GFPGAN has several potential use cases for technical users. One possible application is in the field of historical preservation and restoration. Museums, archives, and historical societies can use the model to enhance the quality of old photographs, bringing them back to life with more clarity and detail. The algorithm can also be beneficial for AI-generated faces, where the generated images may lack realistic features or details. Researchers and developers working on AI-generated faces can use GFPGAN to refine and improve the quality of their generated images, making them more lifelike and convincing. Additionally, the model can be integrated into photo editing software or mobile applications, allowing users to enhance the appearance of their photos by restoring old images or enhancing the quality of AI-generated portraits.
- Cost per run
- Avg run time
- Nvidia T4 GPU
You can use this area to play around with demo applications that incorporate the Gfpgan 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.
Practical face restoration algorithm for *old photos* or *AI-generated face...Read more »
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
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||$0.0033|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||6 seconds|