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

Recent large-scale text-guided diffusion models provide powerful image generation capabilities. 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.

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• 1 Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing tools.
• 2 To edit a real image using these advanced tools, one must first invert the image with a meaningful text prompt into the pretrained model's domain.
• 3 In this paper, we introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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