Hierarchical patch-wise diffusion models for high-resolution video generation

Abstract

Hierarchical patch-wise diffusion models (HPDMs) use a diffusion paradigm that learns a hierarchical distribution of patches instead of whole videos for efficient patch-wise training of diffusion models. To enforce consistency between the patches, deep context fusion may be used to propagate the context information from low-scale to high-scale patches in a hierarchical manner. To accelerate patch-wise training and inference, adaptive computation also may be used to allocate more computational resources and network capacity towards coarse image details and to cheapen synthesis of high-frequency texture details. All the processing stages are jointly trained to provide spatially aligned global context to the higher levels of the cascade. As a result, the model does not operate on the full-resolution inputs, which allows the model to be trained on high-resolution video datasets in an end-to-end fashion.