AI BriefingQwen-Image-Flash: Beyond Objective Function Design
Key point
Qwen-Image-Flash demonstrates the importance of optimizing data composition and training pipelines, beyond objective functions, for accelerating image generation models.
Details
Few-step distillation is an effective strategy for accelerating visual generative models, but prior work has mainly focused on designing distillation objectives.
This study uses Qwen-Image-2.0 as a case study to analyze, from multiple angles, the training recipe that determines the student model's performance. In particular, it focused on investigating the following three factors during unified text-to-image generation and instruction-based image editing distillation:
- Data composition
- Teacher guidance
- Task mixture
Through experimental analysis, the study uncovered several previously little-known behaviors, and based on these findings, developed Qwen-Image-Flash. As a result, it demonstrated that effective few-step distillation requires not only sophisticated objective function design, but also principled organization of the entire training pipeline.
