自适应辅助提示词混合用于目标忠实的扩散生成
Adaptive Auxiliary Prompt Blending for Target-Faithful Diffusion Generation
提出一种基于Tweedie恒等式的免训练自适应提示词混合框架，通过闭式系数在扩散每一步平衡辅助锚框提示词与目标提示词的影响，可直接用于提升罕见概念生成和图像编辑的忠实度。
原文依据 AAPB derives a closed-form adaptive coefficient that optimally balances the influence between the auxiliary anchor and the target prompt at each diffusion step.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于扩散的文本到图像（T2I）模型在生成逼真且语义丰富的图像方面取得了显著进展。然而，当目标概念位于训练分布的低密度区域时，这些模型往往产生语义错位或结构不一致的结果。这一局限源于文本-图像数据集的长尾特性，其中罕见概念或编辑指令代表性不足。为解决此问题，我们引入自适应辅助提示词混合（AAPB）——一个统一框架，用于稳定低密度区域中的扩散过程。AAPB利用辅助锚框提示词在罕见概念生成中提供语义支持，在图像编辑中提供结构支持，确保忠实引导至目标提示词。与先前启发式的提示词交替方法不同，AAPB推导出一个闭式自适应系数，在每一步扩散中优化平衡辅助锚框与目标提示词之间的影响。基于Tweedie恒等式，我们的公式为自适应提示词混合提供了一个有原则且免训练框架，确保稳定且目标忠实的生成。我们通过受控实验证明了自适应插值相对于固定插值的有效性，并在RareBench和FlowEdit数据集上实证展示了持续改进，与先前的免训练基线相比，实现了更优的语义准确性和结构保真度。
英文原文摘要 1490 字符
Diffusion-based text-to-image (T2I) models have made remarkable progress in generating photorealistic and semantically rich images. However, when the target concepts lie in low-density regions of the training distribution, these models often produce semantically misaligned or structurally inconsistent results. This limitation arises from the long-tailed nature of text-image datasets, where rare concepts or editing instructions are underrepresented. To address this, we introduce Adaptive Auxiliary Prompt Blending (AAPB) -- a unified framework that stabilizes the diffusion process in low-density regions. AAPB leverages auxiliary anchor prompts to provide semantic support in rare concept generation and structural support in image editing, ensuring faithful guidance toward the target prompt. Unlike prior heuristic prompt alternation methods, AAPB derives a closed-form adaptive coefficient that optimally balances the influence between the auxiliary anchor and the target prompt at each diffusion step. Grounded in Tweedie's identity, our formulation provides a principled and training-free framework for adaptive prompt blending, ensuring stable and target-faithful generation. We demonstrate the effectiveness of adaptive interpolation over fixed interpolation through controlled experiments and empirically show consistent improvements on the RareBench and FlowEdit datasets, achieving superior semantic accuracy and structural fidelity compared to prior training-free baselines.