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Photorealistic Video Generation with Diffusion Models
- Publication Year :
- 2023
-
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 (ImageNet) generation benchmarks without using classifier free guidance. Finally, we also train a cascade of three models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of $512 \times 896$ resolution at $8$ frames per second.<br />Comment: Project website https://walt-video-diffusion.github.io/
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2312.06662
- Document Type :
- Working Paper