One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications (2312.16145v2)

Abstract: The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning, from which we observe the following issues: 1) Generation alternation towards erosion: Parameter drift during target elimination causes alternations and potential deformations across all generations, even eroding other concepts at varying degrees, which is more evident with multi-concept erased; 2) Transfer inability & deployment inefficiency: Previous model-specific erasure impedes the flexible combination of concepts and the training-free transfer towards other models, resulting in linear cost growth as the deployment scenarios increase. To achieve non-invasive, precise, customizable, and transferable elimination, we ground our erasing framework on one-dimensional adapters to erase multiple concepts from most DMs at once across versatile erasing applications. The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing, and meantime the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once obtained, SPMs can be flexibly combined and plug-and-play for other DMs without specific re-tuning, enabling timely and efficient adaptation to diverse scenarios. During generation, our Facilitated Transport mechanism dynamically regulates the permeability of each SPM to respond to different input prompts, further minimizing the impact on other concepts. Quantitative and qualitative results across ~40 concepts, 7 DMs and 4 erasing applications have demonstrated the superior erasing of SPM. Our code and pre-tuned SPMs are available on the project page https://lyumengyao.github.io/projects/spm.

• The paper presents SPM, a novel one-dimensional adapter that effectively suppresses unwanted concepts in diffusion models without compromising model quality.
• It integrates Latent Anchoring and Facilitated Transport to preserve non-target semantic richness and adjust concept erasure dynamically.
• Quantitative evaluations using metrics like CLIP Score and FID demonstrate SPM's superior performance over existing methods in content moderation.

One-dimensional Adapter to Rule Them All: An Examination of Concept Erasure in Diffusion Models

The paper "One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications" presents a methodological approach to addressing risks associated with text-to-image diffusion models (DMs), such as copyright infringement and inappropriate content dissemination. The authors propose a lightweight, one-dimensional adapter technology, termed Semi-Permeable Membrane (SPM), as a solution to the limitations of existing concept erasing methods. This work emphasizes the need for precise, non-invasive, customizable, and transferable techniques within DMs to effectively mitigate undesirable content without compromising overall model quality or versatility.

The Proposed SPM Framework

At the core of this paper lies the novel concept of the one-dimensional SPM, which is seamlessly integrated into any diffusion model to suppress specific unwanted patterns while maintaining the inherent structure of the original model. By operating at a modest dimensional complexity, SPMs offer a marginal size increase, maintaining computational efficiency without hampering the model's generation capabilities.

The SPM framework is augmented with two essential components: Latent Anchoring (LA) and Facilitated Transport (FT). LA aids in preserving the semantic richness of non-target concepts during the erasing process by sampling a broad swathe of representations from the latent conceptual space. In contrast, FT dynamically modulates the activation threshold of an SPM based on the incoming prompt's semantic alignment with the targeted concept. Together, these components mitigate concept erosion—the unintended degradation of non-target concepts—and ensure robustness across various generative scenarios.

Strong Numerical Results

The paper provides quantitative evaluations demonstrating the effectiveness of SPM relative to prior methods such as ESD, ConAbl, and SA. In erasing experiments involving singular and multiple concepts, such as character instances or artistic styles, SPM consistently shows superior performance in maintaining generation quality and semantic integrity of non-target concepts. The authors meticulously quantify improvements using established metrics such as CLIP Score and Fréchet Inception Distance (FID), ensuring their results can be reliably assessed within the established body of DM literature.

Practical and Theoretical Implications

The adaptability of SPMs is underscored through robust empirical analysis across multiple models and applications. This cross-model functionality enables SPMs to be directly applied to any DM derivative without necessitating re-tuning, significantly reducing overhead costs in time, computation, and parameter storage. Moreover, the paper's exploration into SPM's concept erasure and reconsolidation capabilities positions it as an advancement in content moderation technologies.

From a theoretical standpoint, by marrying latent representation management with parameter-efficient fine-tuning strategies, the paper advances the discourse on semantic alignment in diffusion models. It showcases the potential to extend beyond mere risk mitigation, with implications suggesting enhancements in DM fairness and diversity initiatives.

Future Directions

Potential future research could delve into refining the granular control over concept erasure to differentiate between closely related semantic entities, improving the explainability of erasure decisions in automated model governance, and further integrating SPMs with evolving safety and ethical guidelines. Additionally, investigating the use of SPMs for purpose-driven content adjustments, such as aligning DMs with regional regulatory compliances or linguistic nuances, could present new avenues of application.

In conclusion, the paper provides a compelling case for the utility of SPMs within the broader context of model alignment with societal and regulatory standards. The introduced methodologies align with the current trajectory towards more ethical and transparent AI systems.