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    Denoising diffusion models excel at image editing by reversing noise addition. This survey categorizes methods, introduces a benchmark (EditEval) with a new metric (LMM Score), and discusses future research directions.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Denoising diffusion models are advanced generative models.
    • They excel in image generation and editing tasks.
    • Their core principle involves reversing a noise-adding process to synthesize high-quality images.

    Purpose of the Study:

    • To provide a comprehensive survey of diffusion models for image editing.
    • To analyze and categorize existing methods based on various criteria.
    • To introduce a novel benchmark and metric for evaluating text-guided image editing.

    Main Methods:

    • Systematic review and categorization of diffusion model-based image editing techniques.
    • Analysis of learning strategies, user-input conditions, and editing task applicability.
    • Development of the EditEval benchmark and the LMM Score metric for text-guided editing evaluation.

    Main Results:

    • Categorization of diffusion model image editing methods across multiple dimensions.
    • Detailed analysis of inpainting and outpainting techniques, comparing traditional and multimodal approaches.
    • Introduction of EditEval and LMM Score for robust evaluation of text-guided image editing.

    Conclusions:

    • Diffusion models offer powerful capabilities for diverse image editing applications.
    • The proposed benchmark and metric provide a standardized approach for performance evaluation.
    • Future research should address current limitations and explore new avenues in diffusion model-based image editing.