LUSD: Localized Update Score Distillation for Text-Guided Image Editing

• URL: http://arxiv.org/abs/2503.11054v1
• Date: Fri, 14 Mar 2025 03:45:29 GMT
• Title: LUSD: Localized Update Score Distillation for Text-Guided Image Editing
• Authors: Worameth Chinchuthakun, Tossaporn Saengja, Nontawat Tritrong, Pitchaporn Rewatbowornwong, Pramook Khungurn, Supasorn Suwajanakorn,
• Abstract summary: Recent works have introduced score distillation techniques that leverage the rich generative prior of text-to-image diffusion models.<n>We propose two simple yet effective modifications: attention-based spatial regularization and gradient filtering-normalization.<n> Experimental results show our method outperforms state-of-the-art score distillation techniques in prompt fidelity, improving successful edits while preserving the background.
• Score: 11.293199854940772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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