Enhancing Text-to-Image Generation via End-Edge Collaborative Hybrid Super-Resolution

• URL: http://arxiv.org/abs/2601.14741v1
• Date: Wed, 21 Jan 2026 07:55:37 GMT
• Title: Enhancing Text-to-Image Generation via End-Edge Collaborative Hybrid Super-Resolution
• Authors: Chongbin Yi, Yuxin Liang, Ziqi Zhou, Peng Yang,
• Abstract summary: We propose an end-edge collaborative generation-enhancement framework.<n> Experiments show that our system reduces service latency by 33% compared with baselines.
• Score: 6.015475364527398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence-Generated Content (AIGC) has made significant strides, with high-resolution text-to-image (T2I) generation becoming increasingly critical for improving users' Quality of Experience (QoE). Although resource-constrained edge computing adequately supports fast low-resolution T2I generations, achieving high-resolution output still faces the challenge of ensuring image fidelity at the cost of latency. To address this, we first investigate the performance of super-resolution (SR) methods for image enhancement, confirming a fundamental trade-off that lightweight learning-based SR struggles to recover fine details, while diffusion-based SR achieves higher fidelity at a substantial computational cost. Motivated by these observations, we propose an end-edge collaborative generation-enhancement framework. Upon receiving a T2I generation task, the system first generates a low-resolution image based on adaptively selected denoising steps and super-resolution scales at the edge side, which is then partitioned into patches and processed by a region-aware hybrid SR policy. This policy applies a diffusion-based SR model to foreground patches for detail recovery and a lightweight learning-based SR model to background patches for efficient upscaling, ultimately stitching the enhanced ones into the high-resolution image. Experiments show that our system reduces service latency by 33% compared with baselines while maintaining competitive image quality.

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