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 1 | # Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
 2 | 
 3 | [![arXiv](https://img.shields.io/badge/arXiv-2411.07232-b31b1b.svg)](https://arxiv.org/abs/2411.07232)
 4 | 
 5 | ### [[Project Website](https://research.nvidia.com/labs/par/addit/)]
 6 | 
 7 | > **Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models**<br>
 8 | > Yoad Tewel<sup>1,2</sup>, Rinon Gal<sup>1,2</sup>, Dvir Samuel<sup>3</sup>, Yuval Atzmon<sup>1</sup>, Lior Wolf<sup>2</sup>, Gal Chechik<sup>1</sup> <br>
 9 | > <sup>1</sup>NVIDIA, <sup>2</sup>Tel Aviv University, <sup>3</sup>Bar-Ilan University
10 | 
11 | ![](https://research.nvidia.com/labs/par/addit/static/images/Teaser.png)
12 | 
13 | >**Abstract**: <br>
14 | > Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integrating the new object in a fitting location. Despite extensive efforts, existing models often struggle with this balance, particularly with finding a natural location for adding an object in complex scenes. We introduce Add-it, a training-free approach that extends diffusion models' attention mechanisms to incorporate information from three key sources: the scene image, the text prompt, and the generated image itself. Our weighted extended-attention mechanism maintains structural consistency and fine details while ensuring natural object placement. Without task-specific fine-tuning, Add-it achieves state-of-the-art results on both real and generated image insertion benchmarks, including our newly constructed "Additing Affordance Benchmark" for evaluating object placement plausibility, outperforming supervised methods. Human evaluations show that Add-it is preferred in over 80% of cases, and it also demonstrates improvements in various automated metrics.
15 | 
16 | ## Description
17 | This repo will contain the official code for our Add-it paper.
18 | 
19 | ## News
20 | - **2024 Nov 11**: The project page and arXiv paper for Add-it are now live. We are actively working on making the code available as soon as possible!
21 | 
22 | ## TODO:
23 | - [] Release code.
24 | 
25 | ## Citation
26 | If you make use of our work, please cite our paper:
27 | 
28 | ```
29 | @misc{tewel2024addit,
30 |     title={Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models},
31 |     author={Yoad Tewel and Rinon Gal and Dvir Samuel and Yuval Atzmon and Lior Wolf and Gal Chechik},
32 |     year={2024},
33 |     eprint={2411.07232},
34 |     archivePrefix={arXiv},
35 |     primaryClass={cs.CV}
36 | }
37 | ```


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