EcoVideo: Entropy-Orchestrated Video Generation Paradigm in Cloud-Edge Dynamics

DiT video generation is latency-intensive due to iterativefull-frame denoising, while prior cloud-edge methods largely rely on staticinter-step decoupling and cannot leverage inter-frame similarity or adaptto system dynamics. We propose EcoVideo, an entropy-orchestratedframework for dynamic inter-frame decoupling: early-stage self-attentionentropy provides a training-free estimate of frame-wise information den-sity for frame selection; a cloud large model denoises sparse high-entropykeyframes; and an edge lightweight model reconstructs the remainingframes via motion-aware interpolation with refinement for temporal sta-bility. EcoVideo further adapts the keyframe budget and edge refine-ment depth to real-time bandwidth and compute availability, optimizingend-to-end latency under constraints. Experiments on representative DiTvideo generators show improved quality–efficiency trade-offs and up to2.9× end-to-end speedup in low-bandwidth, compute-limited edge set-tings. Code is available at https://github.com/IF-LAB-PKU/EcoVideo.