Abstract
Although diffusion-based models have achieved impressive results in image super-resolution, they often rely on large-scale backbones such as Stable Diffusion XL (SDXL) and Diffusion Transformers (DiT), which lead to excessive computational and memory costs during training and inference. To address this issue, we develop a lightweight diffusion method, PGP-DiffSR, by removing redundant information from diffusion models under the guidance of the phase information of inputs for efficient image super-resolution. We first identify the intra-block redundancy within the diffusion backbone and propose a progressive pruning approach that removes redundant blocks while reserving restoration capability. We note that the phase information of the restored images produced by the pruned diffusion model is not well estimated. To solve this problem, we propose a phase-exchange adapter module that explores the phase information of the inputs to guide the pruned diffusion model for better restoration performance. We formulate the progressive pruning approach and the phase-exchange adapter module into a unified model. Extensive experiments demonstrate that our method achieves competitive restoration quality while significantly reducing computational load and memory consumption. The code is available at this https URL.
Abstract (translated)
尽管基于扩散的模型在图像超分辨率方面取得了令人印象深刻的结果，但它们通常依赖于大规模的基础架构，如Stable Diffusion XL（SDXL）和Diffusion Transformers（DiT），这会导致训练和推理过程中的计算和内存成本过高。为了解决这个问题，我们开发了一种轻量级的扩散方法PGP-DiffSR，在输入数据相位信息的指导下从扩散模型中去除冗余信息，从而实现高效的图像超分辨率处理。 首先，我们识别出扩散基础架构内的块间冗余，并提出一种渐进式剪枝的方法，这种方法在移除冗余块的同时保留了恢复能力。注意到由剪枝后的扩散模型生成的重建图像的相位信息估计不佳。为了解决这一问题，我们提出了一个相位交换适配器模块，该模块利用输入数据的相位信息来引导剪枝后的扩散模型以获得更好的恢复性能。 我们将渐进式剪枝方法和相位交换适配器模块统一集成到一个模型中。广泛的实验表明，我们的方法在显著降低计算负载和内存消耗的同时，实现了竞争性的恢复质量。代码可在[此链接](https://这个URL被原作者省略了，请访问原文获取准确的链接)获取。
URL
https://arxiv.org/abs/2512.02681
