T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models
This addresses misuse and rights violation risks in text-to-video generation, offering a domain-specific safety improvement.
The paper tackles the problem of text-to-video diffusion models generating explicit or harmful content by proposing an unlearning-based concept erasing method, which effectively erases specific concepts while preserving generation capability for others, outperforming existing methods.
Recent advances in text-to-video (T2V) diffusion models have significantly enhanced the quality of generated videos. However, their capability to produce explicit or harmful content introduces new challenges related to misuse and potential rights violations. To address this newly emerging threat, we propose unlearning-based concept erasing as a solution. First, we adopt negatively-guided velocity prediction fine-tuning and enhance it with prompt augmentation to ensure robustness against prompts refined by large language models (LLMs). Second, to achieve precise unlearning, we incorporate mask-based localization regularization and concept preservation regularization to preserve the model's ability to generate non-target concepts. Extensive experiments demonstrate that our method effectively erases a specific concept while preserving the model's generation capability for all other concepts, outperforming existing methods. We provide the unlearned models in \href{https://github.com/VDIGPKU/T2VUnlearning.git}{https://github.com/VDIGPKU/T2VUnlearning.git}.