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Recent large-scale text-guided diffusion models provide powerfulimage-generation capabilities. Currently, a significant effort is given toenable the modification of these images using text only as means to offerintuitive and versatile editing. However, editing proves to be difficult forthese generative models due to the inherent nature of editing techniques, whichinvolves preserving certain content from the original image. Conversely, intext-based models, even minor modifications to the text prompt frequentlyresult in an entirely distinct result, making attaining one-shot generationthat accurately corresponds to the users intent exceedingly challenging. Inaddition, to edit a real image using these state-of-the-art tools, one mustfirst invert the image into the pre-trained models domain - adding anotherfactor affecting the edit quality, as well as latency. In this exploratoryreport, we propose LEDITS - a combined lightweight approach for real-imageediting, incorporating the Edit Friendly DDPM inversion technique with SemanticGuidance, thus extending Semantic Guidance to real image editing, whileharnessing the editing capabilities of DDPM inversion as well. This approachachieves versatile edits, both subtle and extensive as well as alterations incomposition and style, while requiring no optimization nor extensions to thearchitecture.

LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance