Duplicate from vantagewithai/Z-Image-GGUF

Co-authored-by: Vantage with AI <vantagewithai@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-to-image
+library_name: diffusers
+---
+
+**Quantized GGUF version of Z-Image.**
+
+**Original model link:** [https://huggingface.co/Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)
+
+**Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai)
+
+
+<h1 align="center">⚡️- Image<br><sub><sup>An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer</sup></sub></h1>
+
+<div align="center">
+
+[![Official Site](https://img.shields.io/badge/Official%20Site-333399.svg?logo=homepage)](https://tongyi-mai.github.io/Z-Image-blog/)&#160;
+[![GitHub](https://img.shields.io/badge/GitHub-Z--Image-181717?logo=github&logoColor=white)](https://github.com/Tongyi-MAI/Z-Image)&#160;
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Checkpoint-Z--Image-yellow)](https://huggingface.co/Tongyi-MAI/Z-Image)&#160;
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Online_Demo-Z--Image-blue)](https://huggingface.co/spaces/Tongyi-MAI/Z-Image)&#160;
+[![ModelScope Model](https://img.shields.io/badge/🤖%20Checkpoint-Z--Image-624aff)](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)&#160;
+[![ModelScope Space](https://img.shields.io/badge/🤖%20Online_Demo-Z--Image-17c7a7)](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=569345&modelType=Checkpoint&sdVersion=Z_IMAGE&modelUrl=modelscope%3A%2F%2FTongyi-MAI%2FZ-Image%3Frevision%3Dmaster)&#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
+
+![Teaser](teaser.jpg)
+![asethetic](https://cdn-uploads.huggingface.co/production/uploads/64379d79fac5ea753f1c10f3/RftwBF4PzC0_L9GvETPZz.jpeg)
+![diverse](https://cdn-uploads.huggingface.co/production/uploads/64379d79fac5ea753f1c10f3/HiFeAD2XUTmlxgdWHwhss.jpeg)
+![negative](https://cdn-uploads.huggingface.co/production/uploads/64379d79fac5ea753f1c10f3/rECmhpZys1siGgEO8L6Fi.jpeg)
+
+**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.
+
+![z-image](https://cdn-uploads.huggingface.co/production/uploads/64379d79fac5ea753f1c10f3/kt_A-s5vMQ6L-_sUjNUCG.jpeg)
+
+### 🌟 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
+
+| Aspect | Z-Image | Z-Image-Turbo |
+|------|------|------|
+| CFG | ✅ | ❌ |
+| Steps | 28~50 | 8 |
+| Fintunablity | ✅ | ❌ |
+| Negative Prompting | ✅ | ❌ |
+| Diversity | High | Low |
+| Visual Quality | High | Very High |
+| RL | ❌ | ✅ |
+
+### 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:
+
+```bibtex
+@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}
+}
+```

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+        "## Model links\n\n**diffusion_models**\n\n- [z_image_bf16.safetensors](https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/diffusion_models/z_image_bf16.safetensors)\n\n**GGUF**\n\n- [z_image-xxxx.gguf](https://huggingface.co/vantagewithai/Z-Image-GGUF)\n\n\n**text_encoders**\n\n- [qwen_3_4b.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors)\n\n\n\n**vae**\n\n- [ae.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors)\n\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      └── qwen_3_4b.safetensors\n│   ├── 📂 diffusion_models/\n│   │      └── z_image_bf16.safetensors\n│   ├── 📂 unet/\n│   │      └── z_image-xxxx.gguf\n│   └── 📂 vae/\n│          └── ae.safetensors\n```\n\n"
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