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    We developed a unified sampling framework (USF++) for diffusion probabilistic models (DPMs) to accelerate image generation. Our method significantly improves sample quality with fewer function evaluations, outperforming state-of-the-art methods.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Diffusion probabilistic models (DPMs) show great promise in generative tasks.
    • Current DPM sampling methods are computationally expensive due to a high number of function evaluations (NFE).
    • Improving sample quality with limited NFE remains a challenge.

    Purpose of the Study:

    • To propose a unified sampling framework (USF++) for DPMs to optimize solver strategies.
    • To investigate the impact of different solving strategies at various timesteps.
    • To enhance sample quality and reduce NFE in DPMs.

    Main Methods:

    • Developed a unified sampling framework (USF++) based on exponential integral formulation.
    • Implemented a novel approach allowing flexible solver strategy choices at each timestep.
    • Utilized evolutionary search to discover optimal solver schedules.

    Main Results:

    • Achieved state-of-the-art results on CIFAR-10 (3.89 FID with 5 NFE) and LSUN-Bedroom (8.62 FID with 3 NFE).
    • Demonstrated significant improvements over existing sampling methods.
    • Achieved a 2x acceleration ratio on Stable-Diffusion models without retraining.

    Conclusions:

    • The proposed USF++ framework effectively accelerates DPM sampling while maintaining or improving sample quality.
    • Optimized solver schedules are crucial for reducing truncation error and enhancing generation performance.
    • The framework shows feasibility for rapid sampling in large-scale models like Stable-Diffusion.