Instructions to use Azazelle/LongClip-L-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azazelle/LongClip-L-diffusers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Azazelle/LongClip-L-diffusers") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Azazelle/LongClip-L-diffusers") model = AutoModelForZeroShotImageClassification.from_pretrained("Azazelle/LongClip-L-diffusers") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c3914f302247cc36c17a5105e6caa950bfa7a4de9144b95086092295a1e5bac
- Size of remote file:
- 1.71 GB
- SHA256:
- 0e36f1a3d8d1724797adf711100b63afc7365367c274e92e0ffa6aba874c63f1
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