ConceptVoid: Precision Multi-Concept Erasure in Generative Video Diffusion

Generative video diffusion models (GVDs) generate high-fidelity, text-conditioned videos but risk producing unsafe or copyrighted content due to training on large, uncurated datasets. Concept erasure techniques aim to remove such harmful concepts from pre-trained models while preserving overall generative performance. However, existing methods mainly target single-concept erasure and thus cannot satisfy the demand for simultaneously eliminating multi-concept in real-world scenarios. On the one hand, naively applying single-concept erasure sequentially to multi-concept often yields suboptimal results due to conflicts among target concepts; on the other hand, methods that alter concept mappings exhibit very poor adaptability and fail to accommodate the dynamic concept changes. To address these, we propose ConceptVoid, a scalable multi-concept erasure framework formulated as a constrained multi-objective optimization problem. For each target concept, an erasure loss is defined as the discrepancy between noise predictions conditioned and unconditioned on the concept. Non-target generation capabilities are preserved via output-distribution alignment regularization. We apply the multiple gradient descent algorithm (MGDA) to obtain Pareto-optimal solutions, aiming to minimize conflicts among different concept erasure objectives. In addition, we improve MGDA by introducing an importance-weighting mechanism, which adjusts the weights of gradients corresponding to each erasure objective, enabling flexible control over the priority and intensity of erasing different concepts, thereby enhancing the scalability of ConceptVoid. Extensive experiments demonstrate the effectiveness of ConceptVoid, validating our key contributions: (1) a scalable framework for multi-concept erasure in GVDs; (2) the integration of per-concept erasure with distribution alignment to retain non-target quality; and (3) an enhanced MGDA for conflict-aware, controllable erasure.