潜在拡散アーキテクチャを用いたマルチスタイル画像生成のためのコンテンツスタイル分離

Existing multi-style image generation methods face critical challenges: insufficient content-style decoupling, high computational costs for high-resolution generation, and structural distortion during style transfer. To address these, we propose the Dual-Conditional Lightweight Style Diffusion Model (DCLSDM), a novel approach enhancing content-style decoupling via a dual-conditional control mechanism. This mechanism independently manages content structure and style expression, enabling better control in style transfer. Experimental results on WikiArt and Summer2Winter Yosemite datasets show DCLSDM outperforms comparative models in SSIM, LPIPS, and FID, with significant improvements in inference time, memory usage, and parameter scale-making it suitable for resource-constrained scenarios. It offers an efficient, controllable solution for multi-style image generation, with potential in content creation and digital art production.