Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models

TL;DR

This paper introduces a novel method using growth inhibitors and an adapter to effectively suppress inappropriate concepts in diffusion models, avoiding fine-tuning and preserving image quality.

Contribution

It proposes a concept suppression technique in image space that directly erases inappropriate ideas without fine-tuning, reducing catastrophic forgetting.

Findings

Effective suppression of inappropriate concepts in diffusion models.

Minimal impact on image quality and other concepts.

Outperforms existing fine-tuning approaches.

Abstract

Despite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which leads to various ethical and business risks. Specifically, model-generated images may exhibit not safe for work (NSFW) content and style copyright infringements. The prompts that result in these problems often do not include explicit unsafe words; instead, they contain obscure and associative terms, which are referred to as implicit unsafe prompts. Existing approaches directly fine-tune models under textual guidance to alter the cognition of the diffusion model, thereby erasing inappropriate concepts. This not only requires concept-specific fine-tuning but may also incur catastrophic forgetting. To address these issues, we explore the representation of inappropriate concepts in the image space and guide them towards more suitable ones by injecting growth inhibitors, which are tailored based on the identified features related to inappropriate concepts during the diffusion process. Additionally, due to the varying degrees and scopes of inappropriate concepts, we train an adapter to infer the corresponding suppression scale during the injection process. Our method effectively captures the manifestation of subtle words at the image level, enabling direct and efficient erasure of target concepts without the need for fine-tuning. Through extensive experimentation, we demonstrate that our approach achieves superior erasure results with little effect on other concepts while preserving image quality and semantics.