Instructions to use Janchan123/Z-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Janchan123/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("Janchan123/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/architecture.webp from Janchan123/Z-Image-Turbo: direct link, hf CLI and curl.
- Browser
- Download file 422 kB
-
https://huggingface.co/Janchan123/Z-Image-Turbo/resolve/main/assets/architecture.webp
- Command line
-
hf download hf://Janchan123/Z-Image-Turbo/assets/architecture.webp
-
curl -L -o architecture.webp https://huggingface.co/Janchan123/Z-Image-Turbo/resolve/main/assets/architecture.webp
422 kB

- Xet hash:
- c28b04b424339284f915fb912431d8f5adca7c1c4a9573f675333a1e4cea009b
- Size of remote file:
- 422 kB
- SHA256:
- 261af62ecc7e9749ae28e1d3a84e2f70a6c192d2017b7d8f020c7bff982ef59c
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