Technology Category
Computer Vision: Diffusion Models for VisionNatural Language Processing: Prompt Engineering / PromptingMachine Learning: Active Learning
Concept Reachability in Diffusion Models: Beyond Dataset Constraints
🤖 AI Summary
This paper addresses the challenge of reliably eliciting rare or entangled concepts in diffusion models using text prompts. We propose a novel paradigm for controllable generation via intermediate latent-space activation steering. Our method intervenes directly in key hidden-layer activations during the diffusion process—bypassing prompt engineering and model retraining—to enable fine-grained concept control under realistic constraints such as data scarcity, ambiguous annotations, and concept entanglement. Our contributions are threefold: (1) We identify a phase-transition phenomenon in concept accessibility; (2) We reveal stage-sensitive intervention efficacy and pinpoint the optimal layer segment for activation steering; (3) Empirical results demonstrate that latent-space guidance substantially outperforms textual prompting—achieving high concept accessibility and robustness even with few-shot samples and low-quality data. This provides an interpretable, lightweight, plug-and-play pathway for user-end controllable generation.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Understanding concept reachability in diffusion models
Overcoming dataset constraints like scarcity and bias
Exploring latent space steering for better control
Innovation
Methods, ideas, or system contributions that make the work stand out.
Steering intermediate model activations for control
Phase transition in concept reachability discovery
Intervention timing critical for concept accessibility
🔎 Similar Papers
No similar papers found.
