Summary of Diffeditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing, by Chong Mou et al.
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing
by Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Large-scale text-to-image (T2I) diffusion models have transformed image generation, but fine-grained image editing remains a challenge. Our proposed DiffEditor addresses two weaknesses: inaccurate editing results and lack of flexibility in harmonizing editing operations. We introduce image prompts to better describe editing content, combining stochastic differential equations (SDEs) with ordinary differential equation (ODE) sampling for local flexibility while maintaining consistency. Additionally, we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling to improve editing quality. Our method achieves state-of-the-art performance on various fine-grained image editing tasks, including single-image edits and cross-image edits. Our source code is released at https://github.com/MC-E/DragonDiffusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make big changes to images using computers. Right now, we can generate new images, but it’s hard to edit existing ones accurately and easily. The authors suggest a new way called DiffEditor that makes editing more precise and flexible. They use special math equations to help the computer understand what to change in the image. This method works really well for making small changes like moving objects or changing colors. It can even work across multiple images, replacing one object with another. You can find the code used to create this method on a website. |
Keywords
* Artificial intelligence * Diffusion * Image generation
