gfpgan

Maintainer: xinntao

Total Score

9.3K

Last updated 6/21/2024
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API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

gfpgan is a practical face restoration algorithm developed by Tencent ARC, aimed at restoring old photos or AI-generated faces. It leverages rich and diverse priors encapsulated in a pretrained face GAN (such as StyleGAN2) for blind face restoration. This approach is contrasted with similar models like Codeformer which also focus on robust face restoration, and upscaler which aims for general image restoration, while ESRGAN specializes in image super-resolution and GPEN focuses on blind face restoration in the wild.

Model inputs and outputs

gfpgan takes in an image as input and outputs a restored version of that image, with the faces improved in quality and detail. The model supports upscaling the image by a specified factor.

Inputs

  • img: The input image to be restored

Outputs

  • Output: The restored image with improved face quality and detail

Capabilities

gfpgan can effectively restore old or low-quality photos, as well as faces in AI-generated images. It leverages a pretrained face GAN to inject realistic facial features and details, resulting in natural-looking face restoration. The model can handle a variety of face poses, occlusions, and image degradations.

What can I use it for?

gfpgan can be used for a range of applications involving face restoration, such as improving old family photos, enhancing AI-generated avatars or characters, and restoring low-quality images from social media. The model's ability to preserve identity and produce natural-looking results makes it suitable for both personal and commercial use cases.

Things to try

Experiment with different input image qualities and upscaling factors to see how gfpgan handles a variety of restoration scenarios. You can also try combining gfpgan with other models like Real-ESRGAN to enhance the non-face regions of the image for a more comprehensive restoration.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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