Instructions to use Mariobilly/msch-painting01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Mariobilly/msch-painting01 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Mariobilly/msch-painting01") 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:
- d689afacacd2adc0ed790cd9b100dd2f09b3a283821bdfe5111937f7c1c5a6e5
- Size of remote file:
- 741 kB
- SHA256:
- 17b4407c46f9c7933a989b1425b4400ca852789321d091c0ec07a68ba01b7e99
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