The Quiet Prompt: Erasing Ineffable Styles from Diffusion Models via Concept Leakage-aware Negative Guidance

Recent text-to-image diffusion models (T2I DMs) depend on vast, uncurated datasets that often include copyrighted artworks and personal images, risking the generation of unwanted content. While concept erasure methods have emerged to suppress such outputs, they predominantly rely on text prompts, limiting their effectiveness for visually nuanced or ineffable styles that are difficult to articulate verbally. To bridge this gap, we propose Quiet Prompt (QuP), a reference-based concept erasure method that identifies complex concepts using fewer than ten reference images. QuP operates in two stages: (1) Concept Embedding Generation (CEG), which captures the intricate visual morphology of an unwanted concept into a single latent embedding; and (2) Concept leakage-aware Negative Guidance (CNG), which utilizes this embedding as a negative semantic condition to steer the diffusion process. In addition, to overcome the limitations of conventional fixed-scale negative guidance (negative prompting)—which often suffers from structural distortion and unintended content loss—we introduce concept leakage-aware negative guidance scale $n_g(t)$. This mechanism adaptively modulates the guidance strength at each denoising step based on a semantic measurement of concept leakage between the intermediate generated image and the target concept in a joint embedding space. Extensive evaluations on idiosyncratic art styles and object erasure tasks demonstrate that QuP achieves superior concept suppression and content preservation, outperforming state-of-the-art text-based baselines in both image fidelity and erasure precision. The source code will be publicly provided to ensure reproducibility of our work.