For video diffusion transformers fine-tuned on high-resolution images, how does CAT's token allocation strateg

Abstract

Abstract: We present a practical pipeline for fine-tuning open-source video diffusion transformers to synthesize cinematic scenes for television and film production from small datasets. The proposed two-stage process decouples visual style learning from motion generation. In the first stage, Low-Rank Adaptation (LoRA) modules are integrated into the cross-attention layers of the Wan2.1 I2V-14B model to adapt its visual representations using a compact dataset of short clips from Ay Yapim's historical television film El Turco. This enables efficient domain transfer within hours on a single GPU. In the sec

Research Question

For video diffusion transformers fine-tuned on high-resolution images, how does CAT's token allocation strategy affect end-to-end inference time and output quality compared to uniform tokenization under different spatial-temporal redundancy levels, measured by throughput and LPIPS on UHD benchmarks?

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