Beyond Named Prompts: Mitigating Direct and Indirect IP Generation via Generation-State Intervention

Modern text-to-image diffusion models can generate images that closely resemble recognizable intellectual-property (IP) characters, whether prompted directly by character names or indirectly through name-free semantic cues. Visual descriptions of appearance, clothing, and composition can induce the latter, challenging mitigations that rely only on prompt inspection or predefined parameter-level interventions. In this work, we introduce a generation-state intervention framework that monitors target-like semantics as they emerge during diffusion and redirects the same generation trajectory without modifying model parameters. The framework uses a predicted clean output for early monitoring and a generic-category-anchored contrast to reduce target-related components while anchoring the requested visual content. Across FLUX.1-dev and Stable Diffusion 3.5 Medium, our method substantially reduces target-character detections under both direct and indirect prompting while maintaining consistency with the visual characteristics specified by the prompt. Together, these results show that the evolving generation state provides an actionable signal for mitigation even when the prompt contains no explicit target reference. Our code is available at https://anonymous.4open.science/r/DiT-IP-Generation-Mitigation-8C43.