Paper page - Qwen-Image-Flash: Beyond Objective Design

Source: https://huggingface.co/papers/2606.03746 Authors:

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Abstract

Few-step distillation for visual generative models benefits from systematic investigation of training recipes beyond just distillation objectives, leading to improved student performance through optimized data composition, teacher guidance, and task mixture.

Few-step distillationhas become an effective strategy for accelerating advancedvisual generative models, yet prior work has largely focused ondistillation objectives. In this work, we revisitfew-step distillationfrom a complementary perspective, focusing on thetraining recipethat critically shapes student performance. Using Qwen-Image-2.0 as a representative case, we systematically investigate three factors in unifiedtext-to-image generationandinstruction-guided image editingdistillation:data composition,teacher guidance, andtask mixture. Our empirical analysis reveals several non-obvious behaviors, which motivate the development of Qwen-Image-Flash. Overall, our results suggest that effectivefew-step distillationrequires not only carefully designed objectives, but also principled organization of the broader training pipeline.

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