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DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization.

Nisha Huang, Yuxin Zhang, Fan Tang

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    This summary is machine-generated.

    DiffStyler introduces a novel dual diffusion architecture for text-driven image stylization, enabling precise control over content and style. This method enhances structure preservation in stylized images, offering an intuitive user experience.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Arbitrary image-guided style transfer shows promise, but text-driven image stylization offers more intuitive user control.
    • Existing feed-forward Convolutional Neural Network (CNN) pipelines face challenges due to cross-modal discrepancies in text-driven stylization.
    • Developing effective methods for text-guided image stylization is crucial for advancing creative image manipulation.

    Purpose of the Study:

    • To present DiffStyler, a novel dual diffusion processing architecture for text-driven image stylization.
    • To enable precise control over the balance between content and style in stylized images.
    • To improve the preservation of structural information from the original content image during stylization.

    Main Methods:

    • Proposed a dual diffusion processing architecture to manage content-style balance.
    • Integrated cross-modal style information as step-by-step guidance within the diffusion process.
    • Introduced content image-based learnable noise to guide the reverse denoising process for better structure preservation.

    Main Results:

    • DiffStyler effectively controls the balance between content and style in stylized images.
    • The method successfully integrates textual style descriptions as guidance during diffusion.
    • Stylization results demonstrate improved preservation of the original content image's structure.
    • Extensive qualitative and quantitative experiments validate DiffStyler's superiority over baseline methods.

    Conclusions:

    • DiffStyler offers an effective and intuitive approach to text-driven image stylization.
    • The dual diffusion architecture and learnable noise mechanism significantly enhance stylization quality and content preservation.
    • The proposed method advances the field of cross-modal image synthesis and style transfer.