Summary of Animatediff-lightning: Cross-model Diffusion Distillation, by Shanchuan Lin et al.
AnimateDiff-Lightning: Cross-Model Diffusion Distillation
by Shanchuan Lin, Xiao Yang
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Our research paper introduces AnimateDiff-Lightning, a novel approach to video generation that achieves state-of-the-art results in few-step video synthesis. By applying progressive adversarial diffusion distillation, we’ve made significant improvements in video quality and efficiency. The model is designed for the video modality, with modifications allowing it to adapt to this specific domain. Additionally, we propose a new technique to simultaneously distill the probability flow of multiple base diffusion models, resulting in a single distilled motion module that can generate videos with broader style compatibility. We’re excited to share our AnimateDiff-Lightning model with the community for use and further development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a way to create realistic-looking videos quickly. Our method uses something called progressive adversarial diffusion distillation, which is new and improved. This technology can make videos in just a few steps, while still looking very good. We’re sharing our model with others so they can use it too. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Probability
