Stable Diffusion V2 Inpainting


AI model preview image
The stable-diffusion-v2-inpainting model is an image-to-image deep learning model that is used for inpainting, which refers to the process of filling in missing or corrupted parts of an image. This model utilizes stable diffusion, a technique that allows for high-quality image completion by iteratively filling in missing pixels. This model is an updated version of the stable-diffusion-inpainting model and incorporates improvements in stability and visual quality.

Use cases

The stable-diffusion-v2-inpainting model has numerous potential use cases in various industries. In the field of image editing and restoration, this model can be utilized to effectively remove unwanted objects or blemishes from photographs, resulting in a seamless and visually appealing final image. It can also be employed in the film and entertainment industry to restore or enhance old and damaged footage, allowing for the preservation and remastering of valuable visual content. Additionally, in the realm of virtual reality and computer graphics, this model can be used to generate realistic and immersive environments by filling in missing or incomplete details in 3D models or virtual scenes. Moreover, this model can serve as a powerful tool in forensic analysis and investigation, enabling the reconstruction of degraded or partially obscured images to aid in criminal investigations or evidence gathering. In terms of potential products or practical uses, this AI model could inspire the development of user-friendly software applications or plugins for popular image editing software, allowing everyday users to effortlessly remove or replace unwanted elements in their photographs with professional-level results. It could also be integrated into various image processing pipelines in industries like advertising, where the need for seamless image manipulation is paramount. Overall, the stable-diffusion-v2-inpainting model has the potential to revolutionize image editing, restoration, and manipulation processes, unlocking new possibilities for professionals and enthusiasts alike.



Cost per run
Avg run time
Nvidia A100 (40GB) GPU

Creator Models

Pix2pix Zero$?4,206
Night Enhancement$0.0104520,721
Mindall E$?1,645
Compositional Vsual Generation With Composable Diffusion Models Pytorch$0.01155774

Similar Models

Try it!

You can use this area to play around with demo applications that incorporate the Stable Diffusion V2 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 NameStable Diffusion V2 Inpainting
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.092
Prediction HardwareNvidia A100 (40GB) GPU
Average Completion Time40 seconds