Add-it: Training-Free Object Insertion in Images With Pretrained
  Diffusion Models
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.
6732f723e6a45b6a0b3143fd
Yoad Tewel
claimed_verified
2024-11-15T09:26:31.956Z
6201838f305742efd5a6a0e3
/avatars/4e47ab9ef1a4b9f59756a6d8c90a970f.svg
Yoad Tewel
user
YoadTew
6732f723e6a45b6a0b3143fe
Rinon Gal
6732f723e6a45b6a0b3143ff
Dvir Samuel Yuval Atzmon
claimed_verified
2025-03-25T09:05:46.128Z
630f0d48982455e61cc4cc08
/avatars/eea6ed2e112e830effa98a4661c5474f.svg
Samuel
user
Dvir
6732f723e6a45b6a0b314400
Lior Wolf
6732f723e6a45b6a0b314401
Gal Chechik
Add-it: Training-Free Object Insertion in Images With Pretrained
  Diffusion Models
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.
Add-it extends diffusion models with a weighted extended-attention mechanism to seamlessly integrate objects into images based on text instructions, achieving state-of-the-art results on real and generated image insertion benchmarks.
diffusion models extended-attention mechanism semantic image editing object placement Additing Affordance Benchmark