CSS: Clustering-guided SliderSpace for Under-represented Visual Concept Exploration of Diffusion Models

Recent advances in diffusion models have enabled users to generate high-quality images from simple text prompts. However, efficiently steering these models to produce images with diverse styles and appearances remains a significant challenge. SliderSpace was recently introduced as a promising solution, leveraging principal component analysis (PCA) on generated image features to find meaningful semantic directions and training low-rank adaptation-based (LoRA) sliders of these directions for controllable image generation. However, we observe that existing pre-trained diffusion models, such as Stable Diffusion, exhibit substantial biases in their generation results which limit the visual exploration capability of SliderSpace for rare attributes or concepts. To address this issue, we propose clustering-guided SliderSpace, which introduces an initial clustering step to explicitly identify subconcepts from generated images followed by applying PCA and the remaining SliderSpace procedure to each cluster. Experimental results demonstrate that this clustering step is useful and essential for the discovery of rare and under-represented concepts. In addition, the proposed method complements the original SliderSpace, when used together, it enables more comprehensive visual concept exploration.