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Some Upscalers


AI model preview image
The model consists of several image upscaling algorithms that aim to improve image quality by increasing the resolution using an approach called ESRGAN. These algorithms specifically focus on upscaling images by a factor of 4. The ESRGAN method enhances the visual details of the upscaled images and produces more realistic results compared to traditional upscaling techniques.

Use cases

For a technical audience, this AI model has various potential use cases. It can be employed in applications where high-quality images are crucial, such as photography, graphic design, and video editing. By utilizing the image upscaling algorithms, this model can enhance the resolution of low-quality images and make them suitable for printing or display on high-resolution screens. Additionally, it can be integrated into image editing software or online platforms to provide users with an automatic upscaling feature, saving time and effort. Furthermore, industries like satellite imaging, medical imaging, and surveillance may benefit from this model by improving the clarity and details of their image data. As for potential products, this model could be the basis for a standalone image upscaling software or an API that developers can integrate into their applications. It could also be incorporated into digital cameras or smartphones to provide real-time image upscaling capabilities.



Cost per run
Avg run time
Nvidia T4 GPU

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Try it!

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Currently, there are no demos available for this model.


Summary of this model and related resources.

Model NameSome Upscalers
Some 4x esrgan upscalers
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.0077
Prediction HardwareNvidia T4 GPU
Average Completion Time14 seconds