This is a simplified guide to an AI model called Codeformer maintained by Lucataco. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
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 re…