Training-Free Conflict-Aware Attribute Forgetting for Customized Video Generation

Customized video generation aims to preserve personalized subject characteristics from reference images while generating dynamics aligned with text prompts. Existing methods typically apply reference conditioning uniformly throughout generation, implicitly assuming that all reference attributes remain valid for the target event. When a prompt requires an attribute to change, reference and textual conditions impose incompatible attribute states, resulting in attribute conflict. To address this conflict, we propose AttriForget, a training-free framework for conflict-aware attribute forgetting that constructs an event-conditioned attribute graph to model the validity of reference attributes under the target event. Guided by this graph, AttriForget probes orthogonal attribute-sensitive subspaces in reference features to preserve information associated with persistent attributes while "forgetting" reference constraints on evolving attributes, turning conflicting reference and textual conditions into complementary guidance. Furthermore, AttriForget can be integrated into various Diffusion Transformer architectures without additional training, extending global reference control to fine-grained attribute-level reference control. Experiments on OpenS2V-Eval across four subject-customized video generation backbones show that AttriForget consistently improves total scores and video naturalness while enhancing subject consistency on most evaluated settings, with an average inference latency increase of only 4% across multiple backbones. Code will be made publicly available.