# Diffusion Self-Guidance for Controllable Image Generation

**Authors:** Dave Epstein, Allan Jabri, Ben Poole, Alexei A. Efros, Aleksander Holynski  
**arXiv:** [2306.00986](https://arxiv.org/abs/2306.00986)  
**Date:** 2023-06-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.00986v1/

## Key points

- Paper introduces a method called self-guidance for controllable image generation
- Self-guidance enhances control over generated images by guiding internal representations of diffusion models
- Large-scale generative models can produce high-quality images from text descriptions, but conveying certain aspects of an image through text alone is challenging
- Self-guidance extracts properties like object shape, location, and appearance from internal representations to steer the sampling process
- Self-guidance operates similarly to classifier guidance but uses signals present in the pretrained model itself, eliminating the need for additional models or training
- Various challenging image manipulations can be performed using self-guidance, including modifying object position or size, merging object appearances from different images with layouts from others, and combining objects from multiple images into one
- Self-guidance can also be employed to edit real images
- Limitations of self-guidance include unwanted leakage of object position when setting high guidance weights for appearance terms and entanglement of objects in attention space
- Paper provides results and an interactive demo on their project page at https://dave.ml/selfguidance/
- Approach presents a novel way to enhance control over generated images using self-guidance and leveraging internal representations of diffusion models
- Properties like object shape and appearance can be extracted and manipulated for complex image edits
- Authors provide evidence of effectiveness through various examples and offer an interactive demo for further exploration.

## Summary

The paper titled "Diffusion Self-Guidance for Controllable Image Generation" introduces a method called self-guidance that enhances the control over generated images by guiding the internal representations of diffusion models. While large-scale generative models can produce high-quality images from text descriptions, conveying certain aspects of an image through text alone is challenging or even impossible. The authors demonstrate that properties like object shape, location, and appearance can be extracted from these representations and used to steer the sampling process. Self-guidance operates similarly to classifier guidance but utilizes signals present in the pretrained model itself, eliminating the need for additional models or training. By composing a simple set of properties, the authors showcase how various challenging image manipulations can be performed. These include modifying object position or size, merging object appearances from different images with layouts from others, and combining objects from multiple images into one. The paper also demonstrates that self-guidance can be employed to edit real images. The authors provide results and an interactive demo on their project page at https://dave.ml/selfguidance/. Additionally, they discuss some limitations of self-guidance such as unwanted leakage of object position when setting high guidance weights for appearance terms and entanglement of objects in attention space. In conclusion, this paper presents a novel approach to enhancing control over generated images using self-guidance. By leveraging internal representations of diffusion models, properties like object shape and appearance can be extracted and manipulated to perform complex image edits. The authors provide evidence of the effectiveness of their method through various examples and offer an interactive demo for further exploration.