Few-step diffusion distillation has become increasingly mature for 4-8-step generation, yet pushing further to 2 steps remains challenging. In this work, we introduce Z-Image Turbo++, a high-quality 2-step image generation model distilled from the 8-step Z-Image Turbo teacher. Our method addresses the central bottlenecks of increased task difficulty and limited model capacity in 2-step generation through three simple but effective design choices tailored to this regime. First, we propose Distribution-Aligned Adversarial Learning, which uses teacher-generated images rather than external real images as real samples for GAN training, providing a more attainable and informative adversarial target. Second, we adopt Step-Decoupled Parameterization, assigning independent model parameters to the two denoising steps to better match their distinct capacity demands. Third, we perform End-to-End Training with Iterative Regularization, allowing the first step to receive gradients from final image quality while preserving a meaningful intermediate generation through an explicit step-1 loss. Together, these designs substantially narrow the quality gap between 2-step and 8-step generation in both qualitative and quantitative evaluations, highlighting the potential of carefully tailored distillation strategies for improving the quality-efficiency trade-off in few-step generation.

翻译：少步扩散蒸馏在4-8步生成领域已日趋成熟，但进一步压缩至两步生成仍面临重大挑战。本文提出Z-Image Turbo++，一种从8步Z-Image Turbo教师模型中蒸馏得到的高质量两步图像生成模型。针对两步生成中任务难度增加与模型容量受限的核心瓶颈，我们通过三种简洁有效的定制化设计来应对。首先，提出分布对齐对抗学习，采用教师生成图像而非真实外部图像作为生成对抗网络的真实样本，提供更易实现且信息量更丰富的对抗目标。其次，采用步分解参数化方法，为两个去噪步骤分配独立模型参数，以更好匹配其差异化的容量需求。第三，实施带迭代正则化的端到端训练，使第一步能接收最终图像质量的梯度反馈，同时通过显式的第一步损失保留有意义的中间生成结果。这些设计在定性与定量评估中显著缩小了两步与八步生成的质量差距，凸显了针对少步生成场景精心设计的蒸馏策略在提升质量-效率平衡方面的潜力。