# SINE: SINgle Image Editing with Text-to-Image Diffusion Models

**Authors:** Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris Metaxas, Jian Ren  
**arXiv:** [2212.04489](https://arxiv.org/abs/2212.04489)  
**Date:** 2022-12-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2212.04489v1/

## Key points

- Recent works on diffusion models have shown promising results in conditioning image generation, particularly in text-guided image synthesis.
- Existing approaches that fine-tune these models with a single image often suffer from severe overfitting issues.
- The proposed SINE (SINgle Image Editing) approach leverages classifier-free guidance to address the problem of information leakage and mismatch between edited content and the original image.
- SINE allows knowledge from a model trained on a single image to be distilled into the pre-trained diffusion model, enabling content creation even with only one given image.
- A patch-based fine-tuning technique is proposed to facilitate the generation of images with arbitrary resolutions.
- Extensive experiments validate the design choices of SINE and demonstrate its editing capabilities, including style changes, content addition, and object manipulation.
- The authors provide their code implementation for research purposes.

## Summary

, , , , 

Recent works on diffusion models have shown promising results in conditioning image generation, particularly in text-guided image synthesis. This success has motivated researchers to explore the use of large-scale pre-trained diffusion models for real image editing tasks. However, existing approaches that fine-tune these models with a single image often suffer from severe overfitting issues. The leakage of information from the pre-trained diffusion models leads to a mismatch between the edited content and the original image while incorporating new features guided by language instructions. To address this problem, this paper introduces a novel model-based guidance approach called SINE (SINgle Image Editing) that leverages classifier-free guidance. The proposed method allows knowledge from a model trained on a single image to be distilled into the pre-trained diffusion model, enabling content creation even with only one given image. Additionally, the authors propose a patch-based fine-tuning technique that effectively facilitates the generation of images with arbitrary resolutions. Extensive experiments are conducted to validate the design choices of SINE and demonstrate its promising editing capabilities. These include style changes, content addition, and object manipulation. The authors also provide their code implementation for research purposes. In summary, this work presents an innovative solution for single-image editing using text-to-image diffusion models. By introducing model-based guidance and patch-based fine-tuning, SINE addresses the challenges associated with limited availability of training images and enables high-quality content creation while preserving the original image's characteristics.