Few-Shot Visual Concept Extraction for Steering Diffusion Transformers

Image steering aims to provide fine-grained control over concepts in generated images, enabling targeted and continuous manipulation without broadly altering the generation process. Recent steering methods for diffusion models primarily focus on the text-conditioning space or rely on costly auxiliary training. Deviating from that, we here propose a novel steering approach that extracts visual concept directions directly from the image-side activations of Diffusion Transformers. Specifically, we showcase few-shot concept extraction in the activation space using a simple difference-of-means estimator over intermediate residuals. We observe (a) scene-level concepts such as lighting, weather, and style are distributed across image tokens, as well as (b) local concepts such as object colors, textures, materials, and facial attributes are concentrated in specific regions. Towards controlling both concepts, we introduce a new unified training-free framework that uses global difference-of-means for (a) distributed concepts and masked difference-of-means for (b) local concepts, preventing local signals from being diluted by global averaging. Notably, using only a handful of positive and negative prompt pairs, our method identifies and extracts reusable concept directions that can be injected at inference for continuous steering. Quantitatively, our method outperforms text-encoder steering and training-free activation-editing baselines, as well as is competitive with specialized editing models. Our results suggest that as concept directions can be applied at patch level, our method enables precise targeted and compositional steering, manipulating multiple concepts in the same image.