Abstract
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.
Abstract (translated)
扩散模型在解决诸如单图像超分辨率等逆向问题方面展现出强大的潜力，即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。条件方法（包括扩散后验采样(DPS)和流形约束梯度(MCG)）可以显著提高重建质量，但它们引入了需要仔细调整的额外超参数。在这项工作中，我们通过对FFHQ超分辨率进行实证消融研究，识别出在将条件应用于预训练扩散模型时影响性能的主要因素，并表明条件步骤大小比扩散步数对性能的影响更大，在我们的实验中，范围为[2.0, 3.0]的步骤大小产生了最佳的整体效果。
URL
https://arxiv.org/abs/2512.17675
