aura-sr
Maintainer: zsxkib
5
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
aura-sr is a GAN-based super-resolution model designed to upscale real-world images. It is based on the GigaGAN approach and can produce impressive results for certain types of images. The model is developed by zsxkib and is available through the Replicate platform. Similar models like SeeSR, ArbSR, ESRGAN, and Real-ESRGAN also aim to improve image super-resolution in various ways.
Model inputs and outputs
The aura-sr model takes an input image file and a scale factor as its inputs. The scale factor determines how much the image will be upscaled, with options for 2, 4, 8, 16, or 32 times the original size. The model outputs a higher-resolution version of the input image.
Inputs
- image: The input image file to be upscaled.
- scale_factor: The factor by which to upscale the image (2, 4, 8, 16, or 32).
- max_batch_size: Controls the number of image tiles processed simultaneously. Higher values may increase speed but require more GPU memory.
Outputs
- Output: The upscaled image file.
Capabilities
aura-sr is particularly effective at upscaling PNG, lossless WebP, and high-quality JPEG XL images. It can handle different sized jobs and work quickly, making it a useful tool for tasks that require enlarging images while preserving quality.
What can I use it for?
The aura-sr model can be used to upscale AI-generated images or high-quality photographs, making them larger and clearer without losing important details. This can be useful for a variety of applications, such as creating larger promotional materials, improving image quality for websites or social media, or enhancing the visual impact of visualizations and data presentations.
Things to try
While aura-sr is a powerful tool, it does have some limitations. It works best with certain image formats and may not perform well on heavily compressed or low-quality images. Experimenting with different input images and scale factors can help you find the optimal use cases for this model.
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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