Instructions to use nan2/lcbanner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nan2/lcbanner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nan2/lcbanner", 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 nan2/lcbanner: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/nan2/lcbanner/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://nan2/lcbanner@refs/pr/2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nan2/lcbanner/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- c5d7dcf406ae6aff2585c1a43b4426b57f1fc5225769dada98a300611ceb6230
- Size of remote file:
- 246 MB
- SHA256:
- d4bcab1f73b087279223c11b5fc1dbf0119f82a1fbd65da493eb2258340ae3c7
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