image  ControlNet ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as Stable Diffusion v1.5. Using ControlNet models in combination with text-to-image models offers diverse options for more explicit control over how to generate an image. With ControlNet, you add an additional conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple keypoints that are connected into a skeleton) as a conditioning input, the model generates an image that follows the pose of the image. Check out the more in-depth ControlNet guide to learn more about other conditioning inputs and how to use them. In this example, let’s condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations:   Copied from diffusers import ControlNetModel, AutoPipelineForText2Image