Duplicate from unsloth/Z-Image-GGUF

Co-authored-by: Michael Han <shimmyshimmer@users.noreply.huggingface.co>

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README.md

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+---
+base_model: Tongyi-MAI/Z-Image
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-to-image
+library_name: ggml
+tags:
+- gguf
+- unsloth
+- quantized
+---
+
+This is a GGUF quantized version of [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image). <br>
+unsloth/Z-Image-GGUF uses [Unsloth Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) methodology for SOTA performance.
+- Important layers are upcasted to higher precision.
+- Uses tooling from [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) by city96.
+
+
+<div>
+  <div style="display: flex; gap: 5px; align-items: center; ">
+    <a href="https://github.com/unslothai/unsloth/">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
+    </a>
+    <a href="https://discord.gg/unsloth">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
+    </a>
+        <a href="https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs">
+      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
+    </a>
+  </div>
+</div>
+
+---
+<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;
+[![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 | ❌ | ✅ |
+
+## 🚀 Quick Start
+
+### Installation & Download
+
+Install the latest version of diffusers:
+```bash
+pip install git+https://github.com/huggingface/diffusers
+```
+
+Download the model:
+```bash
+pip install -U huggingface_hub
+HF_XET_HIGH_PERFORMANCE=1 hf download Tongyi-MAI/Z-Image
+```
+
+### Recommended Parameters
+
+- **Resolution:** 512×512 to 2048×2048 (total pixel area, any aspect ratio)
+- **Guidance scale:** 3.0 – 5.0
+- **Inference steps:** 28 – 50
+
+### Usage Example
+
+```python
+import torch
+from diffusers import ZImagePipeline
+
+# Load the pipeline
+pipe = ZImagePipeline.from_pretrained(
+    "Tongyi-MAI/Z-Image",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=False,
+)
+pipe.to("cuda")
+
+# Generate image
+prompt = "两名年轻亚裔女性紧密站在一起，背景为朴素的灰色纹理墙面，可能是室内地毯地面。左侧女性留着长卷发，身穿藏青色毛衣，左袖有奶油色褶皱装饰，内搭白色立领衬衫，下身白色裤子；佩戴小巧金色耳钉，双臂交叉于背后。右侧女性留直肩长发，身穿奶油色卫衣，胸前印有“Tun the tables”字样，下方为“New ideas”，搭配白色裤子；佩戴银色小环耳环，双臂交叉于胸前。两人均面带微笑直视镜头。照片，自然光照明，柔和阴影，以藏青、奶油白为主的中性色调，休闲时尚摄影，中等景深，面部和上半身对焦清晰，姿态放松，表情友好，室内环境，地毯地面，纯色背景。"
+negative_prompt = "" # Optional, but would be powerful when you want to remove some unwanted content
+
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    height=1280,
+    width=720,
+    cfg_normalization=False,
+    num_inference_steps=50,
+    guidance_scale=4,
+    generator=torch.Generator("cuda").manual_seed(42),
+).images[0]
+
+image.save("example.png")
+```
+
+## 📜 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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