TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution

• URL: http://arxiv.org/abs/2605.02767v1
• Date: Mon, 04 May 2026 16:06:24 GMT
• Title: TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
• Abstract summary: We present a framework for building efficient one-step super-resolution models by first discovering a compact diffusion backbone.<n>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.<n> Experiments demonstrate that the proposed approach enables efficient super-resolution while maintaining strong reconstruction quality.
• Score: 4.395931521489804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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