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README.md 0 → 100644
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# Z-Image
## 论文
[z-Image](https://arxiv.org/abs/2511.22699)
## 模型简介
Z-Image 是一个强大的、高效的图像生成模型，具有 6B 参数。目前有三个变体：
**🚀Z-Image-Turbo** – Z-Image 的精简版本，仅用 8 NFEs（函数评估次数）即可匹配或超越领先的竞争对手。它在企业级 H800 GPU 上提供 ⚡️亚秒级推理延迟⚡️，并且可以轻松适应 16G 显存的消费设备。它在逼真图像生成、双语文本渲染（英文和中文）以及稳健的指令遵循方面表现出色。
**🧱 Z-Image-Base** – 非精简的基础模型。通过发布此检查点，我们旨在解锁社区驱动的微调和自定义开发的全部潜力。
**✍️ Z-Image-Edit** – 专门针对图像编辑任务进行微调的 Z-Image 变体。它支持创意的图像到图像生成，并具有出色的指令跟随能力，允许基于自然语言提示进行精确编辑。
**模型架构**
我们采用了一种可扩展的单流DiT（S3-DiT）架构。在这种设置中，文本、视觉语义标记和图像VAE标记在序列级别上被连接起来，作为统一的输入流，与双流方法相比，最大化了参数效率。
<div align=center>
<img src="./doc/model.png"/>
</div>
## 环境依赖
| 软件 | 版本 |
| :------: | :------: |
| DTK | 25.04.2 |
| python | 3.10.12 |
| transformers | >=4.57.1 |
| vllm | 0.9.2+das.opt1.dtk25042 |
| torch | 2.5.1+das.opt1.dtk25042 |
| triton | 3.1+das.opt1.3c5d12d.dtk25041 |
| flash_attn | 2.6.1+das.opt1.dtk2504 |
| flash_mla | 1.0.0+das.opt1.dtk25042 |
当前仅支持镜像:
- 挂载地址`-v`根据实际模型情况修改
```bash
docker run -it --shm-size 60g --network=host --name z-image --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro -v /path/your_code_path/:/path/your_code_path/ docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.2-py3.10 bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
## 数据集
暂无
## 训练
暂无
## 推理
### pytorch
#### 单机推理
可参考run.sh脚本
```python
import torch
from diffusers import ZImagePipeline
# 1. Load the pipeline
# Use bfloat16 for optimal performance on supported GPUs
pipe = ZImagePipeline.from_pretrained(
"/home/dengjb/download/Tongyi-MAI/Z-Image-Turbo",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=False,
)
pipe.to("cuda")
# [Optional] Attention Backend
# Diffusers uses SDPA by default. Switch to Flash Attention for better efficiency if supported:
# pipe.transformer.set_attention_backend("flash") # Enable Flash-Attention-2
# pipe.transformer.set_attention_backend("_flash_3") # Enable Flash-Attention-3
# [Optional] Model Compilation
# Compiling the DiT model accelerates inference, but the first run will take longer to compile.
# pipe.transformer.compile()
# [Optional] CPU Offloading
# Enable CPU offloading for memory-constrained devices.
# pipe.enable_model_cpu_offload()
prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
# 2. Generate Image
image = pipe(
prompt=prompt,
height=1024,
width=1024,
num_inference_steps=9, # This actually results in 8 DiT forwards
guidance_scale=0.0, # Guidance should be 0 for the Turbo models
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("example.png")
```
## 效果展示
<div align=center>
<img src="./doc/example.png"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 预训练权重
| 模型名称 | 权重大小 | DCU型号 | 最低卡数需求 |下载地址|
|:-----:|:----------:|:----------:|:---------------------:|:----------:|
| Z-Image-Turbo | 6B | BW1000 | 1 | [下载地址](https://modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/z-image_pytorch
## 参考资料
- https://github.com/Tongyi-MAI/Z-Image/tree/main
README_origin.md 0 → 100644
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<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;
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Checkpoint-Z--Image--Turbo-yellow)](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo)&#160;
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Online_Demo-Z--Image--Turbo-blue)](https://huggingface.co/spaces/Tongyi-MAI/Z-Image-Turbo)&#160;
[![ModelScope Model](https://img.shields.io/badge/🤖%20Checkpoint-Z--Image--Turbo-624aff)](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)&#160;
[![ModelScope Space](https://img.shields.io/badge/🤖%20Online_Demo-Z--Image--Turbo-17c7a7)](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=469191&modelType=Checkpoint&sdVersion=Z_IMAGE_TURBO&modelUrl=modelscope%253A%252F%252FTongyi-MAI%252FZ-Image-Turbo%253Frevision%253Dmaster%7D%7BOnline)&#160;
[![Art Gallery PDF](https://img.shields.io/badge/%F0%9F%96%BC%20Art_Gallery-PDF-ff69b4)](assets/Z-Image-Gallery.pdf)&#160;
[![Web Art Gallery](https://img.shields.io/badge/%F0%9F%8C%90%20Web_Art_Gallery-online-00bfff)](https://modelscope.cn/studios/Tongyi-MAI/Z-Image-Gallery/summary)&#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
Z-Image is a powerful and highly efficient image generation model with **6B** parameters. Currently there are three 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-Base** – The non-distilled foundation model. By releasing this checkpoint, we aim to unlock the full potential for community-driven fine-tuning and custom development.
