用扩散模型生成拼贴图，结构更稳、风格更个性化。
Generative Phomosaic with Structure-Aligned and Personalized Diffusion
- 用扩散模型生成拼贴块，基于参考图控制细节
- 低频条件机制让整体结构一致，避免错位
- 少样本即可生成定制风格，无需大量图片
我们提出首个生成式照片拼贴方法。传统方法依赖大量拼贴图像和颜色匹配，限制了多样性与结构一致性。本框架采用基于扩散的生成方式，根据参考图像合成拼贴块，通过低频条件扩散机制对齐全局结构，同时保留提示驱动的细节。该生成范式使拼贴在语义表达和结构连贯性上均更优，有效克服了匹配类方法的根本局限。借助少样本个性化扩散，模型可生成用户专属或风格一致的拼贴块，无需大量图像数据。
原文摘要 · Abstract (English)
We present the first generative approach to photomosaic creation. Traditional photomosaic methods rely on a large number of tile images and color-based matching, which limits both diversity and structural consistency. Our generative photomosaic framework synthesizes tile images using diffusion-based generation conditioned on reference images. A low-frequency conditioned diffusion mechanism aligns global structure while preserving prompt-driven details. This generative formulation enables photomosaic composition that is both semantically expressive and structurally coherent, effectively overcoming the fundamental limitations of matching-based approaches. By leveraging few-shot personalized diffusion, our model is able to produce user-specific or stylistically consistent tiles without requiring an extensive collection of images.
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