Gpen-bfr-2048.pth !!link!! Direct

A typical workflow using a script (like the popular run_gpen.py ) looks like this:

You can then use the model to generate images by providing a random noise vector as input.

Suggest in a face-swapping software like ReActor. Let me know how you'd like to explore this tool further . github.com yangxy/GPEN - GitHub gpen-bfr-2048.pth

While it was briefly taken down by the original authors due to "commercial issues," it is currently hosted on platforms like ModelScope and Hugging Face for public research and use. GPEN/README.md at main - GitHub

It works by embedding a Generative Adversarial Network (GAN) prior into a Deep Neural Network, effectively using the "knowledge" of what faces look like to fill in missing details in blurry or damaged photos. A typical workflow using a script (like the popular run_gpen

model is capable of reconstructing much higher-frequency details, making it ideal for images intended for large-scale printing or high-DPI displays.

file is the "brain" of a GAN Prior Embedded Network. While most restoration AI tries to guess what a pixel should look like, GPEN uses a Generative Adversarial Network (GAN) prior github

The .pth extension identifies it as a PyTorch model file, containing the learned weights and parameters required to run the restoration algorithm. KenjieDec - Hugging Face