- ✍️ **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 | Hugging Face | ModelScope |
| :--- |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Z-Image-Turbo** | [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Checkpoint%20-Z--Image--Turbo-yellow)](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) <br> [![Hugging Face Space](https://img.shields.io/badge/%F0%9F%A4%97%20Online%20Demo-Z--Image--Turbo-blue)](https://huggingface.co/spaces/Tongyi-MAI/Z-Image-Turbo) | [![ModelScope Model](https://img.shields.io/badge/🤖%20%20Checkpoint-Z--Image--Turbo-624aff)](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) <br> [![ModelScope Space](https://img.shields.io/badge/%F0%9F%A4%96%20Online%20Demo-Z--Image--Turbo-17c7a7)](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-Base** | *To be released* | *To be released* |
| **Z-Image-Edit** | *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](assets/showcase_realistic.png)
📖 **Accurate Bilingual Text Rendering**: **Z-Image-Turbo** excels at accurately rendering complex Chinese and English text.
![Showcase of Z-Image on Bilingual Text Rendering](assets/showcase_rendering.png)
💡 **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](assets/reasoning.png)
🧠 **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](assets/showcase_editing.png)
### 🏗️ 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](assets/architecture.webp)
### 📈 Performance
According to the Elo-based Human Preference Evaluation (on [*Alibaba AI Arena*](https://aiarena.alibaba-inc.com/corpora/arena/leaderboard?arenaType=T2I)), Z-Image-Turbo shows highly competitive performance against other leading models, while achieving state-of-the-art results among open-source models.
<p align="center">
<a href="https://aiarena.alibaba-inc.com/corpora/arena/leaderboard?arenaType=T2I">
<img src="assets/leaderboard.png" alt="Z-Image Elo Rating on AI Arena"/><br />
<span style="font-size:1.05em; cursor:pointer; text-decoration:underline;"> Click to view the full leaderboard</span>
</a>
</p>
### 🚀 Quick Start
Install the latest version of diffusers, use the following command:
<details>
<summary>Click here for details for why you need to install diffusers from source</summary>
We have submitted two pull requests ([#12703](https://github.com/huggingface/diffusers/pull/12703) and [#12715](https://github.com/huggingface/diffusers/pull/12704)) to the 🤗 diffusers repository to add support for Z-Image. Both PRs have been merged into the latest official diffusers release.
Therefore, you need to install diffusers from source for the latest features and Z-Image support.
</details>
```bash
pip install git+https://github.com/huggingface/diffusers
```
Then, try the following code to generate an image:
```python
import torch
from diffusers import ZImagePipeline
# 1. Load the pipeline
# Use bfloat16 for optimal performance on supported GPUs
pipe = ZImagePipeline.from_pretrained(
"Tongyi-MAI/Z-Image-Turbo",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=False,
)
pipe.to("cuda")
# [Optional] Attention Backend
# Diffusers uses SDPA by default. Switch to Flash Attention for better efficiency if supported:
# pipe.transformer.set_attention_backend("flash") # Enable Flash-Attention-2
# pipe.transformer.set_attention_backend("_flash_3") # Enable Flash-Attention-3
# [Optional] Model Compilation
# Compiling the DiT model accelerates inference, but the first run will take longer to compile.
# pipe.transformer.compile()
# [Optional] CPU Offloading
# Enable CPU offloading for memory-constrained devices.
# pipe.enable_model_cpu_offload()
prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
# 2. Generate Image
image = pipe(
prompt=prompt,
height=1024,
width=1024,
num_inference_steps=9, # This actually results in 8 DiT forwards
guidance_scale=0.0, # Guidance should be 0 for the Turbo models
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("example.png")
```
## 🔬 Decoupled-DMD: The Acceleration Magic Behind Z-Image
[![arXiv](https://img.shields.io/badge/arXiv-2511.22677-b31b1b.svg)](https://arxiv.org/abs/2511.22677)
Decoupled-DMD is the core few-step distillation algorithm that empowers the 8-step Z-Image model.
Our core insight in Decoupled-DMD is that the success of existing DMD (Distributaion Matching Distillation) methods is the result of two independent, collaborating mechanisms:
- **CFG Augmentation (CA)**: The primary **engine** 🚀 driving the distillation process, a factor largely overlooked in previous work.
- **Distribution Matching (DM)**: Acts more as a **regularizer** ⚖️, ensuring the stability and quality of the generated output.
By recognizing and decoupling these two mechanisms, we were able to study and optimize them in isolation. This ultimately motivated us to develop an improved distillation process that significantly enhances the performance of few-step generation.
