High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation
Few-step diffusion distillation has become increasingly mature for 4-8-step generation, yet pushing further to 2 steps remains challenging. This paper introduces Z-Image Turbo++, a high-quality 2-step image generation model distilled from the 8-step Z-Image Turbo teacher using three key design choices: Distribution-Aligned Adversarial Learning, Step-Decoupled Parameterization, and End-to-End Training with Iterative Regularization. These designs substantially narrow the quality gap between 2-step and 8-step generation.
[2606.12575] High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation
[Submitted on 10 Jun 2026]
Title:High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.12575 [cs.CV]
(or arXiv:2606.12575v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12575
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dongyang Liu [view email] [v1] Wed, 10 Jun 2026 18:24:50 UTC (24,295 KB)
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