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    BrushEdit: Redefining Image Editing with Interactive Inpainting

    Combining Large Language Models and Image Inpainting for Seamless, User-Friendly Edits

    • Addressing Current Limitations: BrushEdit overcomes challenges in large-scale edits and black-box operations faced by existing editing methods.
    • Interactive Image Editing Made Easy: This framework integrates multimodal language models and dual-branch inpainting to enable intuitive, free-form editing.
    • Setting a New Standard: With superior performance across seven metrics, BrushEdit paves the way for ethical and accessible content creation in image editing.
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    The rapid evolution of diffusion models has redefined text-guided image generation, achieving unprecedented levels of quality, diversity, and alignment with textual guidance. Despite this, image editing—where changes are made to a source image based on specific instructions—remains constrained by technological and practical challenges. Existing methods often falter when handling significant modifications or demand high precision from users, making them less accessible for casual creators.

    BrushEdit emerges as a groundbreaking solution, blending the power of MLLMs with advanced inpainting techniques to offer a seamless, interactive editing experience. This novel approach addresses the limitations of inversion-based and instruction-based methods, introducing a paradigm shift in image manipulation.

    Overcoming Limitations in Existing Methods

    Traditional image editing relies heavily on two primary strategies: inversion-based and instruction-based approaches. Inversion-based methods excel in preserving non-edited regions but struggle with large-scale modifications such as adding or removing objects. These processes are often time-consuming and require precise, high-quality inputs from users. On the other hand, instruction-based methods operate as black boxes, limiting users’ ability to define editing intensity or specific regions effectively.

    BrushEdit’s inpainting-based framework redefines these approaches by enabling intuitive, free-form instructions. Users can specify their desired edits with simplicity while leveraging the advanced capabilities of MLLMs and a dual-branch inpainting model. This cooperative framework facilitates key tasks like editing category classification, mask acquisition, and main object identification, delivering superior results in both precision and coherence.

    Unmatched Performance and Versatility

    Extensive testing of BrushEdit demonstrates its exceptional performance across multiple metrics, including mask region preservation and editing effect coherence. Using benchmarks like PnPBench, BrushBench, and EditBench, BrushEdit has consistently outperformed traditional methods in preserving masked backgrounds and aligning edited images with textual descriptions.

    One of BrushEdit’s standout features is its ability to handle diverse editing instructions, from simple adjustments to complex structural modifications. This adaptability ensures that both novice users and professionals can achieve their desired outcomes with minimal effort, setting a new standard for user-friendly design in image editing tools.

    Challenges

    Despite its impressive capabilities, BrushEdit is not without limitations. The quality of generated content is heavily influenced by the choice of the base model, and irregular mask shapes or misaligned text inputs can occasionally lead to suboptimal results. Future work will focus on addressing these challenges by enhancing model robustness and adaptability.

    Another critical consideration is the ethical implications of image inpainting technology. While BrushEdit offers exciting possibilities for creative expression, it also carries the risk of misuse, such as generating misleading or offensive content. Responsible usage and the establishment of ethical guidelines will be central to BrushEdit’s ongoing development.

    A Leap Forward in Image Editing

    BrushEdit represents a significant advancement in image editing technology, combining the strengths of MLLMs and inpainting models to deliver a user-friendly, interactive experience. By addressing the shortcomings of traditional methods and setting new benchmarks for performance, BrushEdit paves the way for more accessible and versatile editing tools. As future enhancements refine its capabilities and ethical safeguards ensure responsible usage, BrushEdit is poised to become a cornerstone in the next generation of image editing solutions.

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