What question did this study set out to answer?

To develop a latent diffusion model framework that mitigates structural and semantic content distortion during digital art style transfer.

August 16, 2026Advanced ElectromagneticsOpen Access

Optimizing Digital Art Style Transfer Using a Diffusion Model

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Authors

DLD. LiuTaiyuan Normal University LSL.-C. Si

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Implication

Computational evaluation reveals reduced content distortion during digital art style transfer using diffusion models, indicating improved structural preservation for intelligent visual sensing.

Key Points

To develop a latent diffusion model framework that mitigates structural and semantic content distortion during digital art style transfer.
Quantified style strength using normalized CLIP style embeddings and integrated a dynamic cross-attention mechanism during the inverse denoising process.
Implemented a linear strength factor alongside pre-trained ControlNet regional guidance masks to constrain spatial style propagation.
Benchmarked image synthesis performance and structural preservation across the WikiArt and COCO datasets.
Achieved an image reconstruction fidelity with a peak signal-to-noise ratio (PSNR) of 27.6 ± 1.2 dB on benchmark datasets.
Demonstrated structural preservation with a structural similarity index measure (SSIM) of 0.85 ± 0.02 while preventing structural distortion.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a8179abf2fb91fc834ace66 https://doi.org/10.7716/aem.v15i3.3546

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Implication

Computational evaluation reveals reduced content distortion during digital art style transfer using diffusion models, indicating improved structural preservation for intelligent visual sensing.

Optimizing Digital Art Style Transfer Using a Diffusion Model

Discussion

Member takes

Implication

Key Points

Cite This Study

Optimizing Digital Art Style Transfer Using a Diffusion Model

Discussion

Member takes

Implication

Key Points

Cite This Study

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