Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models (2411.07232v2)

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.

• The paper presents Add-it, a novel method that inserts objects using a weighted extended-attention mechanism without further training.
• It leverages structure transfer and Subject-Guided Latent Blending to maintain image consistency and realism.
• Add-it achieved an 83% improvement in affordance scores and outperformed supervised methods in human preference studies.

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

The paper under discussion introduces Add-it, a method for inserting objects into images based on text instructions. This approach innovatively employs pre-trained diffusion models and requires no further training or fine-tuning, distinguishing it from previous methods in the domain of text-based image editing.

Methodological Advances

Add-it leverages the attention mechanism inherent in the diffusion transformers' models to achieve its objectives. The authors extend these models to include a novel weighted extended-attention mechanism. This modification balances the attention between the structural details of the source image, a simple textual prompt, and the image itself. The mechanism adapts the structural and semantic context from the pre-trained diffusion model, allowing precise and plausible object placement without extensive training datasets or task-specific models.

In addition to the attention mechanism, Add-it introduces a structure transfer step which ensures the generated object's seamless integration with the existing image context, maintaining overall consistency and aesthetic appeal. Furthermore, the Subject-Guided Latent Blending mechanism effectively preserves details from the source image that do not require modification, such as shadows and reflections, thereby enhancing realism and image fidelity.

Benchmarks and Evaluation

The efficacy of Add-it is demonstrated across diverse benchmarks. Particularly, the paper introduces the "Additing Affordance Benchmark", designed to evaluate the plausibility of object placement. Add-it achieves an 83% improvement in affordance scores, surpassing several existing supervised methods. Moreover, the authors bolster their evaluation with human preference studies, showing Add-it's superior performance in over 80% of cases. Such empirical results underscore the method's capability to balance semantic understanding and image integrity while performing object addition tasks.

Implications and Future Developments

This paper signals significant practical and theoretical implications. On the practical side, Add-it allows content creators and graphics professionals to add objects to images effortlessly without the needing extensive training resources. Theoretically, the research challenges the notion that training and fine-tuning are prerequisites for successfully modifying image content, suggesting that suitably designed zero-shot methods can rival, if not surpass, task-specific models.

Looking forward, the foundational principles of Add-it might inspire future explorations into seamless object insertions and modifications in dynamic or multi-modal settings, advancing areas like synthetic data generation, gaming, and simulation. Further research could explore the integration of these methodologies into real-time image editing applications, enhancing interactive AI systems with improved contextual and semantic awareness.

In summary, Add-it presents a proficient, training-free method for object insertion by exploiting inherent capabilities in pre-trained diffusion models. It sets a precedent for future methodological advancements in AI-driven image editing, prioritizing model ingenuity over data-centric approaches.