfr-2048.pth

The encoder learns to map a degraded image to a latent vector that, when fed to the already‑powerful StyleGAN2 synthesis network, yields a clean high‑resolution face. Because StyleGAN2 is already a generative prior on faces, the output automatically respects facial geometry and texture statistics, even when the input is severely corrupted. For instance, if you are using the ,

# Load the model model = torch.load('gpen-bfr-2048.pth', map_location=torch.device('cpu')) The encoder learns to map a degraded image

: Improving facial clarity in video footage when used in conjunction with temporal-aware processing tools.

The gpen-bfr-2048.pth model can be used for a variety of applications, including:

Stored as a PyTorch checkpoint file containing the trained neural network weights. Core Technical Specifications Specification Primary Framework Output Resolution 2048 x 2048 pixels Base Architecture U-Net + StyleGAN2 Prior File Format .pth (PyTorch) or .onnx (for Open Neural Network Exchange) File Size Approximately 285 MB to 500 MB Pre-Detection Model RetinaFace-R50 Key Advantages of GPEN-BFR-2048