Instructions to use Tongyi-MAI/Z-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Tongyi-MAI/Z-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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## 🎨 Z-Image
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**Z-Image** is the foundation model behind Z-Image-Turbo, designed for high-quality image generation with strong controllability, broad stylistic coverage, and support for downstream development.
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It serves as the primary community model in the ⚡️- Image family, while Z-Image-Turbo focuses on high-speed inference.
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### 🌟 Key Features
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## 🎨 Z-Image
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**Z-Image** is the foundation model behind Z-Image-Turbo, designed for high-quality image generation with strong controllability, broad stylistic coverage, and support for downstream development. It serves as the primary community model in the ⚡️- Image family, while Z-Image-Turbo focuses on high-speed inference.
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### 🌟 Key Features
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