Summary of Vision-xl: High Definition Video Inverse Problem Solver Using Latent Image Diffusion Models, by Taesung Kwon et al.
VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models
by Taesung Kwon, Jong Chul Ye
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 This paper proposes a novel framework for solving high-definition video inverse problems using latent image diffusion models. Building on recent advancements, the approach leverages latent-space diffusion models to achieve enhanced video quality and resolution. To address computational demands, the authors introduce a pseudo-batch consistent sampling strategy, allowing efficient operation on a single GPU. Additionally, they present pseudo-batch inversion, an initialization technique that incorporates informative latents from the measurement. The framework integrates with SDXL, achieving state-of-the-art video reconstruction across various spatio-temporal inverse problems, including deblurring, super-resolution, and inpainting. It supports multiple aspect ratios and delivers HD-resolution reconstructions in under 6 seconds per frame on a single NVIDIA 4090 GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to improve old videos by making them look better and clearer. They use special models that can make the video more detailed and higher quality. To make it work, they had to find ways to make the computer process the information faster. They also came up with a new way to start the process, which helps the computer understand what the video should look like. The new method is very good at fixing old videos and can even handle different shapes and sizes of videos. It’s fast too, taking only a few seconds to improve each frame. |
Keywords
» Artificial intelligence » Diffusion » Latent space » Super resolution
