Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models
This addresses safety concerns in text-to-image generation for users and developers, but it is incremental as it builds on existing approaches.
The paper tackles the problem of degraded defense performance when combining fine-tuning and training-free guidance methods for text-to-image diffusion models, and proposes a method using implicit negative embeddings that improves defense success rates on nudity and violence benchmarks.
Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the model to unlearn harmful concepts and (2) training-free guidance methods that leverage negative prompts. However, we observe that combining these two orthogonal approaches often leads to marginal or even degraded defense performance. This observation indicates a critical incompatibility between two paradigms, which hinders their combined effectiveness. In this work, we address this issue by proposing a conceptually simple yet experimentally robust method: replacing the negative prompts used in training-free methods with implicit negative embeddings obtained through concept inversion. Our method requires no modification to either approach and can be easily integrated into existing pipelines. We experimentally validate its effectiveness on nudity and violence benchmarks, demonstrating consistent improvements in defense success rate while preserving the core semantics of input prompts.