LongLive-Plug: Once-for-All Distillation for Video Generation
Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillation stage, for example to accelerate sampling or to improve long-video generation. This stage is typically repeated for every specialized model. We introduce LongLive-Plug, a once-for-all distillation framework that learns reusab
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Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillation stage, for example to accelerate sampling or to improve long-video generation. This stage is typically repeated for every specialized model. We introduce LongLive-Plug, a once-for-all distillation framework that learns reusab
This work introduces a 'once-for-all' distillation approach for video generation, which significantly reduces the need for repeated distillation stages for specialized models. For ML engineers, this means more efficient model development and deployment, especially in multimodal domains where specialized models are common.