Instructions to use p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli with timm:
import timm model = timm.create_model("hf_hub:p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli", pretrained=True) - Transformers
How to use p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli") model = AutoModel.from_pretrained("p1atdev/style_250412.vit_base_patch16_siglip_384.v2_webli") - Notebooks
- Google Colab
- Kaggle
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```py
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import torchvision.transforms.v2 as T
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image_size =
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preprocessor = T.Compose(
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[
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T.Resize(
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```py
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import torchvision.transforms.v2 as T
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image_size = 384
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preprocessor = T.Compose(
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[
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T.Resize(
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