ControlNet Pose is an AI tool that allows users to modify images with humans using pose detection. It is built on the ControlNet neural network structure, which enables the control of pretrained large diffusion models to support additional input conditions beyond prompts. In addition to a text input, ControlNet Pose utilizes a pose map of humans in an input image to generate an output image. This tool is particularly useful for tasks that require modifying images based on human poses.
The predictions of ControlNet Pose run on Nvidia A100 (40GB) GPU hardware, with an average completion time of approximately 17 seconds. However, the predict time may vary depending on the inputs provided. The model was developed by Lyumin Zhang.
ControlNet Pose is part of a series of ControlNets that can be used to modify the output of Stable Diffusion. Other ControlNets include options for generating images from drawings, generating humans based on input images, and preserving general qualities about an input image. These ControlNets utilize different input processing methods, allowing users to choose the most suitable option for their specific application.
To use ControlNet Pose, users need to input an image and prompt the model to generate an image, similar to how Stable Diffusion works. The model will detect the pose of humans in the input image and generate an output image accordingly. The original model and code for ControlNet Pose can be found on GitHub.
As for pricing, Replicate, the platform hosting ControlNet Pose, offers a free tier for usage. However, once users exceed the free tier, they will be charged based on the amount of time they use the tool. The price per second varies depending on the hardware on which the model is run. Replicate charges users for the time used when a prediction completes successfully, with a minimum billable time of 1 second. Users can find their current usage and billing information on their account page.
To get started with ControlNet Pose, users are required to sign up and enter their credit card information. However, there is no charge for signing up, and users will only be billed for the predictions they run, based on the time used.
