While diffusion-based T2I models have achieved remarkable image generation quality, they also enable easy creation of harmful content, raising social concerns and highlighting the need for safer generation. Existing inference-time guiding methods lack both adaptivity--adjusting guidance strength based on the prompt--and selectivity--targeting only unsafe regions of the image. Our method, SP-Guard, addresses these limitations by estimating prompt harmfulness and applying a selective guidance mask to guide only unsafe areas. Experiments show that SP-Guard generates safer images than existing methods while minimizing unintended content alteration. Beyond improving safety, our findings highlight the importance of transparency and controllability in image generation.

翻译：尽管基于扩散模型的文本到图像生成模型已实现卓越的图像生成质量，但它们也使得有害内容的创建变得容易，引发了社会担忧并凸显了对更安全生成的需求。现有的推理时引导方法既缺乏自适应性——即根据提示调整引导强度，也缺乏选择性——即仅针对图像中的不安全区域进行引导。我们的方法SP-Guard通过估计提示的危害性并应用选择性引导掩码来仅引导不安全区域，从而解决了这些局限性。实验表明，SP-Guard比现有方法生成更安全的图像，同时最大限度地减少对内容的意外修改。除了提升安全性外，我们的研究结果还强调了图像生成中透明度和可控性的重要性。