Instructions to use Aybeeceedee/knollingcase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aybeeceedee/knollingcase with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aybeeceedee/knollingcase", 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:
- b0aaa22ea9d7e4377d6100ba4094e1be906a15f299d55d70e0d2d6defaf01dba
- Size of remote file:
- 2.13 GB
- SHA256:
- cf836e65a7ab8a7ef29395a3322f53a8b1dabe9a4aeffbf16ea49c68bb925033
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.