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cs.CV2026
Concept Removal Guidance: Evidence-Calibrated Negative Guidance for Safe Diffusion Sampling
Yoonseok Choi, Chaeyoung Oh, Hyunjun Choi +2
Text-to-image diffusion models remain vulnerable to adversarial prompts that elicit disallowed content, motivating reliable inference-time controls. A popular approach is negative…
cs.CV2025
Dynamic VLM-Guided Negative Prompting for Diffusion Models
Hoyeon Chang, Seungjin Kim, Yoonseok Choi
We propose a novel approach for dynamic negative prompting in diffusion models that leverages Vision-Language Models (VLMs) to adaptively generate negative prompts during the denoi…