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Jeffrey Simpson's attachment theory suggests that early caregiver relationships shape lasting patterns of behavior and emotional regulation, known as attachment styles. These patterns are organized along two key dimensions: self-esteem and interpersonal trust. The intersection of these dimensions produces four primary attachment styles that typically persist throughout life and significantly influence how individuals form and maintain relationships.Secure Attachment StyleIndividuals with a...
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Content style decoupling for multi style image generation using latent diffusion architecture.

Kaiyan Chu1,2, Yu Shang3,4, Lingrui Zhang5

  • 1School of Design and Art, College of Science & Technology Ningbo University, Ningbo, 315300, China.

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Summary
This summary is machine-generated.

This study introduces the Dual-Conditional Lightweight Style Diffusion Model (DCLSDM) for efficient multi-style image generation. The DCLSDM enhances content-style decoupling, offering superior control and reduced computational costs.

Keywords:
Content-style decouplingDual-conditional controlLatent diffusion modelLightweight modelMulti-style image generation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-style image generation methods struggle with content-style decoupling, high computational demands for high-resolution outputs, and structural distortions during style transfer.
  • Existing models often fail to independently control content structure and style elements, limiting their effectiveness.

Purpose of the Study:

  • To propose a novel Dual-Conditional Lightweight Style Diffusion Model (DCLSDM) that addresses the limitations of current multi-style image generation techniques.
  • To enhance content-style decoupling for improved control in style transfer applications.
  • To develop an efficient and resource-friendly solution for high-quality image generation.

Main Methods:

  • Developed a Dual-Conditional Lightweight Style Diffusion Model (DCLSDM) incorporating a dual-conditional control mechanism.
  • The mechanism independently manages content structure and style expression for precise control during style transfer.
  • Evaluated the model on WikiArt and Summer2Winter Yosemite datasets.

Main Results:

  • DCLSDM demonstrated superior performance compared to existing models, evidenced by improved SSIM, LPIPS, and FID scores.
  • Achieved significant reductions in inference time, memory usage, and parameter scale.
  • The model proved effective in resource-constrained environments.

Conclusions:

  • The DCLSDM offers an efficient and controllable solution for multi-style image generation.
  • Its lightweight design and enhanced decoupling capabilities make it suitable for various applications, including content creation and digital art production.
  • The model overcomes key challenges in style transfer and high-resolution image synthesis.