Serverless Pricing

Buy credits that can be used anywhere on Segmind

$ 0.0015

Cost per second

Dedicated Cloud Pricing

For enterprise costs and dedicated endpoints

$ 0.0007 - $ 0.0031

Cost per second

ControlNet Depth

ControlNet Depth is a neural network that can be used to control the output of Stable Diffusion models with depth information. This allows you to specify specific features that you want to include in the output image, such as the overall structure of the image, the pose of the subject, or the style of the image, as well as the depth information.

To use ControlNet Depth, you can follow these steps:

  1. Go to the Segmind website: https://www.segmind.com/ and sign up for a free account.

  2. Click on the "Models" tab and select "ControlNet Depth".

  3. Click on the "Try it out" button and upload an image that you want to control.

  4. Click on the "Generate" button to generate the controlled image.

ControlNet Depth is a powerful tool that can be used for various purposes. It is still under development, but it has the potential to revolutionize the way we interact with images. Reach out to us to learn more about how we can help you with customized solutions, large-scale cost-effective deployment, and other use cases.

Applications/Use Cases

  1. Creating images with specific features: ControlNet Depth can be used to create images with specific features, such as a particular pose, a specific style, or a specific object, as well as depth information. This can be useful for creating images for creative projects or for research purposes.

  2. Improving the quality of images: ControlNet Depth can be used to improve the quality of images by removing noise or by adding detail. This can be useful for restoring damaged images or creating more realistic ones.

  3. Controlling the output of Stable Diffusion models with depth information: ControlNet Depth can be used to control the output of Stable Diffusion models to include specific features, as well as depth information. This can be useful for creating images with a particular style or for creating images consistent with a particular dataset.