Training-Free Safe Denoisers for Safe Use of Diffusion Models

• URL: http://arxiv.org/abs/2502.08011v2
• Date: Thu, 13 Feb 2025 02:01:58 GMT
• Title: Training-Free Safe Denoisers for Safe Use of Diffusion Models
• Authors: Mingyu Kim, Dongjun Kim, Amman Yusuf, Stefano Ermon, Mi Jung Park,
• Abstract summary: Powerful diffusion models (DMs) are often misused to generate not-safe-for-work (NSFW) content or generate copyrighted material or data of individuals who wish to be forgotten. We develop a practical algorithm that produces high-quality samples while avoiding negation areas of the data distribution. These results hint at the great potential of our training-free safe denoiser for using DMs more safely.
• Score: 49.045799120267915
• License:
• Abstract: There is growing concern over the safety of powerful diffusion models (DMs), as they are often misused to produce inappropriate, not-safe-for-work (NSFW) content or generate copyrighted material or data of individuals who wish to be forgotten. Many existing methods tackle these issues by heavily relying on text-based negative prompts or extensively retraining DMs to eliminate certain features or samples. In this paper, we take a radically different approach, directly modifying the sampling trajectory by leveraging a negation set (e.g., unsafe images, copyrighted data, or datapoints needed to be excluded) to avoid specific regions of data distribution, without needing to retrain or fine-tune DMs. We formally derive the relationship between the expected denoised samples that are safe and those that are not safe, leading to our $\textit{safe}$ denoiser which ensures its final samples are away from the area to be negated. Inspired by the derivation, we develop a practical algorithm that successfully produces high-quality samples while avoiding negation areas of the data distribution in text-conditional, class-conditional, and unconditional image generation scenarios. These results hint at the great potential of our training-free safe denoiser for using DMs more safely.

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