Instructions to use MirageML/SlimanMansour with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MirageML/SlimanMansour with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MirageML/SlimanMansour", 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
Download text_encoder/pytorch_model.bin from MirageML/SlimanMansour: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/MirageML/SlimanMansour/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://MirageML/SlimanMansour/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MirageML/SlimanMansour/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 815be9cab4221b1da4dd26433ad580970a4120a2bf392fd74e7d055b7fe09ffe
- Size of remote file:
- 1.36 GB
- SHA256:
- 677b19f9a48316386d9c5e9f0c500f3c31927a8ec7c5e09f17c2a43e444de49c
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