Instructions to use nnpy/blip-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nnpy/blip-image-captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="nnpy/blip-image-captioning")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("nnpy/blip-image-captioning") model = AutoModelForImageTextToText.from_pretrained("nnpy/blip-image-captioning") - Notebooks
- Google Colab
- Kaggle
Commit ·
badd543
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Parent(s): 1b67c1b
Update README.md
Browse files
README.md
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@@ -15,7 +15,7 @@ model = BlipForConditionalGeneration.from_pretrained("prasanna2003/blip-image-ca
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image = Image.open('file_name.jpg').convert('RGB')
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prompt = """Instruction:
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output: """
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inputs = processor(image, prompt, return_tensors="pt")
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image = Image.open('file_name.jpg').convert('RGB')
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prompt = """Instruction: Generate a single line caption of the Image.
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output: """
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inputs = processor(image, prompt, return_tensors="pt")
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