Controlnet Inpaint Test
The controlnet-inpaint-test model has several potential use cases in various industries. In the field of photography, it can be used to restore old or damaged photos by filling in missing or deteriorated parts. In the fashion industry, the model can be applied to clothing images, allowing for the removal of unwanted elements or the addition of new design elements. In the field of architecture, the model can assist in the removal of unwanted objects from images of buildings or landscapes, resulting in cleaner and more visually appealing visualizations. Additionally, the model can be used in the field of product design to generate realistic prototypes by digitally filling in missing areas. Furthermore, the model can be integrated into image editing software, providing users with a powerful tool to enhance and manipulate images. Overall, the controlnet-inpaint-test model has the potential to be incorporated into various products and services, making image inpainting more accessible and efficient for a wide range of applications.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Facial Landmark Detection||$0.0064||372|
You can use this area to play around with demo applications that incorporate the Controlnet Inpaint Test 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.
|Model Name||Controlnet Inpaint Test|
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||No Github link provided|
|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||$0.023|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||10 seconds|