  With the advancement of computer vision and natural language processing,
text-to-video generation, enabled by text-to-video diffusion models, has become
more prevalent. These models are trained using a large amount of data from the
internet. However, the training data often contain copyrighted content,
including cartoon character icons and artist styles, private portraits, and
unsafe videos. Since filtering the data and retraining the model is
challenging, methods for unlearning specific concepts from text-to-video
diffusion models have been investigated. However, due to the high computational
complexity and relative large optimization scale, there is little work on
unlearning methods for text-to-video diffusion models. We propose a novel
concept-unlearning method by transferring the unlearning capability of the text
encoder of text-to-image diffusion models to text-to-video diffusion models.
Specifically, the method optimizes the text encoder using few-shot unlearning,
where several generated images are used. We then use the optimized text encoder
in text-to-video diffusion models to generate videos. Our method costs low
computation resources and has small optimization scale. We discuss the
generated videos after unlearning a concept. The experiments demonstrates that
our method can unlearn copyrighted cartoon characters, artist styles, objects
and people's facial characteristics. Our method can unlearn a concept within
about 100 seconds on an RTX 3070. Since there was no concept unlearning method
for text-to-video diffusion models before, we make concept unlearning feasible
and more accessible in the text-to-video domain.
