Qwen-Image-Flash: Beyond Objective Design

2026-06-02

AI Summary

This work addresses the prevailing focus on distillation objectives in existing few-step distillation methods, which often overlooks the critical influence of the training pipeline on student model performance. Taking Qwen-Image-2.0 as the baseline, the study systematically investigates the interplay among three key training components—data composition, teacher guidance, and task mixing—in both text-to-image generation and instruction-guided image editing tasks, thereby transcending the limitations of solely optimizing distillation targets. The authors propose a more holistic few-step distillation paradigm by integrating multi-task mixed training, refined data ratio scheduling, and dynamic teacher guidance strategies. Experimental results demonstrate that this approach substantially enhances student model performance across both generation and editing tasks, underscoring the decisive role of training pipeline design in effective knowledge distillation.

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.

Problem

• few-step distillation
• training recipe
• text-to-image generation
• instruction-guided image editing
• visual generative models

Innovation

• few-step distillation
• training recipe
• data composition
• teacher guidance
• task mixture