Instructions to use milaidy/rainerr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use milaidy/rainerr with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("milaidy/rainerr", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 288194c530748dfc222ef65ea05c20071a293e8a68c7f2d0ad6378db6a2033c9
- Size of remote file:
- 2.13 GB
- SHA256:
- fb826ba1acd327df8a6c3402afd7d0cba8062c3c55fbbf2f98d8310c8ab7daeb
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