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
Organization: Tongyi-MAI

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.