Summary of Accdiffusion V2: Towards More Accurate Higher-resolution Diffusion Extrapolation, by Zhihang Lin et al.
AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion Extrapolation
by Zhihang Lin, Mingbao Lin, Wengyi Zhan, Rongrong Ji
First submitted to arxiv on: 3 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 The proposed AccDiffusion v2 method addresses the issue of object repetition and local distortion in diffusion models when the inference resolution differs from its pre-trained resolution. The authors decouple the vanilla image-content-aware prompt into patch-content-aware prompts, each serving as a precise description of a patch. They also introduce an auxiliary local structural information through ControlNet to mitigate local distortions and dilated sampling with window interaction for better global semantic information. Extensive experiments demonstrate the efficacy of AccDiffusion v2 in image generation extrapolation without training, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AccDiffusion v2 is a new way to make images bigger and better. Right now, when we try to make an image bigger, it can get blurry or repeat parts over and over. This happens because the computer doesn’t understand what’s important in the picture. The authors of this paper came up with a solution by giving the computer more specific instructions for each part of the picture. They also added some extra information to help the computer avoid mistakes. The result is better, bigger images without training the computer beforehand. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference » Prompt
