Hierarchical Patch Diffusion Models for High-Resolution Video Generation

• URL: http://arxiv.org/abs/2406.07792v1
• Date: Wed, 12 Jun 2024 01:12:53 GMT
• Title: Hierarchical Patch Diffusion Models for High-Resolution Video Generation
• Authors: Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin, Sergey Tulyakov,
• Abstract summary: We develop deep context fusion, which propagates context information from low-scale to high-scale patches in a hierarchical manner. We also propose adaptive computation, which allocates more network capacity and computation towards coarse image details. The resulting model sets a new state-of-the-art FVD score of 66.32 and Inception Score of 87.68 in class-conditional video generation.
• Score: 50.42746357450949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have demonstrated remarkable performance in image and video synthesis. However, scaling them to high-resolution inputs is challenging and requires restructuring the diffusion pipeline into multiple independent components, limiting scalability and complicating downstream applications. This makes it very efficient during training and unlocks end-to-end optimization on high-resolution videos. We improve PDMs in two principled ways. First, to enforce consistency between patches, we develop deep context fusion -- an architectural technique that propagates the context information from low-scale to high-scale patches in a hierarchical manner. Second, to accelerate training and inference, we propose adaptive computation, which allocates more network capacity and computation towards coarse image details. The resulting model sets a new state-of-the-art FVD score of 66.32 and Inception Score of 87.68 in class-conditional video generation on UCF-101 $256^2$, surpassing recent methods by more than 100%. Then, we show that it can be rapidly fine-tuned from a base $36\times 64$ low-resolution generator for high-resolution $64 \times 288 \times 512$ text-to-video synthesis. To the best of our knowledge, our model is the first diffusion-based architecture which is trained on such high resolutions entirely end-to-end. Project webpage: https://snap-research.github.io/hpdm.

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