Instructions to use liujx233/OneModelForAll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use liujx233/OneModelForAll with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("liujx233/OneModelForAll", 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:
- 07c53e49c5d4bc0f829e3999be5fb8d63a2564e25e983374db5b9b1366da6f62
- Size of remote file:
- 2.08 MB
- SHA256:
- c9b4d669f12a48641b06486b720f26fe8e688bdbd96c7de5e623bfc060f484c0
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