Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-resolution. However, their large model size and iterative sampling procedures make them computationally expensive for practical deployment. In this work, we present TOC-SR, a framework for building efficient one-step super-resolution models by first discovering a compact diffusion backbone. Starting from a sixteen-channel latent diffusion model, we construct parameter-efficient surrogate blocks using feature-wise generative distillation and perform architecture discovery using epsilon-constrained Bayesian Optimization to minimize model complexity while preserving generative fidelity. The resulting compact diffusion backbone achieves a 6.6x reduction in parameters and a 2.8x reduction in GMACs compared to the expanded diffusion model. We then adapt this backbone for super-resolution and distill the diffusion process into a single-step generator. Experiments demonstrate that the proposed approach enables efficient super-resolution while maintaining strong reconstruction quality.

翻译：扩散模型近年来在包括超分辨率在内的图像复原任务中展现出强大性能。然而，其庞大的模型规模与迭代采样过程导致实际部署时计算成本高昂。本文提出TOC-SR框架，通过先发现紧凑扩散骨干网络来构建高效单步超分辨率模型。基于十六通道隐空间扩散模型，我们利用特征生成蒸馏构建参数高效的替代模块，并采用ε约束贝叶斯优化进行架构发现，在最小化模型复杂度的同时保持生成保真度。与扩展扩散模型相比，所得紧凑扩散骨干网络参数减少6.6倍，GMACs降低2.8倍。随后我们将该骨干网络适配至超分辨率任务，并将扩散过程蒸馏为单步生成器。实验表明，所提方法在保持高质量重建效果的同时实现了高效超分辨率。