Instructions to use dn6/tiny-Z-Image-Turbo-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dn6/tiny-Z-Image-Turbo-transformer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dn6/tiny-Z-Image-Turbo-transformer", 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
- Xet hash:
- 12a7f3cfe412984cf574ae6d8a0bd919789782a40d2f5371bf36bd9dd00a8221
- Size of remote file:
- 4.36 GB
- SHA256:
- 08dba1814328fb30b3a92744e89b9299c8676c90a26d1a82a60925eb971b2540
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