Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models (2303.17591v1)

Abstract: The unlearning problem of deep learning models, once primarily an academic concern, has become a prevalent issue in the industry. The significant advances in text-to-image generation techniques have prompted global discussions on privacy, copyright, and safety, as numerous unauthorized personal IDs, content, artistic creations, and potentially harmful materials have been learned by these models and later utilized to generate and distribute uncontrolled content. To address this challenge, we propose \textbf{Forget-Me-Not}, an efficient and low-cost solution designed to safely remove specified IDs, objects, or styles from a well-configured text-to-image model in as little as 30 seconds, without impairing its ability to generate other content. Alongside our method, we introduce the \textbf{Memorization Score (M-Score)} and \textbf{ConceptBench} to measure the models' capacity to generate general concepts, grouped into three primary categories: ID, object, and style. Using M-Score and ConceptBench, we demonstrate that Forget-Me-Not can effectively eliminate targeted concepts while maintaining the model's performance on other concepts. Furthermore, Forget-Me-Not offers two practical extensions: a) removal of potentially harmful or NSFW content, and b) enhancement of model accuracy, inclusion and diversity through \textbf{concept correction and disentanglement}. It can also be adapted as a lightweight model patch for Stable Diffusion, allowing for concept manipulation and convenient distribution. To encourage future research in this critical area and promote the development of safe and inclusive generative models, we will open-source our code and ConceptBench at \href{https://github.com/SHI-Labs/Forget-Me-Not}{https://github.com/SHI-Labs/Forget-Me-Not}.

• The paper introduces FMN, a method that selectively unlearns specific content from diffusion models without degrading overall performance.
• It fine-tunes established text-to-image models using adapter-like patches, ensuring computational efficiency with minimal model restructuring.
• Experimental results confirm that FMN maintains image diversity while effectively omitting targeted content, addressing privacy and compliance needs.

Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models

The paper "Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models" addresses a central challenge in the development and deployment of diffusion models used for text-to-image generation: the ability to unlearn or forget specific concepts or identifiers without degrading the model's generative capabilities for other non-targeted content. This paper introduces Forget-Me-Not (FMN), a novel framework enabling selective forgetting within these models. The framework ensures that when specific contents, such as a designated ID, object, or style, need to be excluded from the model, this exclusion can be accomplished while preserving the model’s overall performance and output quality.

Methodological Insights

The proposed method leverages the fine-tuning of established text-to-image models like Stable Diffusion to incorporate the forgetting mechanism without necessitating model restructuring. The FMN approach operates by adjusting model parameters in a manner akin to adapter-like patches, making it a lightweight and computationally efficient solution for content filtering. The paper reports the successful application of FMN to selectively forget target concepts, demonstrated through comprehensive experiments yielding outputs where undesired content is ostensibly absent while non-targeted aspects remain intact.

Experimental Results

Quantitative and qualitative evaluations detail the effectiveness of FMN in contextually forgetting specified elements. Results demonstrate the capability of FMN to maintain the quality and diversity of generated images, marking a significant stride in advancing the utility of diffusion models in environments where privacy, ethics, or compliance necessitate the erasure of certain information. These findings are critical as they reveal the potential breadth of FMN’s applicability and its integration ease with current diffusion models.

Implications and Future Directions

Practically, this research contributes a valuable tool for customizing model outputs based on user or regulatory demands, which has significant implications for privacy preservation in sensitive or proprietary data scenarios. Theoretically, the ability to erase learned content selectively invites further exploration into memory mechanisms within neural networks, potentially influencing future AI research that intersects with areas like explainability and controllability in model behavior.

Moreover, the work enriches the dialogue around ethical AI, where forgetting becomes a tangible safeguard against unwanted machine inferences. Prospective developments following this research might explore extending FMN's forgetting capabilities into more complex scenarios, such as dynamic or adaptive forgetting, potentially augmented by reinforcement learning strategies to refine unlearning policies over time.

In summary, the introduction of Forget-Me-Not into text-to-image diffusion models represents a substantive advancement in the field, offering both practical enhancements in model customization and sparking further research possibilities into machine unlearning and memory management.