Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
This addresses a challenging task in semantic image editing for users needing to add objects seamlessly, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of inserting objects into images based on text instructions without training, achieving state-of-the-art results with over 80% human preference and outperforming supervised methods on benchmarks.
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.