![Diagram of Decoupled-DMD](assets/decoupled-dmd.webp)
## 🤖 DMDR: Fusing DMD with Reinforcement Learning
[![arXiv](https://img.shields.io/badge/arXiv-2511.13649-b31b1b.svg)](https://arxiv.org/abs/2511.13649)
Building upon the strong foundation of Decoupled-DMD, our 8-step Z-Image model has already demonstrated exceptional capabilities. To achieve further improvements in terms of semantic alignment, aesthetic quality, and structural coherence—while producing images with richer high-frequency details—we present **DMDR**.
Our core insight behind DMDR is that Reinforcement Learning (RL) and Distribution Matching Distillation (DMD) can be synergistically integrated during the post-training of few-step models. We demonstrate that:
- **RL Unlocks the Performance of DMD** 🚀
- **DMD Effectively Regularizes RL** ⚖️
![Diagram of DMDR](assets/DMDR.webp)
## 🎉 Community Works
- [Cache-DiT](https://github.com/vipshop/cache-dit) offers inference acceleration support for Z-Image with DBCache, Context Parallelism and Tensor Parallelism. Visit their [example](https://github.com/vipshop/cache-dit/blob/main/examples/parallelism/run_zimage_cp.py) for more details.
- [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) is a pure C++ diffusion model inference engine that supports fast and memory-efficient Z-Image inference across multiple platforms (CUDA, Vulkan, etc.). You can use stable-diffusion.cpp to generate images with Z-Image on machines with as little as 4GB of VRAM. For more information, please refer to [How to Use Z‐Image on a GPU with Only 4GB VRAM](https://github.com/leejet/stable-diffusion.cpp/wiki/How-to-Use-Z%E2%80%90Image-on-a-GPU-with-Only-4GB-VRAM)
- [LeMiCa](https://github.com/UnicomAI/LeMiCa) provides a training-free, timestep-level acceleration method that conveniently speeds up Z-Image inference. For more details, see [LeMiCa4Z-Image](https://github.com/UnicomAI/LeMiCa/tree/main/LeMiCa4Z-Image).
## 🚀 Star History
[![Star History Chart](https://api.star-history.com/svg?repos=Tongyi-MAI/Z-Image&type=date&legend=top-left)](https://www.star-history.com/#Tongyi-MAI/Z-Image&type=date&legend=top-left)
## 📜 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}
}
@article{liu2025decoupled,
title={Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield},
author={Dongyang Liu and Peng Gao and David Liu and Ruoyi Du and Zhen Li and Qilong Wu and Xin Jin and Sihan Cao and Shifeng Zhang and Hongsheng Li and Steven Hoi},
journal={arXiv preprint arXiv:2511.22677},
year={2025}
}
@article{jiang2025distribution,
title={Distribution Matching Distillation Meets Reinforcement Learning},
author={Jiang, Dengyang and Liu, Dongyang and Wang, Zanyi and Wu, Qilong and Jin, Xin and Liu, David and Li, Zhen and Wang, Mengmeng and Gao, Peng and Yang, Harry},
journal={arXiv preprint arXiv:2511.13649},
year={2025}
}
```
## 🤝 We're Hiring!
We're actively looking for **Research Scientists**, **Engineers**, and **Interns** to work on foundational generative models and their applications. Interested candidates please send your resume to: **jingpeng.gp@alibaba-inc.com**
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icon.png

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# 模型唯一标识
modelCode=1857
# 模型名称
modelName=z-image_pytorch
# 模型描述
modelDescription=Z-Image 是一个强大的、高效的图像生成模型。
# 应用场景
processType=推理
# 算法类别
appScenario=以文生图
# 框架类型
frameType=pytorch
# 加速卡类型
accelerateType=BW1000
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import torch
from diffusers import ZImagePipeline
# 1. Load the pipeline
# Use bfloat16 for optimal performance on supported GPUs
pipe = ZImagePipeline.from_pretrained(
"/home/dengjb/download/Tongyi-MAI/Z-Image-Turbo",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=False,
)
pipe.to("cuda")
# [Optional] Attention Backend
# Diffusers uses SDPA by default. Switch to Flash Attention for better efficiency if supported:
# pipe.transformer.set_attention_backend("flash") # Enable Flash-Attention-2
# pipe.transformer.set_attention_backend("_flash_3") # Enable Flash-Attention-3
# [Optional] Model Compilation
# Compiling the DiT model accelerates inference, but the first run will take longer to compile.
# pipe.transformer.compile()
# [Optional] CPU Offloading
# Enable CPU offloading for memory-constrained devices.
# pipe.enable_model_cpu_offload()
prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
# 2. Generate Image
image = pipe(
prompt=prompt,
height=1024,
width=1024,
num_inference_steps=9, # This actually results in 8 DiT forwards
guidance_scale=0.0, # Guidance should be 0 for the Turbo models
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("example.png")
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HIP_VISIBLE_DEVICES=0 python run.py
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