The VQFR model offers several potential use cases for image editing and enhancement. Firstly, it can be used in the field of forensic science to reconstruct and enhance faces from low-quality or degraded images, aiding in the identification of suspects or missing persons. Additionally, it could be applied in the domain of digital image restoration, supporting the preservation and restoration of historical photographs or damaged images. Furthermore, the model can be utilized in the development of facial recognition systems, by enhancing low-resolution images before matching them against a database. Overall, the VQFR model has great potential in a wide range of practical applications, enabling users to restore and improve facial images even when the original images are unavailable.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Compositional Vsual Generation With Composable Diffusion Models Pytorch||$0.01155||774|
You can use this area to play around with demo applications that incorporate the Vqfr 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.
Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decode...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||$0.0011|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||2 seconds|