Qwen-Image-Flash: Beyond Objective Design

Few-step distillation has become an effective strategy for accelerating advanced visual generative models, yet prior work has largely focused on distillation objectives. In this work, we revisit few-step distillation from a complementary perspective, focusing on the training recipe that critically shapes student performance. Using Qwen-Image-2.0 as a representative case, we systematically investigate three factors in unified text-to-image generation and instruction-guided image editing distillation: data composition, teacher guidance, and task mixture. Our empirical analysis reveals several non-obvious behaviors, which motivate the development of Qwen-Image-Flash. Overall, our results suggest that effective few-step distillation requires not only carefully designed objectives, but also principled organization of the broader training pipeline.

Tianhe Wu, Kun Yan, Zikai Zhou, Lihan Jiang, Jiahao Li, Jie Zhang, Kaiyuan Gao, Ningyuan Tang, Shengming Yin, Xiaoyue Chen, Xiao Xu, Yilei Chen, Yuxiang Chen, Yan Shu, Yixian Xu, Yanran Zhang, Zihao Liu, Zhendong Wang, Zekai Zhang, Deqing Li, Liang Peng, Yi Wang, Jingren Zhou, Chenfei Wu · 2026