ControlNet: Guide Image generation with precision.

ControlNet is a groundbreaking technology designed to enhance control over image generation processes.

What is ControlNet?

ControlNet is an advanced neural network model designed to enhance Stable Diffusion models. It can be integrated with any Stable Diffusion model to provide more precise control over image generation. Stable Diffusion models primarily operate on a text-to-image basis, where text prompts guide the generation of images to match the given descriptions. ControlNet introduces an additional layer of conditioning alongside the text prompts, allowing for more refined control. ControlNet's additional conditioning can take various forms, as demonstrated by the following examples: edge detection and human pose detection.

Edge Detection Example

In the edge detection scenario, ControlNet processes an additional input image to identify its outlines using the Canny edge detector. The detected edges are saved as a control map, which is then used as an extra conditioning input to the ControlNet model alongside the text prompt. This process, referred to as annotation or preprocessing, enables the generation of images that closely follow the detected edges.

Human Pose Detection

Another method of preprocessing involves human pose detection using OpenPose, a model capable of detecting keypoints such as the positions of hands, legs, and head. In this workflow, keypoints are extracted from the input image and saved as a control map. This control map, along with the text prompt, guides the image generation process. The result is an image that adheres to the detected human pose while allowing for creative interpretation.