提出更高效可解释的掩码扩散采样方法，提升生成速度与稳定性。
Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
- 设计'先选后采'机制，用可解释的采样策略替代复杂掩码过程。
- 通过部分缓存与探索-利用平衡策略，采样速度提升约3倍。
- 理论分析揭示原模型隐含温度采样机制，为优化提供新视角。
掩码扩散模型在多个领域展现出生成高质量样本的潜力，但其采样加速仍相对未被充分探索。本文对图像建模中的MaskGIT采样器进行理论分析，揭示其隐含的温度采样机制。基于此，提出“moment sampler”——一种渐近等价但更易处理且可解释的替代方案，采用‘先选后采’策略，先确定解码位置再生成令牌。此外，通过两项关键创新提升该类算法效率：一是针对Transformer的局部缓存技术，可在不显著增加计算成本的前提下逼近更长采样轨迹；二是融合探索与利用权衡的混合策略，实现自适应解码顺序选择。在图像与文本领域的实验验证了理论分析的有效性及所提方法的高效性，推动了掩码扩散采样器的理论理解与实际应用。
原文摘要 · Abstract (English)
Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampling process remains relatively underexplored. To investigate efficient samplers for masked diffusion, this paper theoretically analyzes the MaskGIT sampler for image modeling, revealing its implicit temperature sampling mechanism. Through this analysis, we introduce the "moment sampler," an asymptotically equivalent but more tractable and interpretable alternative to MaskGIT, which employs a "choose-then-sample" approach by selecting unmasking positions before sampling tokens. In addition, we improve the efficiency of choose-then-sample algorithms through two key innovations: a partial caching technique for transformers that approximates longer sampling trajectories without proportional computational cost, and a hybrid approach formalizing the exploration-exploitation trade-off in adaptive unmasking. Experiments in image and text domains demonstrate our theory as well as the efficiency of our proposed methods, advancing both theoretical understanding and practical implementation of masked diffusion samplers.
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