What question did this study set out to answer?

To resolve style conflict, uneven style dominance, and structural loss in multi-style diffusion-based image generation.

August 16, 2026Discover Artificial IntelligenceOpen Access

A diffusion-based multi-style image generation framework with adaptive weighted style fusion

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Authors

HGHaoran GongNanjing University of Information Science and Technology

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Implication

Computational study demonstrates improved multi-style coordination and content preservation in synthetic images, highlighting balanced diffusion-based style fusion.

Key Points

To resolve style conflict, uneven style dominance, and structural loss in multi-style diffusion-based image generation.
Extracted multi-level content and style features using a pretrained VGG encoder.
Calculated dynamic style weights via style complexity and similarity metrics, integrating cross-attention and adaptive modulation modules within a denoising U-Net.
Evaluated performance on a dataset of 1,000 content images and 1,000 style images through quantitative metrics, ablation tests, and user evaluations.
Achieved a PSNR of 32.4 dB, SSIM of 0.91, FID of 24.3, and LPIPS of 0.176 across test datasets.
Obtained high multi-style alignment with a CLIP-I score of 0.846 and a style consistency score of 0.834.

Cite This Study

Haoran Gong (2026) studied this question.

synapsesocial.com/papers/6a81794cf2fb91fc834ac71b https://doi.org/10.1007/s44163-026-01921-w

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Implication

Computational study demonstrates improved multi-style coordination and content preservation in synthetic images, highlighting balanced diffusion-based style fusion.

A diffusion-based multi-style image generation framework with adaptive weighted style fusion

Discussion

Member takes

Implication

Key Points

Cite This Study

A diffusion-based multi-style image generation framework with adaptive weighted style fusion

Discussion

Member takes

Implication

Key Points

Cite This Study

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