How does the proposed CAT method compare to fixed tokenization approaches in terms of end-to-end inference tim

Abstract

Abstract: Transformer-based video diffusion models rely on 3D attention over spatial and temporal tokens, which incurs quadratic time and memory complexity and makes end-to-end training for ultra-high-resolution videos prohibitively expensive. To overcome this bottleneck, we propose a pure image adaptation framework that upgrades a video Diffusion Transformer pre-trained at its native scale to synthesize higher-resolution videos. Unfortunately, naively fine-tuning with high-resolution images alone often introduces noticeable noise due to the image-video modality gap. To address this, we decouple the lea

Research Question

How does the proposed CAT method compare to fixed tokenization approaches in terms of end-to-end inference time and token count scaling across different domain shifts in image complexity?

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