Realisitic Vision V3 Inpainting
The Realistic Vision V3.0 Inpainting model has a wide range of potential use cases for technical users. One application could be in restoring old or damaged photographs, where the model can fill in missing or damaged parts of the image with realistic and accurate content, preserving the original aesthetic. It can also be applied in the field of image editing, enabling users to seamlessly remove unwanted objects or blemishes from images by intelligently inpainting the affected areas. Another use case could be in the creation of artistic or creative image edits, where the model can generate visually appealing in-painting results to enhance or transform an image. Overall, the Realistic Vision V3.0 Inpainting model offers powerful capabilities for image inpainting that can be leveraged by various industries and applications. Potential products or practical uses could include automated photo restoration software, smart image editing tools, or AI-powered creative apps that provide users with advanced in-painting capabilities.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Realisitic Vision V3 Image To Image||$0.0138||27,846|
|Realistic Vision V3||$0.0184||91,903|
You can use this area to play around with demo applications that incorporate the Realisitic Vision V3 Inpainting 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||Realisitic Vision V3 Inpainting|
Realistic Vision V3.0 Inpainting
|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||$0.0253|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||11 seconds|