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.
@article{arxiv.2605.02767,
title = {TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution},
author = {Sowmya Vajrala and Akshay Bankar and Manjunath Arveti and Shreyas Pandith and Sravanth Kodavanti and Subhajit Sanyal and Amit Unde and Srinivas Soumitri Miriyala},
journal= {arXiv preprint arXiv:2605.02767},
year = {2026}
}