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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Diffusion models, particularly text-to-image (T2I) models, have advanced visual generation.
    • Text-only conditioning limits T2I model applicability in diverse scenarios.
    • Controlling T2I models with novel conditions is an active research area.

    Purpose of the Study:

    • To provide a comprehensive survey of controllable generation with T2I diffusion models.
    • To categorize existing research based on conditioning strategies.
    • To analyze control mechanisms and representative methods.

    Main Methods:

    • Introduction to denoising diffusion probabilistic models (DDPMs) and T2I diffusion models.
    • Categorization of controllable generation into specific, multiple, and universal conditions.
    • Analysis of control mechanisms and core techniques in representative methods.

    Main Results:

    • The survey covers theoretical foundations and practical advancements in controllable T2I generation.
    • Research is systematically categorized by conditioning approach.
    • Key methods are reviewed based on their underlying techniques.

    Conclusions:

    • Controllable generation extends the capabilities of T2I diffusion models beyond text-only inputs.
    • Understanding different conditioning strategies is crucial for advancing visual generation.
    • The survey provides a structured overview and resource for future research.