Papers › Grid Diffusion Models for Text-to-Video Generation
Grid Diffusion Models for Text-to-Video Generation
Taegyeong Lee, Soyeong Kwon, Taehwan Kim
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Generation | UCF-101 | GridDiff (Zero-shot) | FVD16 | 340.0 | #27 of 48 | Archive leaderboard | report |
| Video Generation | UCF-101 | GridDiff (Zero-shot) | Inception Score | 62.88 | #27 of 48 | Archive leaderboard | report |
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