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About Z-Image
zimage.net is an independent platform for Z-Image. We charge for online compute and services. Z-Image is an open-source model—you can also deploy it locally for free.
The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro [27] and Seedream 4.0 [64]. Leading open-source
alternatives, including Qwen-Image [76], Hunyuan-Image-3.0 [8] and FLUX.2 [36], 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.
The landscape of high-performance image generation models is currently dominated by proprietary systems, such as Nano Banana Pro [27] and Seedream 4.0 [64]. Leading open-source
alternatives, including Qwen-Image [76], Hunyuan-Image-3.0 [8] and FLUX.2 [36], 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.
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