Learning Dynamic Spectral Blending for Seamless Text-Guided Image Inpainting

Abstract

Image inpainting is a fundamental task in computer vision and multimedia processing. With the rapid development of denoising diffusion models, text-guided image inpainting has gradually become a mainstream research direction, enabling flexible content creation and localized semantic editing. Although existing text-guided diffusion inpainting approaches have greatly improved the generation quality and prompt alignment, they still face a core challenge: effectively balancing the fidelity preservation of unmasked regions and the semantic consistency of generated content in masked regions, especially suppressing spectral discontinuities and boundary artifacts caused by unreasonable frequency control. To address these problems, we propose Adaptive Frequency-Aware Diffusion(AFID), an adaptive framework for high-quality text-guided image inpainting. First, we design an Adaptive Frequency Threshold Network (AFTN) to dynamically predict multi-band frequency cutoffs according to mask information, denoising timesteps and text conditions, replacing manually designed fixed rules. Second, we propose a two-stage smooth spectral blending strategy to alleviate spectral abruptness and enhance the natural coherence between masked and unmasked areas. Third, we introduce a boundary ring smoothing module to further eliminate stitching artifacts near mask edges without excessive blurring. Experimental results show that our method achieves a leading performance on standard public benchmarks. We conduct comprehensive comparisons against prevailing state-of-the-art inpainting approaches, which solidly confirm our superiority in visual fidelity, intact-area preservation and text-prompt consistency. Extensive ablation studies verify the necessity of all three core modules and further analyze the impact of frequency thresholds and blending configurations on final restoration outcomes.