codeformer

Maintainer: lucataco

Total Score

301

Last updated 5/17/2024
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Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

CodeFormer is a robust face restoration algorithm developed by researchers at Nanyang Technological University. It is designed to enhance old photos or fix issues in AI-generated faces, such as blurriness, compression artifacts, and distortions. CodeFormer uses a novel Codebook Lookup Transformer architecture to achieve high-quality face restoration, outperforming previous methods like GFPGAN. It can handle a wide range of face degradation types and produces natural-looking results.

Model inputs and outputs

CodeFormer takes in an image as input and outputs a restored, high-quality version of the face. The model supports several optional features:

Inputs

  • Image: The input image containing the face to be restored.
  • Upscale: The final upsampling scale of the image, with a default of 2.
  • Face Upsample: A boolean flag to further upsample the restored faces for high-resolution AI-created images.
  • Background Enhance: A boolean flag to enhance the background image using Real-ESRGAN.
  • Codeformer Fidelity: A number between 0 and 1 that balances the quality (lower number) and fidelity (higher number) of the output.

Outputs

  • Output: The restored, high-quality image with the face enhanced.

Capabilities

CodeFormer is capable of robustly restoring a wide range of face degradation types, including blurriness, compression artifacts, and distortions. It can handle both old photos and AI-generated faces, producing natural-looking results that preserve the subject's identity. The model's performance surpasses previous methods like GFPGAN.

What can I use it for?

CodeFormer can be a valuable tool for a variety of applications, such as:

  • Enhancing old family photos or other historical images
  • Improving the quality of AI-generated portraits or avatars
  • Restoring low-quality images or videos with faces
  • Developing applications that require high-quality face restoration, such as photo editing tools or social media platforms

Things to try

One interesting aspect of CodeFormer is its ability to balance the quality and fidelity of the output through the Codeformer Fidelity parameter. By adjusting this value, you can experiment with different levels of restoration, from preserving the original appearance to achieving a more polished, high-quality result. This allows users to customize the output to their specific needs or preferences.



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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