Summary of Upsample Guidance: Scale Up Diffusion Models Without Training, by Juno Hwang et al.
Upsample Guidance: Scale Up Diffusion Models without Training
by Juno Hwang, Yong-Hyun Park, Junghyo Jo
First submitted to arxiv on: 2 Apr 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 A novel technique called upsample guidance is introduced in this paper, which enables pre-trained diffusion models to generate high-resolution images without requiring additional training or external models. This approach builds upon existing architectures by adding a single term to the sampling process, allowing for direct utilization of pre-trained models. The proposed method demonstrates superior performance across various generative tasks, including image and video generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program that can create new images or videos by learning from existing ones. This program is called a “diffusion model.” Right now, these programs are really good at making small changes to existing images, but they struggle when it comes to creating brand new, high-quality images. The authors of this paper have found a way to make these programs better by adding just one simple step to the process. This step is called “upsample guidance,” and it lets the program create much higher-quality images without needing any extra training or help from other programs. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
