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

• URL: http://arxiv.org/abs/2211.09794v1
• Date: Thu, 17 Nov 2022 18:58:14 GMT
• Title: Null-text Inversion for Editing Real Images using Guided Diffusion Models
• Authors: Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, Daniel Cohen-Or
• Abstract summary: We introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image. Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and prompt editing.
• Score: 44.27570654402436
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing. To edit a real image using these state-of-the-art tools, one must first invert the image with a meaningful text prompt into the pretrained model's domain. In this paper, we introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image. Our proposed inversion consists of two novel key components: (i) Pivotal inversion for diffusion models. While current methods aim at mapping random noise samples to a single input image, we use a single pivotal noise vector for each timestamp and optimize around it. We demonstrate that a direct inversion is inadequate on its own, but does provide a good anchor for our optimization. (ii) NULL-text optimization, where we only modify the unconditional textual embedding that is used for classifier-free guidance, rather than the input text embedding. This allows for keeping both the model weights and the conditional embedding intact and hence enables applying prompt-based editing while avoiding the cumbersome tuning of the model's weights. Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and prompt editing, showing high-fidelity editing of real images.

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