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Nexus: Structured Synergy for Efficient Text-to-Image Generation using Rectified Flow Model

2026-08-17 04:48:39
Yizhao Wang
arXiv_CV
                 
Abstract

Diffusion and flow matching models have made significant progress in text-to-image generation, yet high computation, quadratic complexity, and large memory footprint hinder high-resolution synthesis and edge deployment. We propose Nexus, which integrates sparse architecture, linear complexity, and low-bit quantization. It combines MoE feed-forward layers, gated DeltaNet attention, and per-expert low-bit training to reduce computation and memory. Their joint optimization allows Nexus to achieve generation quality comparable to mainstream models such as SDXL and SD3 while delivering markedly higher inference efficiency. Experiments on COCO and LAION validate its effectiveness.

Abstract (translated)

URL

https://arxiv.org/abs/2608.16104

PDF

https://arxiv.org/pdf/2608.16104.pdf

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