Instructions to use NickelK/model3_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NickelK/model3_0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NickelK/model3_0", 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
- Draw Things
- DiffusionBee
- Xet hash:
- 2f394b928c09d81b3ad8a2557156699b3ecdca240f36fbef65dfc81f46b92006
- Size of remote file:
- 167 MB
- SHA256:
- 5da037836bf3e87dd21482307342ceff87996a33ee9fbd38fe0c7662b0350d23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.