Title: Qwen-Image-Flash: Moving Past Objective-Centric Design

Original: arXiv:2606.03746v1 Announce Type: cross Abstract: 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.

Rewrite: arXiv:2606.03746v1 Announcement Type: cross Abstract: While few-step distillation is now a proven method for speeding up sophisticated visual generative models, previous research has predominantly concentrated on the design of distillation objectives. This study approaches the problem from a different angle, emphasizing the training regimen that plays a decisive role in determining the student model’s capabilities. By leveraging Qwen-Image-2.0 as a primary example, we conduct a thorough examination of three critical elements within the distillation process for both unified text-to-image synthesis and instruction-based image editing: the makeup of the training data, the influence of teacher guidance, and the combination of tasks. The empirical findings highlight several unexpected dynamics, which directly inspired the creation of Qwen-Image-Flash. Ultimately, our conclusions indicate that successful few-step distillation depends not merely on meticulously crafted objectives, but also on the structured and logical arrangement of the overall training workflow.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC