研究扩散模型超分辨率中采样超参数的影响，发现条件步长比迭代次数更重要。
An Empirical Study of Sampling Hyperparameters in Diffusion-Based Super-Resolution
- 通过消融实验分析条件扩散模型的超参数影响。
- 步长在2.0到3.0之间时重建效果最佳，优于步数调整。
- 适合关注图像超分辨率优化与扩散模型调参的研究者。
扩散模型在解决单图超分辨率等反问题方面展现出强大潜力，可通过预训练的无条件先验从低分辨率观测中恢复高分辨率图像。条件化方法如扩散后验采样（DPS）和流形约束梯度（MCG）能显著提升重建质量，但引入了需精细调节的额外超参数。本文针对FFHQ数据集上的超分辨率任务开展实证消融研究，揭示在应用条件化时，条件步长对性能的影响远大于扩散步数。实验表明，在2.0至3.0范围内的步长能获得最优整体表现。
原文摘要 · Abstract (English)
Diffusion models have shown strong potential for solving inverse problems such as single-image super-resolution, where a high-resolution image is recovered from a low-resolution observation using a pretrained unconditional prior. Conditioning methods, including Diffusion Posterior Sampling (DPS) and Manifold Constrained Gradient (MCG), can substantially improve reconstruction quality, but they introduce additional hyperparameters that require careful tuning. In this work, we conduct an empirical ablation study on FFHQ super-resolution to identify the dominant factors affecting performance when applying conditioning to pretrained diffusion models, and show that the conditioning step size has a significantly greater impact than the diffusion step count, with step sizes in the range of [2.0, 3.0] yielding the best overall performance in our experiments.
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