Paper Detail
Taihang Hu, Zhao Wang, Zuan Gao, Tao Liu, Hao Yan, Zhengze Xu, Yuhang Yu, Yongchao Du, Xingjian Wang, Jun Zheng, Qinye Zhou, Zhengrui Chen, Chao Lin, Yefeng Shen, Zhengtao Wu, Ge Wu, Xiaoli Xu, Denghui Yang, Huayu Zhang, Mingzhou Zhang, Mengting Chen
We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through systematic training engineering under a constrained computational budget. Swift-Image adopts an efficient 6B single-stream DiT and a progressive training pipeline that evolves from broad semantic coverage to higher resolution, stronger visual quality, and unified generation-editing supervision. For post-training, we employ parallel expert reinforcement learning followed by multi-teacher on-policy distillation to alleviate interference among heterogeneous objectives. We further decouple high-level reasoning from pixel-level rendering with a Prompt Enhancer that translates user requests into generator-aligned visual specifications. For efficient deployment, structural pruning and few-step distillation produce 3B and accelerated variants. Swift-Image achieves leading aggregate performance among evaluated open-source models with only 6B parameters and 243K GPU training hours; the compressed 3B model incurs nearly no loss, while few-step distillation further improves aggregate editing performance with substantially fewer sampling steps. Our study also summarizes practical lessons for architecture, data curriculum, post-training, prompt enhancement, and model compression.
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@article{hu2026swift,
title = {Swift-Image: Exploring the Performance Frontier of Compact Unified Image Generation Models},
author = {Taihang Hu and Zhao Wang and Zuan Gao and Tao Liu and Hao Yan and Zhengze Xu and Yuhang Yu and Yongchao Du and Xingjian Wang and Jun Zheng and Qinye Zhou and Zhengrui Chen and Chao Lin and Yefeng Shen and Zhengtao Wu and Ge Wu and Xiaoli Xu and Denghui Yang and Huayu Zhang and Mingzhou Zhang and Mengting Chen},
year = {2026},
abstract = {We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through systematic training engineering under a constrained computational budget. Swift-Image adopts an efficient 6B single-stream DiT and a progressive training pipeline that evolves from broad semantic coverage to higher resolution, stronger visual quality, and unified generation-editing sup},
url = {https://arxiv.org/abs/2608.20334},
keywords = {cs.CV},
eprint = {2608.20334},
archiveprefix = {arXiv},
}
{}