# Imagic: Text-Based Real Image Editing with Diffusion Models

**Authors:** Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, Michal Irani  
**arXiv:** [2210.09276](https://arxiv.org/abs/2210.09276)  
**Date:** 2022-10-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.09276v3/

## Key points

- The field of text-conditioned image editing has gained significant attention in recent years
- Existing methods are often limited to specific types of edits or only work with synthetically generated images
- Many approaches require multiple input images of the same object
- The paper introduces a novel method called "Imagic" that allows for complex semantic edits on a single real image
- Imagic only requires a single input image and a target text describing the desired edit
- The technique operates on real images without the need for additional inputs such as image masks or additional views of the object
- Imagic leverages a pre-trained text-to-image diffusion model for this task
- The key contribution is the ability to apply non-rigid text-guided semantic edits to high-resolution natural images provided by the user
- Imagic can change the posture and composition of objects within an image while preserving its original characteristics
- It produces a text embedding that aligns with both the input image and the target text, and fine-tunes the diffusion model to capture the specific appearance of the image being edited
- The authors demonstrate the quality and versatility of their method through numerous inputs from various domains, showcasing a wide range of high-quality complex semantic image edits
- Imagic surpasses previous limitations in terms of edit types, reliance on synthetic images, and requirements for multiple input images
- It offers a unified framework for making sophisticated semantic edits on single real images using just one input image and target text description.

## Summary

The field of <kw>text-conditioned image editing</kw> has gained significant attention in recent years. However, existing methods are often limited to specific types of edits or only work with synthetically generated images. Additionally, many approaches require multiple input images of the same object. In this paper titled "Imagic: Text-Based Real Image Editing with Diffusion Models," the authors introduce a novel method that allows for complex semantic edits on a single real image. Unlike previous work, the proposed method only requires a single input image and a target text describing the desired edit. The technique operates on real images without the need for additional inputs such as image masks or additional views of the object. The authors refer to their method as "Imagic" and leverage a pre-trained text-to-image diffusion model for this task. The key contribution of this research is the ability to apply <kw>non-rigid text-guided semantic edits</kw> to high-resolution natural images provided by the user. For example, using Imagic, it becomes possible to change the posture and composition of objects within an image while preserving its original characteristics. This means that a standing dog can be made to sit down or jump, and a bird can have its wings spread, all within a single input image. To achieve this, Imagic produces a text embedding that aligns with both the input image and the target text. It also fine-tunes the diffusion model to capture the specific appearance of the image being edited. The authors demonstrate the quality and versatility of their method through numerous inputs from various domains, showcasing a wide range of high-quality complex semantic image edits. Overall, this paper presents an innovative approach to <kw>text-based real image editing</kw> that surpasses previous limitations in terms of edit types, reliance on synthetic images, and requirements for multiple input images. Imagic offers a <kw>unified framework</kw> for making sophisticated semantic edits on single real images using just one input image and target text description.