Adaptive Video Distillation: Mitigating Oversaturation and Temporal Collapse in Few-Step Generation

ArXi:2603.21864v1 Announce Type: cross Video generation has recently emerged as a central task in the field of generative AI. However, the substantial computational cost inherent in video synthesis makes model distillation a critical technique for efficient deployment. Despite its significance, there is a scarcity of methods specifically designed for video diffusion models. Prevailing approaches often directly adapt image distillation techniques, which frequently lead to artifacts such as oversaturation, temporal inconsistency, and mode collapse.