Grid Diffusion Models for Text-to-Video Generation

Taegyeong Lee, Soyeong Kwon, Taehwan Kim; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 8734-8743

Recent advances in the diffusion models have significantly improved text-to-image generation. However generating videos from text is a more challenging task than generating images from text due to the much larger dataset and higher computational cost required. Most existing video generation methods use either a 3D U-Net architecture that considers the temporal dimension or autoregressive generation. These methods require large datasets and are limited in terms of computational costs compared to text-to-image generation. To tackle these challenges we propose a simple but effective novel grid diffusion for text-to-video generation without temporal dimension in architecture and a large text-video paired dataset. We can generate a high-quality video using a fixed amount of GPU memory regardless of the number of frames by representing the video as a grid image. Additionally since our method reduces the dimensions of the video to the dimensions of the image various image-based methods can be applied to videos such as text-guided video manipulation from image manipulation. Our proposed method outperforms the existing methods in both quantitative and qualitative evaluations demonstrating the suitability of our model for real-world video generation.