gfpgan
Maintainer: tencentarc
72.6K
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
Model overview
gfpgan is a practical face restoration algorithm developed by the Tencent ARC team. It leverages the rich and diverse priors encapsulated in a pre-trained face GAN (such as StyleGAN2) to perform blind face restoration on old photos or AI-generated faces. This approach contrasts with similar models like Real-ESRGAN, which focuses on general image restoration, or PyTorch-AnimeGAN, which specializes in anime-style photo animation.
Model inputs and outputs
gfpgan takes an input image and rescales it by a specified factor, typically 2x. The model can handle a variety of face images, from low-quality old photos to high-quality AI-generated faces.
Inputs
- Img: The input image to be restored
- Scale: The factor by which to rescale the output image (default is 2)
- Version: The
gfpganmodel version to use (v1.3 for better quality, v1.4 for more details and better identity)
Outputs
- Output: The restored face image
Capabilities
gfpgan can effectively restore a wide range of face images, from old, low-quality photos to high-quality AI-generated faces. It is able to recover fine details, fix blemishes, and enhance the overall appearance of the face while preserving the original identity.
What can I use it for?
You can use gfpgan to restore old family photos, enhance AI-generated portraits, or breathe new life into low-quality images of faces. The model's capabilities make it a valuable tool for photographers, digital artists, and anyone looking to improve the quality of their facial images. Additionally, the maintainer tencentarc offers an online demo on Replicate, allowing you to try the model without setting up the local environment.
Things to try
Experiment with different input images, varying the scale and version parameters, to see how gfpgan can transform low-quality or damaged face images into high-quality, detailed portraits. You can also try combining gfpgan with other models like Real-ESRGAN to enhance the background and non-facial regions of the image.
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