title: Add-it: Training-Free Object Insertion in Images With Pretrained
    Diffusion Models
summary: 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.
paper_authors._id: 6732f723e6a45b6a0b3143fd
paper_authors.name: Yoad Tewel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:26:31.956Z
paper_authors._id: 6201838f305742efd5a6a0e3
paper_authors.avatarUrl: /avatars/4e47ab9ef1a4b9f59756a6d8c90a970f.svg
paper_authors.fullname: Yoad Tewel
paper_authors.type: user
paper_authors.user: YoadTew
paper_authors._id: 6732f723e6a45b6a0b3143fe
paper_authors.name: Rinon Gal
paper_authors._id: 6732f723e6a45b6a0b3143ff
paper_authors.name: Dvir Samuel Yuval Atzmon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T09:05:46.128Z
paper_authors._id: 630f0d48982455e61cc4cc08
paper_authors.avatarUrl: /avatars/eea6ed2e112e830effa98a4661c5474f.svg
paper_authors.fullname: Samuel
paper_authors.type: user
paper_authors.user: Dvir
paper_authors._id: 6732f723e6a45b6a0b314400
paper_authors.name: Lior Wolf
paper_authors._id: 6732f723e6a45b6a0b314401
paper_authors.name: Gal Chechik
paper_title: Add-it: Training-Free Object Insertion in Images With Pretrained
    Diffusion Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: diffusion models extended-attention mechanism semantic image editing object placement Additing Affordance Benchmark