Instructions to use ffjefckds/sd-class-beer-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ffjefckds/sd-class-beer-32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ffjefckds/sd-class-beer-32", 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:
- fec6995297c086c1e2ef2007c08f85b0dcc2f2396086bfef26787e55ce59529f
- Size of remote file:
- 74.3 MB
- SHA256:
- 71d4a215b50d6e9e1b912462945495af76b089a02264d623c85e2e31219fb1af
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