blip.py

import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer, AutoModelWithLMHead

processor = BlipProcessor.from_pretrained(
    "Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained(
    "Salesforce/blip-image-captioning-large").to("cuda")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))