Summary of Add-it: Training-free Object Insertion in Images with Pretrained Diffusion Models, by Yoad Tewel et al.
Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
by Yoad Tewel, Rinon Gal, Dvir Samuel, Yuval Atzmon, Lior Wolf, Gal Chechik
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 In semantic image editing, adding objects to images based on text instructions is a challenging task requiring a balance between preserving the original scene and integrating the new object seamlessly. Existing models struggle with this balance, especially in complex scenes. We introduce Add-it, a training-free approach that extends diffusion models’ attention mechanisms by incorporating information from the scene image, text prompt, and generated image itself. Our weighted extended-attention mechanism maintains structural consistency while ensuring natural object placement. Add-it achieves state-of-the-art results on real and generated image insertion benchmarks, including our newly constructed “Additing Affordance Benchmark,” outperforming supervised methods. Human evaluations show that Add-it is preferred in over 80% of cases, with improvements in various automated metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine adding objects to pictures based on text descriptions. This task is tricky because you need to balance keeping the original scene and fitting the new object naturally. Existing models struggle with this. We created a new approach called Add-it that doesn’t require training and uses information from three sources: the picture, the text, and the generated image. Our method maintains details and ensures the object looks like it belongs in the scene. Add-it performs better than other methods on tests, including one we designed to evaluate how well objects are placed. People prefer our approach by a large margin, and it also does better according to automated metrics. |
Keywords
» Artificial intelligence » Attention » Diffusion » Prompt » Supervised
