![]() What’s the difference between using Canny edge detection and Openpose? Canny edge detector extracts the edges of the subject and background alike. Images are generated based on these two conditionings. It is then fed to Stable Diffusion as an extra conditioning together with the text prompt. Keypoints are extracted from the input image using OpenPose, and saved as a control map containing the positions of keypoints. Input image annotated with human pose detection using Openpose.īelow is the ControlNet workflow using OpenPose. Openpose is a fast keypoint detection model that can extract human poses like positions of hands, legs, and head. Human pose detectionĪs you may have suspected, edge detection is not the only way an image could be preprocessed. The process of extracting specific information (edges in this case) from the input image is called annotation (in the research article) or preprocessing (in the ControlNet extension). Stable Diffusion ControlNet with Canny edge conditioning. ![]() The detected edges are saved as a control map and then fed into the ControlNet model as extra conditioning, in addition to the text prompt. In the workflow illustrated below, ControlNet takes an additional input image and detects its outlines using the Canny edge detector. Let me show you two ControlNet examples: (1) edge detection and (2) human pose detection. It uses text prompts as the conditioning to steer image generation. The most basic form of Stable Diffusion model is text-to-image. Difference between Stable Diffusion depth model and ControlNetĬontrolNet is a modified Stable Diffusion model.ControlNet model comparison for copying poses.Install ControlNet Models (Windows/Mac).Install ControlNet extension (Windows/Mac).Install ControlNet on Windows PC or Mac.
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