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Denoising diffusion models have enabled high-quality image generation andediting. We present a method to localize the desired edit region implicit in atext instruction. We leverage InstructPix2Pix (IP2P) and identify thediscrepancy between IP2P predictions with and without the instruction. Thisdiscrepancy is referred to as the relevance map. The relevance map conveys theimportance of changing each pixel to achieve the edits, and is used to to guidethe modifications. This guidance ensures that the irrelevant pixels remainunchanged. Relevance maps are further used to enhance the quality oftext-guided editing of 3D scenes in the form of neural radiance fields. A fieldis trained on relevance maps of training views, denoted as the relevance field,defining the 3D region within which modifications should be made. We performiterative updates on the training views guided by rendered relevance maps fromthe relevance field. Our method achieves state-of-the-art performance on bothimage and NeRF editing tasks. Project page:https://ashmrz.github.io/WatchYourSteps/

Watch Your Steps: Local Image and Scene Editing by Text Instructions