# Guiding Instruction-based Image Editing via Multimodal Large Language Models

**Authors:** Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, Zhe Gan  
**arXiv:** [2309.17102](https://arxiv.org/abs/2309.17102)  
**Date:** 2023-09-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.17102v1/

## Key points

- Multimodal large language models (MLLMs) can enhance instruction-based image editing
- MLLMs allow for precise and flexible manipulation of images through natural commands
- Current methods struggle with capturing and following brief human instructions
- MLLMs have shown promising capabilities in cross-modal understanding and generating visual-aware responses
- The authors propose a method called MLLM-Guided Image Editing (MGIE)
- MGIE utilizes MLLMs to derive expressive instructions and provide explicit guidance for image editing
- The editing model is trained end-to-end, capturing both visual imagination and manipulation
- MGIE is evaluated on various aspects of image editing, including Photoshop-style modification, global photo optimization, and local editing
- Expressive instructions are crucial for instruction-based image editing
- MGIE significantly improves automatic metrics and receives positive evaluations from human evaluators
- MGIE maintains competitive inference efficiency

## Summary

The paper titled "Guiding Instruction-based Image Editing via Multimodal Large Language Models" explores the use of multimodal large language models (MLLMs) to enhance instruction-based image editing. This method allows for more precise and flexible manipulation of images through natural commands, without the need for complex descriptions or regional masks. However, current methods struggle with capturing and following brief human instructions. MLLMs have shown promising capabilities in cross-modal understanding and generating visual-aware responses through language models. In this study, the authors propose a method called MLLM-Guided Image Editing (MGIE), which utilizes MLLMs to derive expressive instructions and provide explicit guidance for image editing. The editing model is trained end-to-end, capturing both visual imagination and manipulation. The authors evaluate MGIE on various aspects of image editing, including Photoshop-style modification, global photo optimization, and local editing. Extensive experimental results demonstrate that expressive instructions are crucial for instruction-based image editing. The proposed MGIE approach significantly improves automatic metrics and receives positive evaluations from human evaluators while maintaining competitive inference efficiency. Overall, this paper highlights the potential of using MLLMs to enhance instruction-based image editing by enabling more accurate interpretation of human instructions and providing effective guidance for image manipulation tasks.