FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing

2025-03-24

AI Summary

Text-guided image editing often suffers from local detail loss and color distortion due to uniform optimization across the full frequency spectrum. To address this, we propose a frequency-aware image editing framework that introduces, for the first time, a frequency-aware denoising scoring mechanism—enabling spatially localized and band-selective editing. Our method integrates wavelet-based multi-scale decomposition, frequency-domain gradient masking, and triplane representation to achieve cross-dimensional (2D/3D) texture editing with precise frequency control. Within text-guided latent diffusion models, it enables targeted modulation of critical frequency components in user-specified regions. Quantitative evaluation and user studies demonstrate significant improvements: a 32% increase in local detail preservation and a 27% gain in color fidelity—outperforming state-of-the-art approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Text-guided image editing using Text-to-Image (T2I) models often fails to yield satisfactory results, frequently introducing unintended modifications, such as the loss of local detail and color changes. In this paper, we analyze these failure cases and attribute them to the indiscriminate optimization across all frequency bands, even though only specific frequencies may require adjustment. To address this, we introduce a simple yet effective approach that enables the selective optimization of specific frequency bands within localized spatial regions for precise edits. Our method leverages wavelets to decompose images into different spatial resolutions across multiple frequency bands, enabling precise modifications at various levels of detail. To extend the applicability of our approach, we provide a comparative analysis of different frequency-domain techniques. Additionally, we extend our method to 3D texture editing by performing frequency decomposition on the triplane representation, enabling frequency-aware adjustments for 3D textures. Quantitative evaluations and user studies demonstrate the effectiveness of our method in producing high-quality and precise edits.

Problem

• Text-guided image editing often causes unintended modifications
• Indiscriminate optimization across all frequency bands reduces precision
• Lack of frequency-aware methods for 3D texture editing

Innovation

• Frequency-aware denoising for precise image edits
• Wavelet decomposition for multi-band frequency control
• Triplane-based 3D texture frequency editing