AI RESEARCH
TeDiO: Temporal Diagonal Optimization for Training-Free Coherent Video Diffusion
arXiv CS.CV
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ArXi:2605.14136v1 Announce Type: new Recent text-to-video diffusion transformers generate visually compelling frames, yet still struggle with temporal coherence, often producing flickering, drifting, or unstable motion. We show that these failures leave a clear imprint inside the model: incoherent videos consistently exhibit irregular, fragmented temporal diagonals in their intermediate self-attention maps, whereas stable motion corresponds to smooth, band-diagonal patterns. Building on this observation, we.