让文本生成视频模型删除特定概念，防止生成有害内容。
T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models
- 用负面引导+提示增强训练，让模型抗住复杂指令攻击。
- 加入掩码定位和概念保留正则，精准擦除目标概念不伤其他能力。
- 适合需要可控内容生成的AI视频平台或安全审查场景。
文本到视频（T2V）扩散模型虽显著提升了生成质量，但其生成敏感或有害内容的能力带来了滥用与权利侵犯风险。为此，我们提出基于遗忘学习的概念擦除方法。首先，采用负向引导的速度预测微调，并结合提示增强，提升对大语言模型优化后提示的鲁棒性；其次，为实现精确遗忘，引入基于掩码的定位正则化与概念保留正则化，确保模型仍可生成非目标概念的内容。大量实验表明，该方法能有效擦除特定概念，同时保持对其他概念的生成能力，优于现有方法。相关未学习模型已开源至 https://github.com/VDIGPKU/T2VUnlearning.git。
原文摘要 · Abstract (English)
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}.
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