We suggest a new approach that enables spatial editing and manipulation of images using Generative Adversarial Networks (GANs). Though many tasks have been solved utilizing the powerful abilities of GANs, this is the first time that a spatial control is suggested. This ability is possible thanks to a test-time spatial normalization that uses the trained model as is and does not requires any fine tuning. Therefore our method is significantly fast and does not required further training. We demonstrate the new approach for the task of class hybridization and saliency manipulation.