ControlNet has a wide range of potential use cases in various industries. In the field of creative design and art, this model can be leveraged to refine and polish images for graphic design, advertising, and digital art. It can also be applied in the entertainment industry to create special effects in movies, television shows, and video games, allowing for precise modifications without sacrificing the original artistic vision. ControlNet can also be used in the field of fashion and product design to simulate and visualize different color schemes or variations of a design, enabling designers to iterate quickly and efficiently. Additionally, this model can be utilized in scientific research for image analysis and enhancement tasks, such as medical imaging or analyzing satellite imagery. Overall, ControlNet provides a powerful tool for image modification that can be harnessed in a diverse range of applications. Potential products or practical uses of ControlNet could include image editing software or plugins that leverage this model to offer a more sophisticated and controllable image manipulation experience, as well as automated image enhancement tools for various industries.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Stable Diffusion Depth2img||$0.0322||50,528|
You can use this area to play around with demo applications that incorporate the Controlnet 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.
Modify images with a prompt while preserving their structure
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|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||$0.0598|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||26 seconds|