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Video diffusion transformers (DiTs) increasingly adopt mixture-of-experts (MoE) architectures to reduce active computation, but their full expert storage remains costly. Existing one-shot pruning criteria mainly rely on static activation or routing statistics and cannot capture layer-level re-routing after expert delet...
This work proposes Visual In-context Editing, a new paradigm elevating video editing from textual instructions to multi-modal visual guidance encompassing single image, image pair, and video pair, and curates VicEdit-400K, the first large-scale dataset for visual in-context video editing.
This work presents a synchronization-aware acceleration framework for efficient audio-visual generation by explicitly accounting for cross-modal dependence during acceleration, and improves inference efficiency while keeping video quality, audio quality, and audio-video synchronization.
This work proposes Cycle-World, a novel framework designed for stable and temporally consistent long-video generation that tackles error drift by enforcing strict temporal reversibility across both the training and inference phases, and demonstrates that forward generative drift can be strictly bottlenecked by a cycle-...
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