Instructions to use MIN-Lab/minWM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MIN-Lab/minWM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MIN-Lab/minWM", 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
- Xet hash:
- 7edc8a903d93a6aa162272a759244bced7c358606e395e49b9dd84ad432a151d
- Size of remote file:
- 5.96 GB
- SHA256:
- c654bc3a69f8c0e5bf6f7615b4020759510098d09e6bebffede4586260f89b76
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.