Summary of Cutdiffusion: a Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method, by Mingbao Lin et al.
CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method
by Mingbao Lin, Zhihang Lin, Wengyi Zhan, Liujuan Cao, Rongrong Ji
First submitted to arxiv on: 23 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 The proposed CutDiffusion method simplifies and accelerates the diffusion extrapolation process for large pre-trained low-resolution models, enhancing adaptability. By cutting a standard patch diffusion process into two phases – comprehensive structure denoising and specific detail refinement – CutDiffusion achieves fast inference speed, cheap GPU cost, and strong generation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CutDiffusion is a new way to make existing low-resolution models work better at higher resolutions. It’s faster, cheaper, and makes better pictures. This is because it has two parts: one that gets the overall structure right, and another that adds in extra details. This helps make the process more efficient and produces better results. |
Keywords
» Artificial intelligence » Diffusion » Inference
