Instructions to use can34/Modill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use can34/Modill with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("can34/Modill", torch_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:
- b16ec42b9a728486de5da4109a5e7d178f5acc37e17e459e4c21a055642e7470
- Size of remote file:
- 4.77 MB
- SHA256:
- 1acbf06e864f306986447b5e2caa8968d46c8951faf9f63acd6dd8c9a9bbcc6c
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