让文字生成模型增强或抑制特定概念，无需添加新元素。
Scaling Concept With Text-Guided Diffusion Models
- 通过分解文本引导扩散模型中的概念，实现对概念强度的调节。
- 在WeakConcept-10数据集上验证，可有效提升模糊概念的表现。
- 支持图像与音频领域的零样本应用，如姿态标准化与声音突出/消除。
文本引导扩散模型通过文本描述生成高质量内容，也实现了概念替换的编辑范式（如将狗替换为虎）。本文探索新方法：不替换概念，而是增强或抑制其本身。通过实证研究发现，概念可在文本引导扩散模型中被分解。基于此，提出ScalingConcept——一种简单有效的技术，可在真实输入中放大或缩小已分解概念，无需引入新元素。为系统评估该方法，构建了WeakConcept-10数据集，其中概念不完整需增强。更重要的是，ScalingConcept实现了跨图像与音频领域的多种零样本应用，如生成标准姿态和生成性声音突出或移除。
原文摘要 · Abstract (English)
Text-guided diffusion models have revolutionized generative tasks by producing high-fidelity content from text descriptions. They have also enabled an editing paradigm where concepts can be replaced through text conditioning (e.g., a dog to a tiger). In this work, we explore a novel approach: instead of replacing a concept, can we enhance or suppress the concept itself? Through an empirical study, we identify a trend where concepts can be decomposed in text-guided diffusion models. Leveraging this insight, we introduce ScalingConcept, a simple yet effective method to scale decomposed concepts up or down in real input without introducing new elements. To systematically evaluate our approach, we present the WeakConcept-10 dataset, where concepts are imperfect and need to be enhanced. More importantly, ScalingConcept enables a variety of novel zero-shot applications across image and audio domains, including tasks such as canonical pose generation and generative sound highlighting or removal.
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