This AI model has several use cases for technical users. It can be used for enhancing the quality of images by increasing their resolution, making them more suitable for printing or display on high-resolution screens. It can also improve the level of detail in images, making them clearer and more recognizable. This can be particularly useful in fields such as medical imaging or satellite imagery analysis where higher resolution can aid in accurate diagnosis or interpretation. Additionally, this model can prepare images for further analysis or processing, such as object detection or image segmentation. Overall, the stable_diffusion2_upscaling model has the potential to be integrated into various products or applications that require image super-resolution, including image editing software, medical imaging systems, or surveillance systems.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
You can use this area to play around with demo applications that incorporate the Stable_diffusion2_upscaling model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
Image super-resolution with stable-diffusion V2
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||No paper link provided|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.0253|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||11 seconds|