AlignGuard: Scalable Safety Alignment for Text-to-Image Generation

• URL: http://arxiv.org/abs/2412.10493v2
• Date: Tue, 24 Jun 2025 20:53:27 GMT
• Title: AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
• Authors: Runtao Liu, I Chieh Chen, Jindong Gu, Jipeng Zhang, Renjie Pi, Qifeng Chen, Philip Torr, Ashkan Khakzar, Fabio Pizzati,
• Abstract summary: Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse.<n>In this work, we introduce AlignGuard, a method for safety alignment of T2I models.
• Score: 68.07258248467309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures are typically limited to text-based filtering or concept removal strategies, able to remove just a few concepts from the model's generative capabilities. In this work, we introduce AlignGuard, a method for safety alignment of T2I models. We enable the application of Direct Preference Optimization (DPO) for safety purposes in T2I models by synthetically generating a dataset of harmful and safe image-text pairs, which we call CoProV2. Using a custom DPO strategy and this dataset, we train safety experts, in the form of low-rank adaptation (LoRA) matrices, able to guide the generation process away from specific safety-related concepts. Then, we merge the experts into a single LoRA using a novel merging strategy for optimal scaling performance. This expert-based approach enables scalability, allowing us to remove 7x more harmful concepts from T2I models compared to baselines. AlignGuard consistently outperforms the state-of-the-art on many benchmarks and establishes new practices for safety alignment in T2I networks. Code and data will be shared at https://safetydpo.github.io/.

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