A review of instruction-guided image editing

This is the authors' abstract. We don't add key points for this paper.

The rapid advancement of artificial intelligence (AI) systems such as large language models (LLMs) and multimodal learning frameworks has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruction-guided editing have enabled intuitive interaction with visual content, using natural language as a bridge between user intent and complex editing operations. This survey reviews how AI-implemented instruction-guided image editing models – including approaches rooted in generative adversarial networks and diffusion models – empower users to achieve precise visual modifications without deep technical knowledge. By synthesizing over 100 publications, we examine multimodal integration for fine-grained content control, compare existing literature, and highlight how these AI applications support creative visual storytelling, design workflows, and multimedia production. We also identify key challenges to stimulate further research. Interested readers are encouraged to access our repository at https://github.com/tamlhp/awesome-instructional-editing .