Instructions to use Andresdossa/Z-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Andresdossa/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("Andresdossa/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 Settings
- Draw Things
- DiffusionBee
Download assets/showcase_rendering.png from Andresdossa/Z-Image-Turbo: direct link, hf CLI and curl.
- Browser
- Download file 7.6 MB
-
https://huggingface.co/Andresdossa/Z-Image-Turbo/resolve/main/assets/showcase_rendering.png
- Command line
-
hf download hf://Andresdossa/Z-Image-Turbo/assets/showcase_rendering.png
-
curl -L -o showcase_rendering.png https://huggingface.co/Andresdossa/Z-Image-Turbo/resolve/main/assets/showcase_rendering.png
7.6 MB

- Xet hash:
- 8ea3b0a1745e046819fae7434a9cdf6173077f3eaf2e8affec9bfed293ddff52
- Size of remote file:
- 7.6 MB
- SHA256:
- 3556dd66be2200d53f957424e12ecf914ddf3eded151cde86c7353f8b231284f
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