Summary of Coloredit: Training-free Image-guided Color Editing with Diffusion Model, by Xingxi Yin et al.
ColorEdit: Training-free Image-Guided Color editing with diffusion model
by Xingxi Yin, Zhi Li, Jingfeng Zhang, Chenglin Li, Yin Zhang
First submitted to arxiv on: 15 Nov 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 proposes a new approach to modifying the color of objects in images using text-to-image diffusion models. The authors identify limitations in existing methods, where attention leakage and collision can lead to misalignment between the resulting image and the text prompt. By analyzing the process of text-guided image synthesizing, they find that visual representations are determined early on in the denoising process and that color adjustment can be achieved through value matrices alignment. Building on these insights, they develop a straightforward yet effective method for object-level color editing without requiring additional fine-tuning or training. The authors also introduce COLORBENCH, a benchmark dataset to evaluate the performance of color change methods. Experimental results demonstrate the effectiveness of their approach in both synthesized and real images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers generate images based on text prompts. It shows that current methods can have problems when trying to change the color of an object in an image. The authors studied what happens during this process and found ways to improve it. They developed a new method for changing the color of objects without needing extra training or fine-tuning. They also created a special dataset, COLORBENCH, to help test how well different methods can change colors. Overall, the paper shows that computers can do a better job of changing image colors with these improvements. |
Keywords
» Artificial intelligence » Alignment » Attention » Diffusion » Fine tuning » Prompt
