用精简扩散模型实现单步图像超分辨率，速度快且保真度高。
TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
- 通过特征生成蒸馏与贝叶斯优化，压缩扩散模型至16通道小骨干。
- 参数减少6.6倍，GMACs降低2.8倍，仍保持良好生成质量。
- 适合部署在资源受限设备的快速图像超分任务。
扩散模型在图像修复任务中表现优异，包括超分辨率，但其庞大的模型规模和迭代采样过程导致计算开销大，难以实际部署。本文提出TOC-SR框架，通过首先发现紧凑的扩散主干来构建高效的一步式超分辨率模型。从16通道潜在扩散模型出发，利用特征级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搜索，在最小化模型复杂度的同时保持生成保真度。所得紧凑扩散主干相比扩展扩散模型参数减少6.6倍，GMACs降低2.8倍。随后将该主干适配于超分辨率任务，并将扩散过程蒸馏为单步生成器。实验表明，该方法可在保持强重建质量的前提下实现高效超分辨率。
原文摘要 · Abstract (English)
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.
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