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