ControlNet Openpose

Input

Prompt

Steps

Scheduler

Seed

Input Image

Uploaded

Output

output


ControlNet-Openpose

ControlNet is a neural network architecture that can be used to control pretrained large diffusion models to support additional input conditions. The purpose of ControlNet-openpose is to enable conditional input of human pose keypoints to control large diffusion models and facilitate related applications.

Weights

These weights were trained on stable diffusion 1.5.

Features

  • Accurate pose estimation: ControlNet-Openpose accurately detects keypoints and estimates body joints, providing precise pose estimation even in challenging conditions.
  • Real-time performance: Thanks to its efficient architecture and voltaML optimizations, ControlNet-Openpose delivers real-time performance, making it suitable for a wide range of applications, including interactive systems and live video analysis.
  • Robust to occlusions: ControlNet-Openpose is designed to handle partial occlusions, ensuring that it maintains high accuracy even when some body parts are not visible.

Applications

ControlNet-Openpose can be utilized in various applications, such as:

  • Human-computer interaction (HCI)
  • Virtual and augmented reality
  • Animation and game development
  • Fitness and sports analytics

Getting Started

For more detailed instructions, refer to the API documentation and resources available on Github.

Github

https://github.com/lllyasviel/ControlNet

License

Apache License 2.0