Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models

TL;DR

This paper reveals the ineffectiveness of prompt-based safety guidance for unlearned text-to-image diffusion models and proposes a simple, robust method using implicit negative embeddings to improve safety defenses without modifying existing approaches.

Contribution

It introduces a novel method replacing negative prompts with implicit negative embeddings via concept inversion, enhancing safety defenses in text-to-image models.

Findings

Improved defense success rate on nudity and violence benchmarks.

Maintains core semantics of input prompts.

Compatible with existing guidance methods.

Abstract

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.