无需训练即可提升扩散模型生成图像的视觉质量。
Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models
- 通过低层特征提取退化概念向量，实现无训练编辑。
- 结合瓶颈补丁与无分类器引导，显著改善图像质量。
- 适合需要快速优化生成效果的研究者和开发者。
无条件扩散模型具备强大的生成先验，但如何引导其生成更具美感的输出仍鲜有研究。我们发现，当前主流的无训练编辑方法 h-space patching 在进行全局性、低层级的美学与感知优化时存在系统性失效问题。为此，提出一种全新的、通用的无条件扩散模型图像编辑框架，无需显式训练。该推理阶段机制通过提取退化概念向量，在低层特征空间中结合瓶颈补丁与无分类器引导，引导采样过程远离退化流形，从而在不重训练模型的情况下持续生成更优图像。
原文摘要 · Abstract (English)
Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We show that h-space patching, the dominant paradigm for training-free diffusion editing, systematically fails for global, low-level transformations required for aesthetic and perceptual refinement. We introduce a novel, generalized framework for image-editing in unconditional diffusion models without explicit training. This inference-time mechanism operates on low-level features by extracting degradation concept vectors and combining bottleneck patching with classifier-free guidance to guide sampling away from the degraded manifold, producing consistently improved images without any model retraining.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。