Qwen-Image-Flash: Beyond Objective Design
Authors: name: Tianhe Wu, name: Kun Yan, name: Zikai Zhou, name: Lihan Jiang, name: Jiahao Li, name: Jie Zhang, name: Kaiyuan Gao, name: Ningyuan Tang, name: Shengming Yin, name: Xiaoyue Chen, name: Xiao Xu, name: Yilei Chen, name: Yuxiang Chen, name: Yan Shu, name: Yixian Xu, name: Yanran Zhang, name: Zihao Liu, name: Zhendong Wang, name: Zekai Zhang, name: Deqing Li, name: Liang Peng, name: Yi Wang, name: Jingren Zhou, name: Chenfei Wu
Organization: Qwen

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.