Towards Chunk-Wise Generation for Long Videos
This addresses GPU memory constraints for researchers and practitioners in video generation, but it is incremental as it builds on existing diffusion models.
The paper tackles the challenge of generating long-duration videos by proposing an autoregressive chunk-by-chunk strategy to avoid out-of-memory issues, and it designs an efficient k-step search solution to mitigate problems from applying short image-to-video models.
Generating long-duration videos has always been a significant challenge due to the inherent complexity of spatio-temporal domain and the substantial GPU memory demands required to calculate huge size tensors. While diffusion based generative models achieve state-of-the-art performance in video generation task, they are typically trained with predefined video resolutions and lengths. During inference, a noise tensor with specific resolution and length should be specified at first, and the model will perform denoising on the entire video tensor simultaneously, all the frames together. Such approach will easily raise an out-of-memory (OOM) problem when the specified resolution and/or length exceed a certain limit. One of the solutions to this problem is to generate many short video chunks autoregressively with strong inter-chunk spatio-temporal relation and then concatenate them together to form a long video. In this approach, a long video generation task is divided into multiple short video generation subtasks, and the cost of each subtask is reduced to a feasible level. In this paper, we conduct a detailed survey on long video generation with the autoregressive chunk-by-chunk strategy. We address common problems caused by applying short image-to-video models to long video tasks and design an efficient $k$-step search solution to mitigate these problems.