Instructions to use braintacles/brainblip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use braintacles/brainblip 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="braintacles/brainblip")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("braintacles/brainblip") model = AutoModelForImageTextToText.from_pretrained("braintacles/brainblip") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -31,7 +31,7 @@ processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base
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model = BlipForConditionalGeneration.from_pretrained("braintacles/brainblip").to("cuda")
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image_path_or_url = r"https://imagePath_or_url.jpg"
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raw_image = Image.open(requests.get(
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inputs = processor(raw_image, return_tensors="pt").to("cuda")
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out = model.generate(**inputs, min_length=40, max_new_tokens=75, num_beams=5, repetition_penalty=1.40)
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model = BlipForConditionalGeneration.from_pretrained("braintacles/brainblip").to("cuda")
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image_path_or_url = r"https://imagePath_or_url.jpg"
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raw_image = Image.open(requests.get(image_path_or_url, stream=True).raw) if image_path_or_url.startswith("http") else Image.open(image_path_or_url)
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inputs = processor(raw_image, return_tensors="pt").to("cuda")
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out = model.generate(**inputs, min_length=40, max_new_tokens=75, num_beams=5, repetition_penalty=1.40)
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