Instructions to use wipy/lusia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wipy/lusia with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wipy/lusia", 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 wipy/lusia: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/wipy/lusia/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://wipy/lusia/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wipy/lusia/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- a93429ccd007e0ec3dae199e565ca1c544634d442b2707c7a5fbee7479b0c9a1
- Size of remote file:
- 246 MB
- SHA256:
- 4fd2226a14891e3e7cee8901bd974bb07372f04ddc8aeaf2082cf277aba6c491
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