Name: Z Image Turbo
Author: Tongyi-MAI

Overview

Z Image Turbo is a high-performance text-to-image model designed for developers who need a balance between generation speed and visual fidelity. Unlike heavier diffusion models, Turbo is optimized for low-latency inference, making it an ideal candidate for real-time applications, iterative prototyping, and dynamic content generation within apps. It integrates easily via standard API endpoints and operates under the permissive Apache-2.0 license, ensuring flexibility for commercial deployment. Developers can leverage it for rapid asset creation, automated UI placeholders, or integrating generative art into user-facing workflows without the overhead of managing massive GPU clusters.

Highlights

Low-latency inference for real-time image generation
Apache-2.0 license for flexible commercial integration
Optimized for rapid iterative prototyping and asset creation
Efficient text-to-image pipeline reducing infrastructure overhead

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Tongyi-MAI/Z-Image-Turbo")
tokenizer = AutoTokenizer.from_pretrained("Tongyi-MAI/Z-Image-Turbo")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download Tongyi-MAI/Z-Image-Turbo

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download Tongyi-MAI/Z-Image-Turbo config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Tongyi-MAI/Z-Image-Turbo')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/Tongyi-MAI/Z-Image-Turbo

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Tongyi-MAI/Z-Image-Turbo

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Tongyi-MAI/Z-Image-Turbo')
tokenizer = AutoTokenizer.from_pretrained('Tongyi-MAI/Z-Image-Turbo')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model Tongyi-MAI/Z-Image-Turbo

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model Tongyi-MAI/Z-Image-Turbo README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Tongyi-MAI/Z-Image-Turbo')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/Tongyi-MAI/Z-Image-Turbo.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Tongyi-MAI/Z-Image-Turbo.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Tongyi-MAI/Z-Image-Turbo')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
language:

en

pipeline_tag: text-to-image
library_name: diffusers
---

<h1 align="center">⚡️- Image<br><sub><sup>An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer</sup></sub></h1>

![Official Site](https://tongyi-mai.github.io/Z-Image-blog/) 
![GitHub](https://github.com/Tongyi-MAI/Z-Image) 
![Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) 
![Hugging Face](https://huggingface.co/spaces/Tongyi-MAI/Z-Image-Turbo) 
![Hugging Face](https://huggingface.co/spaces/akhaliq/Z-Image-Turbo) 
![ModelScope Model](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) 
![ModelScope Space](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=469191&modelType=Checkpoint&sdVersion=Z_IMAGE_TURBO&modelUrl=modelscope%3A%2F%2FTongyi-MAI%2FZ-Image-Turbo%3Frevision%3Dmaster) 
![Art Gallery PDF](assets/Z-Image-Gallery.pdf) 
![Web Art Gallery](https://modelscope.cn/studios/Tongyi-MAI/Z-Image-Gallery/summary) 
<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

Z-Image is a powerful and highly efficient image generation model family with 6B parameters. Currently there are four variants:

🚀 Z-Image-Turbo – A distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers ⚡️sub-second inference latency⚡️ on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.

🎨 Z-Image – The foundation model behind Z-Image-Turbo. Z-Image focuses on high-quality generation, rich aesthetics, strong diversity, and controllability, well-suited for creative generation, fine-tuning, and downstream development. It supports a wide range of artistic styles, effective negative prompting, and high diversity across identities, poses, compositions, and layouts.

🧱 Z-Image-Omni-Base – The versatile foundation model capable of both generation and editing tasks. By releasing this checkpoint, we aim to unlock the full potential for community-driven fine-tuning and custom development, providing the most "raw" and diverse starting point for the open-source community.

✍️ Z-Image-Edit – A variant fine-tuned on Z-Image specifically for image editing tasks. It supports creative image-to-image generation with impressive instruction-following capabilities, allowing for precise edits based on natural language prompts.

📥 Model Zoo

| Model | Pre-Training | SFT | RL | Step | CFG | Task | Visual Quality | Diversity | Fine-Tunability | Hugging Face | ModelScope |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Z-Image-Omni-Base | ✅ | ❌ | ❌ | 50 | ✅ | Gen. / Editing | Medium | High | Easy | *To be released* | *To be released* |
| Z-Image | ✅ | ✅ | ❌ | 50 | ✅ | Gen. | High | Medium | Easy | ![Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image) <br> ![Hugging Face Space](https://huggingface.co/spaces/Tongyi-MAI/Z-Image) | ![ModelScope Model](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image) <br> ![ModelScope Space](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) |
| Z-Image-Turbo | ✅ | ✅ | ✅ | 8 | ❌ | Gen. | Very High | Low | N/A | ![Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) <br> ![Hugging Face Space](https://huggingface.co/spaces/Tongyi-MAI/Z-Image-Turbo) | ![ModelScope Model](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) <br> ![ModelScope Space](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=469191&modelType=Checkpoint&sdVersion=Z_IMAGE_TURBO&modelUrl=modelscope%3A%2F%2FTongyi-MAI%2FZ-Image-Turbo%3Frevision%3Dmaster) |
| Z-Image-Edit | ✅ | ✅ | ❌ | 50 | ✅ | Editing | High | Medium | Easy | *To be released* | *To be released* | | *To be released* |

🖼️ Showcase

📸 Photorealistic Quality: Z-Image-Turbo delivers strong photorealistic image generation while maintaining excellent aesthetic quality.

!Showcase of Z-Image on Photo-realistic image Generation

📖 Accurate Bilingual Text Rendering: Z-Image-Turbo excels at accurately rendering complex Chinese and English text.

!Showcase of Z-Image on Bilingual Text Rendering

💡 Prompt Enhancing & Reasoning: Prompt Enhancer empowers the model with reasoning capabilities, enabling it to transcend surface-level descriptions and tap into underlying world knowledge.

!reasoning.jpg

🧠 Creative Image Editing: Z-Image-Edit shows a strong understanding of bilingual editing instructions, enabling imaginative and flexible image transformations.

!Showcase of Z-Image-Edit on Image Editing

🏗️ Model Architecture

We adopt a Scalable Single-Stream DiT (S3-DiT) architecture. In this setup, text, visual semantic tokens, and image VAE tokens are concatenated at the sequence level to serve as a unified input stream, maximizing parameter efficiency compared to dual-stream approaches.

!Architecture of Z-Image and Z-Image-Edit

📈 Performance

According to the Elo-based Human Preference Evaluation (on *Alibaba AI Arena*), Z-Image-Turbo shows highly competitive performance against other leading models, while achieving state-of-the-art results among open-source models.

<p ali