Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis
About
In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention, a mechanism that implicitly propagates diffusion features across frames, ensuring superior temporal coherence and high-fidelity synthesis. Fairy not only addresses limitations of previous models, including memory and processing speed. It also improves temporal consistency through a unique data augmentation strategy. This strategy renders the model equivariant to affine transformations in both source and target images. Remarkably efficient, Fairy generates 120-frame 512x384 videos (4-second duration at 30 FPS) in just 14 seconds, outpacing prior works by at least 44x. A comprehensive user study, involving 1000 generated samples, confirms that our approach delivers superior quality, decisively outperforming established methods.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Video Editing | TGVE benchmark | Pick Score19.8 | 11 | |
| Video Editing | TGVE (test) | ViCLIPout0.208 | 9 | |
| Video Editing | TGVE+ (test) | ViCLIPout0.197 | 9 | |
| Video Editing | Senorita (test) | DVS Score0.4 | 8 | |
| Text-guided Video Editing | Video Editing 4s, 30 FPS, 512p x 384p (test) | Latency (s)13.8 | 3 |