The controlnet-depth2img model has several potential use cases for technical audiences. For image editing, the model can be used to enhance or modify images based on their depth maps, allowing for precise adjustments in different areas of the image. Depth-based inpainting is another application, where the model can fill in missing areas in an image based on the surrounding depth information, resulting in seamless and realistic inpainting results. Additionally, the model can generate 3D-like effects in images by manipulating the depth maps and generating corresponding modified images. This can be useful for creating virtual reality or augmented reality experiences, or for adding depth and dimension to static images. With its versatility and ability to handle a wide range of image manipulation tasks, the controlnet-depth2img model has the potential to give rise to innovative products and practical uses in various industries such as photography, graphic design, and computer-generated imagery.
- 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 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.
|Model Name||Controlnet Depth2img|
Modify images using depth maps
|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||$0.0322|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||14 seconds|