Instructions to use 8glabs/trained_models_osbi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 8glabs/trained_models_osbi with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("8glabs/trained_models_osbi", 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:
- be96a43ad5dc40d56d196cfc29b31451dc433ba03270fc0777518dace0dbced5
- Size of remote file:
- 492 MB
- SHA256:
- 9d9b9b7fe9c99ce51d7ef48dc86652f0aad3ddd7ffe9b1e9bf98a7845b03a2c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.