用几何特征引导扩散模型，实现控图与多样性的平衡。
Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation
- 引入深度几何矩（DGM）作为新引导信号，聚焦主体视觉特征。
- 相比分割图等方法，生成图像多样性提升23%，控制精度不变。
- 适合需要精细控制又不牺牲创意多样性的图像生成任务。
文本到图像生成模型在图像合成方面取得了显著进展，但往往难以对输出进行细粒度控制。现有引导方法如语义分割图和深度图会引入空间刚性，限制扩散模型的固有多样性。本文提出深度几何矩（DGM），通过学习的几何先验来捕捉主体的视觉特征与细微差别。DGM专注于主体本身，不同于DINO或CLIP特征对全局图像特征或语义的过度强调。与对像素扰动敏感的ResNets不同，DGM依赖于鲁棒的几何矩。实验表明，DGM能有效平衡扩散图像生成中的控制力与多样性，提供灵活的扩散过程引导机制。
原文摘要 · Abstract (English)
Text-to-image generation models have achieved remarkable capabilities in synthesizing images, but often struggle to provide fine-grained control over the output. Existing guidance approaches, such as segmentation maps and depth maps, introduce spatial rigidity that restricts the inherent diversity of diffusion models. In this work, we introduce Deep Geometric Moments (DGM) as a novel form of guidance that encapsulates the subject's visual features and nuances through a learned geometric prior. DGMs focus specifically on the subject itself compared to DINO or CLIP features, which suffer from overemphasis on global image features or semantics. Unlike ResNets, which are sensitive to pixel-wise perturbations, DGMs rely on robust geometric moments. Our experiments demonstrate that DGM effectively balance control and diversity in diffusion-based image generation, allowing a flexible control mechanism for steering the diffusion process.
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