Instructions to use schdoel/sd-class-Flower-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use schdoel/sd-class-Flower-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("schdoel/sd-class-Flower-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:
- 1d067e34c9ae0a2f91f0f3ae2c7d27f2f535183f104ac3668696bf397764ae5e
- Size of remote file:
- 74.3 MB
- SHA256:
- 49871c35306837996acdc9075032b5097a4f57eb591e63647fc39f10ea4be7f7
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