# InstructDiffusion: A Generalist Modeling Interface for Vision Tasks

**Authors:** Zigang Geng, Binxin Yang, Tiankai Hang, Chen Li, Shuyang Gu, Ting Zhang, Jianmin Bao, Zheng Zhang, Han Hu, Dong Chen, Baining Guo  
**arXiv:** [2309.03895](https://arxiv.org/abs/2309.03895)  
**Date:** 2023-09-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.03895v1/

## Key points

- InstructDiffusion aligns computer vision tasks with human instructions
- It transforms diverse vision tasks into an intuitive image-manipulating process
- Users can provide instructions such as encircling specific objects or applying masks to certain areas of an image
- The model is built upon the diffusion process and predicts pixels based on user instructions
- It handles various vision tasks, including segmentation, keypoint detection, editing, and enhancement
- InstructDiffusion outperforms existing methods when tested on novel datasets
- It represents a significant advancement in the field of computer vision and bridges the gap between human instructions and computer vision algorithms
- It contributes towards the development of artificial general intelligence
- InstructDiffusion has the potential to revolutionize how we interact with computer vision systems and enable more intuitive and versatile applications in various domains.

## Summary

InstructDiffusion is a groundbreaking framework that aims to align computer vision tasks with human instructions. Unlike existing approaches that rely on predefined output spaces and prior knowledge for each vision task, InstructDiffusion takes a different approach by transforming diverse vision tasks into an intuitive image-manipulating process. The framework operates in a flexible and interactive pixel space, allowing users to provide instructions such as encircling specific objects or applying masks to certain areas of an image. The model behind InstructDiffusion is built upon the diffusion process and is trained to predict pixels based on user instructions. This enables the framework to handle various vision tasks, including understanding tasks like segmentation and keypoint detection, as well as generative tasks like editing and enhancement. Moreover, InstructDiffusion showcases its ability to handle previously unseen tasks and outperforms existing methods when tested on novel datasets. By providing a generalist modeling interface for vision tasks, InstructDiffusion represents a significant advancement in the field of computer vision. It not only bridges the gap between human instructions and computer vision algorithms but also contributes towards the development of artificial general intelligence. With its innovative approach and impressive performance, InstructDiffusion has the potential to revolutionize how we interact with computer vision systems and pave the way for more intuitive and versatile applications in various domains.