vantagewithai/Z-Image-GGUF

Quantized GGUF version of Z-Image.

Original model link: https://huggingface.co/Tongyi-MAI/Z-Image

Watch us at Youtube: @VantageWithAI

<h1 align="center">⚡️- Image<br>_{^{An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer}}</h1>

<div align="center">

&#160; &#160; &#160; &#160; &#160; &#160; <a href="https://arxiv.org/abs/2511.22699" target="blank"><img src="https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv" height="21px"></a>

Welcome to the official repository for the Z-Image（造相）project!

</div>

🎨 Z-Image

[image]

Z-Image is the foundation model of the ⚡️- Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. While Z-Image-Turbo is built for speed, Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom.

🌟 Key Features

• —Undistilled Foundation: As a non-distilled base model, Z-Image preserves the complete training signal. It supports full Classifier-Free Guidance (CFG), providing the precision required for complex prompt engineering and professional workflows.
• —Aesthetic Versatility: Z-Image masters a vast spectrum of visual languages—from hyper-realistic photography and cinematic digital art to intricate anime and stylized illustrations. It is the ideal engine for scenarios requiring rich, multi-dimensional expression.
• —Enhanced Output Diversity: Built for exploration, Z-Image delivers significantly higher variability in composition, facial identity, and lighting across different seeds, ensuring that multi-person scenes remain distinct and dynamic.
• —Built for Development: The ideal starting point for the community. Its non-distilled nature makes it a good base for LoRA training, structural conditioning (ControlNet) and semantic conditioning.
• —Robust Negative Control: Responds with high fidelity to negative prompting, allowing users to reliably suppress artifacts and adjust compositions.

🆚 Z-Image vs Z-Image-Turbo

Recommended Parameters

• —Resolution: 512×512 to 2048×2048 (total pixel area, any aspect ratio)
• —Guidance scale: 3.0 – 5.0
• —Inference steps: 28 – 50

📜 Citation

If you find our work useful in your research, please consider citing:

@article{team2025zimage,
  title={Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer},
  author={Z-Image Team},
  journal={arXiv preprint arXiv:2511.22699},
  year={2025}
}