Instructions to use qingsheng/diffusion_model_wbx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use qingsheng/diffusion_model_wbx with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("qingsheng/diffusion_model_wbx", 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:
- 4216af25869f579ac8f1de86fad36c1905de5d9193059b700784b828828ae427
- Size of remote file:
- 74.3 MB
- SHA256:
- e3d0c12ce2b16fb6f83075f07f977928c91fe98a3405a92056f5419d09fec9a2
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