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