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:
- 0e3adfe4628c2da4af1c28c72fdb2d1e2cd721926c671ed48bcbef6a774a0659
- Size of remote file:
- 2.89 GB
- SHA256:
- 93435e4e0e3a679ad080dc0d597f81e1a9d14157b200d1a3a2a509f365fffada
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