Instructions to use AdamJoyse/MistoLine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AdamJoyse/MistoLine with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AdamJoyse/MistoLine", 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:
- e0ad6a36a98f4d4029b36ffdca7e0e1b6c5b1adef10144a9dd64461889ff31a5
- Size of remote file:
- 10.3 MB
- SHA256:
- 49f5201f92859fdc495ac3fe1a1ec258342acdf1cfae28a5d809a9bdf2b8986d
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