Instructions to use AdversaLLC/Z-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AdversaLLC/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("AdversaLLC/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

- Xet hash:
- 2d41c8e946f6d22b2a8000a57d8bd53ab3f9430df033126d1b8c5b9e8697d1e1
- Size of remote file:
- 152 kB
- SHA256:
- 4568ca559b997fc38f57dc1c3f5b1da3a3c144ae12419caa855ced972bf8c7aa
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