arXivVideo AI
Breaking the Uniformity Trap: Scaling Video Diffusion Models via SplitMoE
突破均勻分佈陷阱：透過 SplitMoE 解決影片擴散模型的擴展瓶頸
This study introduces SplitMoE, a split-role sparse architecture that bifurcates the expert pool into semantic and generic experts, overcoming the "uniformity trap" of traditional MoEs to prevent visual fragmentation in video diffusion.
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