1. [Publications](/publications)
2. Add-it: Training-Free Object Insertion in Images via Pretrained Diffusion Models
 
 # Add-it: Training-Free Object Insertion in Images via Pretrained Diffusion Models

  ![](/sites/default/files/styles/wide/public/publications/addit.png?itok=JuzmbUKN)

 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.



 ## Authors



[Yoad Tewel](/person/yoad-tewel)

Rinon Gal (NVIDIA)

Dvir Samuel (Bar-Ilan University)

[Yuval Atzmon](/person/yuval-atzmon)

Lior Wolf (Tel Aviv University)

[Gal Chechik](/person/gal-chechik)

 

 

 ## Publication Date



Monday, April 14, 2025

 

 ## Published in



[ICLR 2025](https://iclr.cc/virtual/2025/poster/29207)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Computer Graphics](/research-area/computer-graphics)

 

 

 ## External Links



[ICLR 2025](https://iclr.cc/virtual/2025/poster/29207)