通过教师对齐的端到端蒸馏实现高保真两步图像生成

摘要

少步扩散蒸馏在4到8步生成任务中已日渐成熟，但进一步压缩至两步仍具挑战性。本文提出Z-Image Turbo++，这是一种从八步Z-Image Turbo教师模型蒸馏而来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有限这两大核心瓶颈，我们通过三项简单但针对该场景精心设计的选择加以突破。首先，我们提出分布对齐对抗学习，利用教师模型生成的图像而非外部真实图像作为GAN训练的真实样本，从而提供更易实现且更具信息量的对抗目标。其次，我们采用解耦分步参数化，为两个去噪步骤分配独立的模型参数，以更好匹配各自不同的容量需求。第三，我们执行端到端训练与迭代正则化，使第一步能接收来自最终图像质量的梯度，同时通过显式的第一步损失保留有意义的中间生成结果。这些设计共同在定性和定量评估中显著缩小了两步生成与八步生成之间的质量差距，突显了针对少步生成精心设计的蒸馏策略在改善质量-效率权衡方面的潜力。

English

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.