Instructions to use xcreates/Z-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xcreates/Z-Image-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xcreates/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Upload complete model
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-to-image
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library_name: diffusers
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---
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**See Z-Image-Turbo in action - [demonstration video - coming soon](https://youtube.com/xcreate)**
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## Usage Notes
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#### Coming soon
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##### For more details see [demonstration video - coming soon](https://youtube.com/xcreate) or visit [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo).
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## Disclaimer
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We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.
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