Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Authors: name: Z-Image Team; name: Huanqia Cai; name: Sihan Cao; name: Ruoyi Du; name: Peng Gao; name: Steven Hoi; name: Shijie Huang; name: Zhaohui Hou; name: Dengyang Jiang; name: Xin Jin; name: Liangchen Li; name: Zhen Li; name: Zhong-Yu Li; name: David Liu; name: Dongyang Liu; name: Junhan Shi; name: Qilong Wu; name: Feng Yu; name: Chi Zhang; name: Shifeng Zhang; name: Shilin Zhou
Published: 2025-11-27 18:52:07

Abstract: The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro and Seedream 4.0. Leading open-source alternatives, including Qwen-Image, Hunyuan-Image-3.0 and FLUX.2, are characterized by massive parameter counts (20B to 80B), making them impractical for inference, and fine-tuning on consumer-grade hardware. To address this gap, we propose Z-Image, an efficient 6B-parameter foundation generative model built upon a Scalable Single-Stream Diffusion Transformer (S3-DiT) architecture that challenges the "scale-at-all-costs" paradigm. By systematically optimizing the entire model lifecycle -- from a curated data infrastructure to a streamlined training curriculum -- we complete the full training workflow in just 314K H800 GPU hours (approx. $630K). Our few-step distillation scheme with reward post-training further yields Z-Image-Turbo, offering both sub-second inference latency on an enterprise-grade H800 GPU and compatibility with consumer-grade hardware (<16GB VRAM). Additionally, our omni-pre-training paradigm also enables efficient training of Z-Image-Edit, an editing model with impressive instruction-following capabilities. Both qualitative and quantitative experiments demonstrate that our model achieves performance comparable to or surpassing that of leading competitors across various dimensions. Most notably, Z-Image exhibits exceptional capabilities in photorealistic image generation and bilingual text rendering, delivering results that rival top-tier commercial models, thereby demonstrating that state-of-the-art results are achievable with significantly reduced computational overhead. We publicly release our code, weights, and online demo to foster the development of accessible, budget-friendly, yet state-of-the-art generative models.