The combination of language processing and image processing keeps attracting increased interest given recent impressive advances that leverage the combined strengths of both domains of research. Among these advances, the task of editing an image on the basis solely of a natural language instruction stands out as a most challenging endeavour. While recent approaches for this task resort to training or fine-tuning, this paper explores a novel, unsupervised method that permits instruction-guided image editing on the fly. This approach is organized along three steps that resort to image captioning and DDIM inversion, followed by obtaining the edit direction embedding, followed by generating the edited image. While dispensing with any form of training, our approach is shown to be effective and competitive, outperforming recent, state-of-the-art models for this task on the MAGICBRUSH dataset.