LoMOE: Localized Multi-Object Editing via Multi-Diffusion
(2403.00437)Abstract
Recent developments in the field of diffusion models have demonstrated an exceptional capacity to generate high-quality prompt-conditioned image edits. Nevertheless, previous approaches have primarily relied on textual prompts for image editing, which tend to be less effective when making precise edits to specific objects or fine-grained regions within a scene containing single/multiple objects. We introduce a novel framework for zero-shot localized multi-object editing through a multi-diffusion process to overcome this challenge. This framework empowers users to perform various operations on objects within an image, such as adding, replacing, or editing $\textbf{many}$ objects in a complex scene $\textbf{in one pass}$. Our approach leverages foreground masks and corresponding simple text prompts that exert localized influences on the target regions resulting in high-fidelity image editing. A combination of cross-attention and background preservation losses within the latent space ensures that the characteristics of the object being edited are preserved while simultaneously achieving a high-quality, seamless reconstruction of the background with fewer artifacts compared to the current methods. We also curate and release a dataset dedicated to multi-object editing, named $\texttt{LoMOE}$-Bench. Our experiments against existing state-of-the-art methods demonstrate the improved effectiveness of our approach in terms of both image editing quality and inference speed.
Overview
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LoMOE introduces a novel approach for high-fidelity, zero-shot localized editing of multiple objects within images, overcoming limitations of current generative models.
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It leverages a pre-trained diffusion model within a multi-diffusion framework, enabling precise regional editing and preservation of original image attributes.
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The introduction of the LoMOE-Bench dataset offers a dedicated benchmark for evaluating multi-object editing performance.
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LoMOE demonstrates superior performance across various metrics, including edit fidelity, image quality, and inference speed, while indicating the importance of ethical considerations in image editing technology.
Enhancing Multi-Object Image Editing with LoMOE: A Localized Multi-Object Editing Framework
Introduction to LoMOE
The recent advent of diffusion models has significantly improved the capabilities of generative models in producing photorealistic images, conditioned on textual prompts. Despite these advancements, accurately applying edits to multiple objects within an image, particularly with detailed spatial and relational context, remains a considerable challenge. To address this, we introduce a novel approach named Localized Multi-Object Editing (LoMOE), which is formulated to enable high-fidelity, zero-shot localized editing of multiple objects within a single image pass. This method is not only capable of editing precise regions designated by masks but also enhances the quality and efficiency of the editing process as compared to existing state-of-the-art frameworks.
Methodological Overview
LoMOE operates by leveraging a pre-trained diffusion model, utilizing it within a multi-diffusion framework tailored for localized editing. This approach encompasses several key components:
- Inversion for Editing: Utilizing latent code inversion for establishing a starting point for edits, ensuring the preservation of the original image composition.
- Multi-Diffusion for Localized Editing: Implementing a localized prompting strategy, allowing for accurate editing within specified regions defined by masks.
- Attribute and Background Preservation: Employing losses that ensure fidelity to both the edited object's attributes and the image's background, facilitating seamless integration of the edits into the original scene.
The LoMOE framework demonstrates significant improvements over baseline methods in terms of edit fidelity, image quality, and inference efficiency, facilitating multiple edits within single iterative passes.
The LoMOE-Bench Dataset
Recognizing the need for a dedicated benchmark for evaluating multi-object editing performance, we introduce LoMOE-Bench. This dataset is meticulously curated to encompass a wide array of editing scenarios, specifically designed to challenge and assess the performance of multi-object editing frameworks. It constitutes a valuable resource for researchers seeking to advance the state of the art in localized image editing.
Experimental Insights
Our comprehensive evaluation of LoMOE, against existing state-of-the-art methods, reveals its superior performance across a range of metrics. It not only demonstrates high-quality image edits but also exhibits notable improvements in terms of inference speed, attributed to its unique approach of effectuating multiple edits in a single pass. These achievements underscore LoMOE's capabilities in enhancing the practicality and applicability of localized multi-object editing tasks.
Future Directions and Ethical Considerations
While LoMOE represents a notable advancement in the domain of image editing, it also opens up various avenues for future exploration, including the refinement of object deletion and swapping techniques. It is imperative to acknowledge the potential ethical implications associated with generative editing technologies. The research community must remain vigilant, ensuring that these powerful tools are used responsibly, with ongoing efforts to mitigate risks related to privacy, misinformation, and the potential for abuse.
Conclusion
LoMOE sets a new benchmark in the realm of localized multi-object image editing, presenting a robust framework that significantly enhances edit quality and efficiency. Through the introduction of the LoMOE-Bench dataset, it also provides a foundational platform for future research initiatives aimed at advancing image editing technologies. As we move forward, it remains crucial to balance innovation with ethical responsibility, ensuring the positive impact of these advancements on society.
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