How does the choice of LoRA rank in cross-attention layers influence the trade-off between FVD and LPIPS score

Abstract

Abstract: We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities. Second, for memory and training efficiency, we use a window attention architecture tailored for joint spatial and spatiotemporal generative modeling. Taken together these design decisions enable us to achieve state-of-the-art performance on established video (UCF-101 and Kinetics-600) and image

Research Question

How does the choice of LoRA rank in cross-attention layers influence the trade-off between FVD and LPIPS scores across different temporal consistency metrics in Wan2.1 I2V-14B?

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