Instructions to use k33pCum/stable-cascade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use k33pCum/stable-cascade with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("k33pCum/stable-cascade", 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:
- 666aef5dcc88e75ff3fc53ed946e630628ea63d1b0491d94e48c68a4e9547857
- Size of remote file:
- 6.25 GB
- SHA256:
- 76f506c71dbaac04ade47f31f9eb546f7fb6543adc4d7f44d24ab9e6177476fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.