Instructions to use osanseviero/huggingface2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use osanseviero/huggingface2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("osanseviero/huggingface2", 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 osanseviero/huggingface2: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/osanseviero/huggingface2/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://osanseviero/huggingface2@refs/pr/2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/osanseviero/huggingface2/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 9992a9b6b6c1799ca0546978dd2270325139e49b3fa52ef7929ae9f863dfdbcc
- Size of remote file:
- 492 MB
- SHA256:
- 554a21a2d18cbe90297cd6a80e54b4a41dc4e20fb24361215e56e72d8f9680c2
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