  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}.
