codeformer

Maintainer: sczhou

Total Score

33.4K

Last updated 6/19/2024
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API SpecView on Replicate
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Paper LinkView on Arxiv

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Model overview

The codeformer is a robust face restoration algorithm developed by researchers at the Nanyang Technological University's S-Lab, focused on enhancing old photos or AI-generated faces. It builds upon previous work like GFPGAN and Real-ESRGAN, adding new capabilities for improved fidelity and quality. Unlike GFPGAN which aims for "practical" restoration, codeformer takes a more comprehensive approach to handle a wider range of challenging cases.

Model inputs and outputs

The codeformer model accepts an input image and allows users to control various parameters to balance the quality and fidelity of the restored face. The main input is the image to be enhanced, and the model outputs the restored high-quality image.

Inputs

  • Image: The input image to be restored, which can be an old photo or an AI-generated face.
  • Fidelity: A parameter that controls the balance between quality (lower values) and fidelity (higher values) of the restored face.
  • Face Upsample: A boolean flag to further upsample the restored face with Real-ESRGAN for high-resolution AI-created images.
  • Background Enhance: A boolean flag to enhance the background image along with the face restoration.

Outputs

  • Restored Image: The output image with the face restored and enhanced.

Capabilities

The codeformer model is capable of robustly restoring faces in challenging scenarios, such as low-quality, old, or AI-generated images. It can handle a wide range of degradations, including blurriness, noise, and artifacts, producing high-quality results. The model also supports face inpainting and colorization for cropped and aligned face images.

What can I use it for?

The codeformer model can be used for a variety of applications, such as restoring old family photos, enhancing profile pictures, or fixing defects in AI-generated avatars and artwork. It can be particularly useful for individuals or businesses working with historical archives, digital art, or social media applications. The model's ability to balance quality and fidelity makes it suitable for both creative and practical uses.

Things to try

One interesting aspect of the codeformer model is its ability to handle a wide range of face degradations, from low-quality scans to AI-generated artifacts. You can try experimenting with different types of input images, adjusting the fidelity parameter to see the impact on the restored results. Additionally, the face inpainting and colorization capabilities can be explored on cropped and aligned face images, opening up creative possibilities for photo editing and 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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