Summary of Accdiffusion: An Accurate Method For Higher-resolution Image Generation, by Zhihang Lin et al.
AccDiffusion: An Accurate Method for Higher-Resolution Image Generation
by Zhihang Lin, Mingbao Lin, Meng Zhao, Rongrong Ji
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 AccDiffusion method tackles the problem of object repetition in patch-wise higher-resolution image generation without requiring additional training data. By decoupling the vanilla prompt into patch-content-aware prompts, each serving as a precise description of an image patch, AccDiffusion effectively addresses repeated object generation. Additionally, dilated sampling with window interaction is introduced to maintain global consistency in generated images. Experimental results demonstrate improved performance and reduced repetition compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem where generated images keep repeating the same objects. To fix this, the researchers propose a new method called AccDiffusion that doesn’t need extra training data. They break down the original prompt into smaller prompts for each part of the image, making it more accurate. This helps to get rid of repeated objects and generates better-looking high-resolution images. |
Keywords
» Artificial intelligence » Image generation » Prompt
