Real-ESRGAN
Maintainer: ai-forever
98
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Real-ESRGAN is an upgraded version of the ESRGAN model, designed to enhance real-world images by improving details and removing artifacts. It was trained on a custom dataset by the maintainer ai-forever, and shows better results on faces compared to the original ESRGAN model. This model is easier to integrate into projects than the original implementation.
Similar models include real-esrgan by nightmareai, gfpgan by tencentarc, and realesrgan by lqhl.
Model inputs and outputs
Real-ESRGAN takes low-resolution real-world images as input and outputs high-resolution, enhanced versions of those images. The model is capable of 4x upscaling, and can remove common artifacts and improve details in the process.
Inputs
- Low-resolution real-world images
Outputs
- High-resolution, enhanced versions of the input images with improved details and removed artifacts
Capabilities
Real-ESRGAN is designed to enhance the quality of real-world images, particularly those with faces. It can remove common issues like blurriness, JPEG artifacts, and missing details, while preserving the overall integrity of the image.
What can I use it for?
You can use Real-ESRGAN to improve the visual quality of images for a variety of applications, such as social media, photography, and content creation. The model's ability to upscale and enhance images makes it a valuable tool for tasks like restoring old photos, improving the quality of AI-generated images, and enhancing the visuals in your projects.
Things to try
One interesting thing to try with Real-ESRGAN is using it in combination with face restoration models like gfpgan to achieve even better results on images with human faces. You could also experiment with different input resolutions and upscaling factors to find the optimal settings for your specific use case.
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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