通过分析深层隐变量波动，提出无需训练的去噪框架，减少生成幻觉。
Unified Backbone Refinement for Diffusion Models via Internal-Latent Analysis
- 基于共享EMA准则检测深层隐变量异常波动
- 对异常区域实施针对性抑制，提升生成质量
- 适用于U-Net与Transformer模型，无需额外训练
扩散模型在多个领域表现卓越，其性能与参数化得分函数的去噪主干密切相关。本文提出一种阶段感知的系统性分析，发现深层低噪声隐变量中早期剧烈波动与伪影强相关。基于此，我们提出DUNE（Diffusion Unified Network refiNEr）——一种无需训练的优化框架，利用共享EMA准则检测深层低噪声内部隐变量中的突变，对检测出的条目实施主干特定抑制。尽管源自U-Net，该检测-抑制原则可自然扩展至基于Transformer的扩散模型，作用于深度自注意力块的隐变量。大量实验表明，DUNE在多种主干结构上均提升了保真度并减少了幻觉现象，为何时何地控制扩散主干提供了新视角。
原文摘要 · Abstract (English)
Diffusion models have achieved remarkable success across diverse domains, with performance closely related to the denoising backbones that parameterize the score function. In this paper, we present a systematic, phase-aware analysis of diffusion components and show that abrupt, early-stage fluctuations in deep latents are strongly associated with artifacts. Guided by these findings, we introduce DUNE (Diffusion Unified Network refiNEr), a training-free refinement framework that detects abrupt deviations in deep low-noise internal latents using a shared EMA-based criterion, and applies backbone-specific suppression to the detector-selected entries. Although derived from U-Net, the same detect-suppress principle extends naturally to Transformer-based diffusion models by acting on the latents of deep self-attention blocks. Extensive experiments across multiple backbones indicate that DUNE improves fidelity while reducing hallucinations, offering new insight into where and when diffusion backbones should be controlled.
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