Training-Free Concept Negation in Text-to-Image Synthesis

Text-to-image (T2I) models often struggle to follow negation prompts involving logical negation, raising serious concerns in critical scenarios. Existing negative prompting methods fail to overcome the strong correlations between contextual and negated concepts, while negation-aware fine-tuning is costly and difficult to generalize. To address negation failures, we revisit the text-image fusion mechanism in generative models and propose a training-free, context-aware null space projection method that suppresses negated concepts while preserving image layout and visual quality. Specifically, our method modifies the value vectors of cross-attention to directly control content synthesis via projecting contextual components onto the null space of negated semantics. We additionally introduce ghost token augmentation that expands the search space of negated concepts to further suppress negated content. Our approach operates as a plug-in module without additional training. Experiments on challenging generation tasks, including our new Negation Benchmark featuring complex prompts with mixed positive and negated concepts, demonstrate significant improvements over open-source baselines and competitive performance with Nano Banana Flash.