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Related Experiment Video

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer.

Yanqi Ge, Jiaqi Liu, Qingnan Fan

    IEEE Transactions on Visualization and Computer Graphics
    |December 5, 2025
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    Summary

    This study introduces Adaptive Style Incorporation (ASI) for diffusion model-based style transfer. ASI enhances content preservation and structural consistency in stylized images, benefiting artists with precise artistic editing.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Digital Art

    Background:

    • Text-driven style transfer using diffusion models offers creative potential but struggles with content preservation and structural consistency.
    • Current methods use coarse-grained, prompt-level style injection, leading to undesirable structural deviations in stylized images.
    • This limitation hinders professional artists requiring precise control over artistic editing.

    Purpose of the Study:

    • To achieve a balance between content preservation and style transformation in text-driven style transfer.
    • To introduce a novel method for fine-grained, feature-level style incorporation.

    Main Methods:

    • Proposed Adaptive Style Incorporation (ASI) method.
    • Developed Siamese Cross-Attention (SiCA) to separate content and style features.
    • Introduced Adaptive Content-Style Blending (AdaBlending) for structure-consistent coupling of content and style.

    Main Results:

    • ASI demonstrates superior performance in maintaining image structure compared to existing methods.
    • The method effectively preserves content while achieving desired stylized effects.
    • Experimental results validate the efficacy of SiCA and AdaBlending modules.

    Conclusions:

    • Adaptive Style Incorporation (ASI) offers a significant advancement in text-driven style transfer.
    • The proposed fine-grained approach addresses key limitations in structure and content preservation.
    • ASI provides artists with enhanced control for precise artistic editing and style transformation.