Instructions to use universalml/sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/sd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/sd") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("universalml/sd") model = AutoModelForImageClassification.from_pretrained("universalml/sd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb032a45f4a2d1011279fbfb7ff38cc42d554c15a52a5b6ada841c8aac48608c
- Size of remote file:
- 4.09 kB
- SHA256:
- f0654419f4a6f5420dc5ba68a9710511998e564af61ddd387fa4e381c5f37107
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