Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models

Yoad Tewel, Rinon Gal, Samuel, Lior Wolf, Gal Chechik

Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models: 67 upvotes on Hugging Face Daily Papers, #1 of 16 papers on 2024-11-12. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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