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:
- 0e42b44b27d6e4809704772d9cd35efe9bdf887580517bd5abc6cb3600dbea86
- Size of remote file:
- 3.44 GB
- SHA256:
- 1b3d20e0969fcf012e4ffd4e02664704298797ba37ce18784b8a72b01316e5d3
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