Instructions to use BryanW/43.a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BryanW/43.a with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BryanW/43.a", 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:
- 8ec3df3fcf6a39b04aac3bb272a6e9fb79a4e98e07be1992f4460bc895ec2b17
- Size of remote file:
- 2.89 GB
- SHA256:
- d0df17ac774a3d92bf6d3574b8f661f5e93ec4a2793d3c52990e8bd16c22edf9
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