Instructions to use tiny-random/z-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tiny-random/z-image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tiny-random/z-image", 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:
- 8eaa79b0c1d0f841a28df1e6667390ad0fce759c1a89b77819a5fe92d69567da
- Size of remote file:
- 2.46 MB
- SHA256:
- 25eeb02baf45bca7a4d6d353ee850b77a57f0f589a8718c5b00b3a48b8d3ea9f
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