Summary of Guided and Variance-corrected Fusion with One-shot Style Alignment For Large-content Image Generation, by Shoukun Sun et al.
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image Generation
by Shoukun Sun, Min Xian, Tiankai Yao, Fei Xu, Luca Capriotti
First submitted to arxiv on: 17 Dec 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 This paper presents a novel approach to producing large images using small diffusion models. The authors propose Guided Fusion (GF), Variance-Corrected Fusion (VCF), and one-shot Style Alignment (SA) methods to address the noticeable artifacts in existing methods, such as seams and inconsistent objects and styles. The proposed methods improve the quality of generated images significantly, making them suitable for wide application as a plug-and-play module for other fusion-based methods. Denoising Diffusion Probabilistic Model is used, and experiments demonstrate the effectiveness of the proposed methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it possible to create big pictures using small computer models. The problem with current methods is that they can look fake or have weird edges. To fix this, the authors developed new ways to combine these small images into one large picture. They used a model called Denoising Diffusion Probabilistic Model and tested their ideas. The results show that their approach works well and can be used to improve other methods for creating big pictures. |
Keywords
» Artificial intelligence » Alignment » Diffusion » One shot » Probabilistic model
