Instructions to use WGNW/chamcham_v1_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WGNW/chamcham_v1_checkpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WGNW/chamcham_v1_checkpoint", 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:
- 665c212924b4d757a1a2e216580d73d0fa3cc1bcddd53cce16daf620ee015210
- Size of remote file:
- 492 MB
- SHA256:
- 9948d5814320e8e4b6669882ac2c39863f0c53b8120a7be9e89677a3cc7eeec4
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