While diffusion models show promising results in image editing given a target prompt, achieving both prompt fidelity and background preservation remains difficult. Recent works have introduced score distillation techniques that leverage the rich generative prior of text-to-image diffusion models to solve this task without additional fine-tuning. However, these methods often struggle with tasks such as object insertion. Our investigation of these failures reveals significant variations in gradient magnitude and spatial distribution, making hyperparameter tuning highly input-specific or unsuccessful. To address this, we propose two simple yet effective modifications: attention-based spatial regularization and gradient filtering-normalization, both aimed at reducing these variations during gradient updates. Experimental results show our method outperforms state-of-the-art score distillation techniques in prompt fidelity, improving successful edits while preserving the background. Users also preferred our method over state-of-the-art techniques across three metrics, and by 58-64% overall.
View on arXiv@article{chinchuthakun2025_2503.11054,
title={ LUSD: Localized Update Score Distillation for Text-Guided Image Editing },
author={ Worameth Chinchuthakun and Tossaporn Saengja and Nontawat Tritrong and Pitchaporn Rewatbowornwong and Pramook Khungurn and Supasorn Suwajanakorn },
journal={arXiv preprint arXiv:2503.11054},
year={ 2025 }
}