Stable Diffusion Depth2img
The stable-diffusion-depth2img model can be applied to a range of use cases in the field of computer vision. One use case could be in the field of graphic design where designers need to create variations of an image while preserving the overall composition and structure. Another possible use case could be in the domain of virtual reality and augmented reality, where realistic and diverse images are required to enhance the immersive experience for users. Additionally, the model could be valuable in the context of image synthesis for training data augmentation, where diverse images are needed to improve the performance and generalization of deep learning models. Based on this model, potential practical applications could include an image editing tool that allows users to effortlessly generate variations of their input images while retaining the desired visual characteristics. Another application could be an artistic rendering tool that transforms existing images into unique and visually appealing artworks while retaining the essence of the original composition.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
You can use this area to play around with demo applications that incorporate the Stable Diffusion Depth2img 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.
|Stable Diffusion Depth2img
Create variations of an image while preserving shape and depth
|View on Replicate
|View on Replicate
|View on Github
|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
|Nvidia A100 (40GB) GPU
|Average Completion Time