--- license: apache-2.0 language: - en pipeline_tag: text-to-image library_name: diffusers --- ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer [](https://tongyi-mai.github.io/Z-Image-blog/) [](https://github.com/Tongyi-MAI/Z-Image) [](https://huggingface.co/Tongyi-MAI/Z-Image) [](https://huggingface.co/spaces/Tongyi-MAI/Z-Image) [](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image) [](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) Welcome to the official repository for the Z-Image(造相)project! ## 🎨 Z-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 | 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} } ```
