Qwen-Image-Flash: 超越目标设计
阅读原文· arxiv.orgQwen-Image-Flash 是基于 Qwen-Image-2.0 的少步蒸馏模型。研究者从训练配方视角,系统考察了统一文生图和指令引导图像编辑蒸馏中的三个因素:数据组成、教师指导和任务混合。实证分析揭示出若干非直观行为,并据此开发了 Qwen-Image-Flash。结果表明,有效的少步蒸馏不仅需要精心设计目标,还需对整体训练流程进行原则性组织。
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.