CVPR2023

Null-text Inversion for Editing Real Images using Guided Diffusion Models

Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, Daniel Cohen-Or

Abstract

Figure 1. Null-text inversion for real image editing. Our method takes as input a real image (leftmost column) and an associated caption. The image is inverted with a DDIM diffusion model to yield a diffusion trajectory (second column to the left). Once inverted, we use the initial trajectory as a pivot for null-text optimization that accurately reconstructs the input image (third column to the left). Then, we can edit the inverted image by modifying only the input caption using the editing technique of Prompt-to-Prompt [18] .