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
The continuous rapid development in AI, particularly generative models, necessitates ongoing research into optimization techniques like few-step distillation.
Improving the efficiency and performance of visual generative models through refined training methodologies is critical for advancing AI capabilities and reducing computational costs.
This research shifts focus from solely distillation objectives to the 'training recipe' itself, providing a new dimension for optimizing advanced visual generative models.
- · AI researchers
- · Generative AI model developers
- · Companies utilizing visual generative AI
- · Inefficient AI training methodologies
More efficient development and deployment of high-quality visual generative AI models.
Accelerated integration of advanced image generation and editing capabilities into various applications and industries.
Reduced compute requirements for AI development, potentially broadening access to advanced AI capabilities.
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Read at arXiv cs.LG