Summary of Videoguide: Improving Video Diffusion Models Without Training Through a Teacher’s Guide, by Dohun Lee et al.
VideoGuide: Improving Video Diffusion Models without Training Through a Teacher’s Guide
by Dohun Lee, Bryan S Kim, Geon Yeong Park, Jong Chul Ye
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces VideoGuide, a novel framework that enhances the temporal consistency of text-to-video (T2V) generation models without requiring additional training or fine-tuning. The method leverages any pretrained video diffusion model (VDM) as a guide during early inference stages, improving temporal quality by interpolating denoised samples from the guiding model into the sampling model’s denoising process. VideoGuide achieves significant improvements in temporal consistency and image fidelity, providing a cost-effective and practical solution that synergizes strengths of various video diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VideoGuide is a new way to make text-to-video generation better by using old video models as guides. This helps keep the story consistent over time and makes the pictures look good too. It’s like having a helpful friend who shows you what to do, instead of trying to figure it out on your own. |
Keywords
» Artificial intelligence » Diffusion model » Fine tuning » Inference
