通过操控中间激活，突破文本生成模型对概念的访问瓶颈。
Concept Reachability in Diffusion Models: Beyond Dataset Constraints
- 用干预中间特征值的方法，让模型触及原本无法通过提示到达的概念。
- 少量样本即可触发概念可及性的相变，且干预位置决定能否成功。
- 即使数据质量差，通过操控仍能稳定实现概念生成，适合控制优化。
尽管文本到图像模型在生成质量和复杂度上取得显著进展，但提示词并不总能带来预期输出。通过直接操控模型中间激活来引导隐空间中的概念，已成为一种可行替代方案，可触及原本因提示限制而无法访问的概念。本文设计了包含三重挑战的训练数据设置：概念稀缺、描述不明确以及概念间的数据偏差。结果表明：(i) 隐空间中概念的可及性表现出明显的相变现象，仅需少量样本即可实现可及性；(ii) 干预在隐空间中的位置至关重要，某些概念仅在特定变换阶段可被触及；(iii) 尽管提示能力随数据质量下降迅速减弱，但通过操控仍能可靠实现概念生成。该发现使模型提供方可绕过昂贵的重训练与数据整理，转而聚焦于用户可控机制的创新。
原文摘要 · Abstract (English)
Despite significant advances in quality and complexity of the generations in text-to-image models, prompting does not always lead to the desired outputs. Controlling model behaviour by directly steering intermediate model activations has emerged as a viable alternative allowing to reach concepts in latent space that may otherwise remain inaccessible by prompt. In this work, we introduce a set of experiments to deepen our understanding of concept reachability. We design a training data setup with three key obstacles: scarcity of concepts, underspecification of concepts in the captions, and data biases with tied concepts. Our results show: (i) concept reachability in latent space exhibits a distinct phase transition, with only a small number of samples being sufficient to enable reachability, (ii) where in the latent space the intervention is performed critically impacts reachability, showing that certain concepts are reachable only at certain stages of transformation, and (iii) while prompting ability rapidly diminishes with a decrease in quality of the dataset, concepts often remain reliably reachable through steering. Model providers can leverage this to bypass costly retraining and dataset curation and instead innovate with user-facing control mechanisms.
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