Summary of Snapgen-v: Generating a Five-second Video Within Five Seconds on a Mobile Device, by Yushu Wu et al.
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device
by Yushu Wu, Zhixing Zhang, Yanyu Li, Yanwu Xu, Anil Kag, Yang Sui, Huseyin Coskun, Ke Ma, Aleksei Lebedev, Ju Hu, Dimitris Metaxas, Yanzhi Wang, Sergey Tulyakov, Jian Ren
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
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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 The proposed comprehensive acceleration framework enables edge users to harness the power of large-scale video diffusion models for cinematic and high-resolution video generation from arbitrary input prompts. By initializing with a compact image backbone and searching for efficient temporal layer design, the model achieves hardware efficiency. The authors also introduce a dedicated adversarial fine-tuning algorithm to reduce denoising steps to 4, resulting in a lightweight model with only 0.6B parameters that can generate a 5-second video on an iPhone within 5 seconds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for anyone to create high-quality videos using just their phone! The researchers developed a way to make big video generation models smaller and faster, so people without super powerful computers or special servers can still use them. They did this by starting with a small image-based model and adding special layers that work well on phones. This means you can generate a 5-second video on your iPhone in just 5 seconds! |
Keywords
» Artificial intelligence » Diffusion » Fine tuning
