Instructions to use Johnhex/Clam1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Johnhex/Clam1.2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Johnhex/Clam1.2", 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:
- 5286e07fb12e727139bdadb427261c90ff553b77a9cdfa5200d1f0227425f1ab
- Size of remote file:
- 492 MB
- SHA256:
- a25ff7e7be9f9148e7083b61a923c564dd9c9935dfd2eb25cd1b0f9b419efa55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.