2023-02-15 03:55:46 +01:00
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import requests
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import torch
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from PIL import Image
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from transformers import BlipForConditionalGeneration
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from transformers import BlipProcessor
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2023-02-15 00:38:21 +01:00
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2023-02-15 03:55:46 +01:00
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
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# raw_image = Image.open('/tmp/istockphoto-470604022-612x612.jpg').convert('RGB')
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def caption_image(raw_image):
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inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
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out = model.generate(**inputs, max_new_tokens=100)
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return processor.decode(out[0], skip_special_tokens=True)
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