Yoad Tewel^1,2, Rinon Gal^1,2, Dvir Samuel³, Yuval Atzmon¹, Lior Wolf², Gal Chechik¹

¹NVIDIA • ²Tel Aviv University • ³Bar-Ilan University

Adding objects 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.

This repository contains the official implementation of the Add-it paper, providing tools for seamless object insertion into images using pretrained diffusion models.

conda env create -f environment.yml
conda activate addit

Add-it provides two CLI scripts for different use cases:

Use run_CLI_addit_generated.py to add objects to AI-generated images:

python run_CLI_addit_generated.py \
    --prompt_source "A photo of a cat sitting on the couch" \
    --prompt_target "A photo of a cat wearing a red hat sitting on the couch" \
    --subject_token "hat"

🔴 Required Arguments:

• --prompt_source: Source prompt for generating the base image
• --prompt_target: Target prompt describing the desired edited image
• --subject_token: Single token representing the subject to add (must appear in prompt_target)

🔵 Optional Arguments:

• --output_dir: Directory to save output images (default: "outputs")
• --seed_src: Seed for source generation (default: 6311)
• --seed_obj: Seed for edited image generation (default: 1)
• --extended_scale: Extended attention scale (default: 1.05)
• --structure_transfer_step: Structure transfer step (default: 2)
• --blend_steps: Blend steps (default: [15]). To allow for changes in the input image pass --blend_steps with empty value.
• --localization_model: Localization model (default: "attention_points_sam")
  • Options: attention_points_sam, attention, attention_box_sam, attention_mask_sam, grounding_sam
• --show_attention: Show attention maps using pyplot (flag), will be saved to attn_vis.png.

Use run_CLI_addit_real.py to add objects to existing images:

python run_CLI_addit_real.py \
    --source_image "images/bed_dark_room.jpg" \
    --prompt_source "A photo of a bed in a dark room" \
    --prompt_target "A photo of a dog lying on a bed in a dark room" \
    --subject_token "dog"

🔴 Required Arguments:

• --source_image: Path to the source image (default: "images/bed_dark_room.jpg")
• --prompt_source: Source prompt describing the original image
• --prompt_target: Target prompt describing the desired edited image
• --subject_token: Subject token to add to the image (must appear in prompt_target)

🔵 Optional Arguments:

• --output_dir: Directory to save output images (default: "outputs")
• --seed_src: Seed for source generation (default: 6311)
• --seed_obj: Seed for edited image generation (default: 1)
• --extended_scale: Extended attention scale (default: 1.1)
• --structure_transfer_step: Structure transfer step (default: 4)
• --blend_steps: Blend steps (default: [18]). To allow for changes in the input image pass --blend_steps with empty value.
• --localization_model: Localization model (default: "attention")
  • Options: attention_points_sam, attention, attention_box_sam, attention_mask_sam, grounding_sam
• --use_offset: Use offset in processing (flag)
• --show_attention: Show attention maps using pyplot (flag), will be saved to attn_vis.png.
• --disable_inversion: Disable source image inversion (flag)

You can run Add-it in two interactive modes:

The notebooks contain examples of different prompts and parameters that can be adjusted to control the object insertion process.

• Prompt Design: The --prompt_target should be similar to the --prompt_source, but include a description of the new object to insert
• Seed Variation: Try different values for --seed_obj - some prompts may require a few attempts to get satisfying results
• Localization Models: The most effective --localization_model options are attention_points_sam and attention. Use the --show_attention flag to visualize localization performance
• Object Placement Issues: If the object is not added to the image:
  • Try decreasing --structure_transfer_step
  • Try increasing --extended_scale
• Flexibility: To allow more flexibility in modifying the source image, set --blend_steps to an empty value to send an empty list: []

• 🎉 2025 JUL: Official Add-it implementation is released!
• 📝 2025 APRIL: Paper accepted and presented at ICLR 2025! View on OpenReview

If you make use of our work, please cite our paper:

@inproceedings{
    tewel2025addit,
    title={Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models},
    author={Yoad Tewel and Rinon Gal and Dvir Samuel and Yuval Atzmon and Lior Wolf and Gal Chechik},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={https://openreview.net/forum?id=ZeaTvXw080}
}