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

ICLR 2025 Conference Submission1318 Authors

Keywords: Diffusion, Editing, Affordance

TL;DR: We presents a method to insert objects into existing images using pretrained diffusion models, guided by text prompts, without additional training.

Abstract: 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.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1318