Introspective Attention Modulation for Safe Text-to-Image Generation
State-of-the-art flow based text-to-image (T2I) models exhibit remarkable generative abilities but remain vulnerable to producing unsafe content. Prior safety efforts range from concept erasure and prompt filtering to classifier-based gating. However, simple techniques like parameter efficient adaptations of the models easily bypass such guardrails. We introduce a unique principled approach that achieves safety by regulating the model's attention dynamics through inference-time introspection, exhibiting intrinsic robustness. Our method analyzes and rebalances attention activations throughout image synthesis, steering generations away from unsafe concepts while preserving semantic alignmen...
| Authors | Basim Azam, Hossein Rahmani, Naveed Akhtar |
|---|---|
| Primary category | cs.CV |
| Published | 16 July 2026 |
| Last updated at source | 11 August 2026 |
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