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Diffusion models can finally produce temporally stable video fusion by reframing the task as history-conditioned motion generation, sidestepping the limitations of optical flow and frame-by-frame processing.
Decentralized training can not only match, but *beat* monolithic training for video generation, opening the door to cheaper, more accessible video foundation models.
