vqfr

Maintainer: cjwbw

Total Score

137

Last updated 6/13/2024
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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

vqfr is a blind face restoration model that incorporates a Vector-Quantized (VQ) dictionary and a Parallel Decoder to produce realistic facial details while maintaining comparable fidelity. Compared to previous models like GFPGAN and the creator's own DACLIP-UIR and SUPIR, vqfr aims to investigate the potential and limitations of VQ dictionaries for facial restoration.

Model inputs and outputs

vqfr takes an input image and can restore both the full image or just the face region. The model supports restoring non-aligned faces as well as aligned faces.

Inputs

  • Image: The input image to be restored, either a full image or a cropped face.
  • Aligned: A boolean flag indicating whether the input image is an aligned face.

Outputs

  • Restored Image: The output image with the face region restored to a higher quality.

Capabilities

vqfr is capable of blind face restoration, meaning it can restore low-quality or degraded facial images without any additional information. The model is able to produce realistic facial details while maintaining comparable fidelity to the input.

What can I use it for?

vqfr can be useful for a variety of applications that involve restoring low-quality facial images, such as old photos, AI-generated faces, or images captured in less than ideal conditions. The model's ability to restore both the face region and the entire image makes it suitable for use cases like photo enhancement, digital archiving, and creative applications.

Things to try

With vqfr, you can experiment with restoring a variety of facial images, from old photographs to AI-generated portraits. The model's support for non-aligned faces and ability to enhance the background regions (using Real-ESRGAN) opens up interesting possibilities for creative projects and image restoration tasks.



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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