  Generating images from text has become easier because of the scaling of
diffusion models and advancements in the field of vision and language. These
models are trained using vast amounts of data from the Internet. Hence, they
often contain undesirable content such as copyrighted material. As it is
challenging to remove such data and retrain the models, methods for erasing
specific concepts from pre-trained models have been investigated. We propose a
novel concept-erasure method that updates the text encoder using few-shot
unlearning in which a few real images are used. The discussion regarding the
generated images after erasing a concept has been lacking. While there are
methods for specifying the transition destination for concepts, the validity of
the specified concepts is unclear. Our method implicitly achieves this by
transitioning to the latent concepts inherent in the model or the images. Our
method can erase a concept within 10 s, making concept erasure more accessible
than ever before. Implicitly transitioning to related concepts leads to more
natural concept erasure. We applied the proposed method to various concepts and
confirmed that concept erasure can be achieved tens to hundreds of times faster
than with current methods. By varying the parameters to be updated, we obtained
results suggesting that, like previous research, knowledge is primarily
accumulated in the feed-forward networks of the text encoder.
