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", 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:
- 4a2085498c49d3a336d65c8410ee4a32e3334a1f257c945734c636732caba8ac
- Size of remote file:
- 2.13 GB
- SHA256:
- 392970184173ffb580a8a54fde4e872787de09e32cb67e82c5aab66eeb0d095f
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