提出无需训练的高效采样方法，实现内容生成全局一致。
Accelerated Likelihood Maximization for Diffusion-based Versatile Content Generation
- 在反向扩散过程中直接优化未观察区域，提升生成一致性。
- 相比现有方法，生成质量更高且计算效率显著提升。
- 适合需要快速生成高质量内容的研究与应用者。
从部分给定输入生成多样化、连贯且合理的内容，仍是扩散模型面临的根本挑战。现有方法存在明显局限：基于训练的方法虽任务性能强，但计算成本高，跨任务泛化差；无训练方法虽效率高，但未显式优化未观测变量，导致全局不一致。为此，我们提出加速似然最大化（ALM），一种集成于反向扩散过程的新型无训练采样策略，显著拓展了扩散模型在复杂生成任务中的适用性。与以往通过预生成区域约束隐式影响缺失区域不同，我们直接在采样过程中优化未观测区域，实现全局连贯且合理的生成。此外，引入加速策略，在不牺牲性能的前提下大幅提升计算效率。实验表明，ALM在多种数据域和任务中持续优于当前最优方法，建立了一种强大的多功能内容生成范式。
原文摘要 · Abstract (English)
Generating diverse, coherent, and plausible content from partially given inputs remains a fundamental challenge for diffusion models. Existing approaches face clear limitations: training-based approaches offer strong task-specific results but require costly computation, and they generalize poorly across tasks. Training-free approaches offer better efficiency, but they do not explicitly optimize over unobserved variables, leading to globally inconsistent results. To address these limitations, we introduce Accelerated Likelihood Maximization (ALM), a novel training-free sampling strategy integrated into the reverse diffusion process that significantly extends the applicability of diffusion models beyond simple generation tasks. Unlike previous methods that implicitly influence missing regions through pre-generated region constraints, we directly optimize the unobserved region during the sampling process, enabling globally coherent and plausible generation. Furthermore, we incorporate an acceleration strategy that significantly improves computational efficiency without sacrificing performance. Experimental results demonstrate that ALM consistently outperforms state-of-the-art methods in various data domains and tasks, establishing a powerful paradigm for versatile content generation.
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