| from transformers import BlipProcessor, BlipForConditionalGeneration |
| import torch |
|
|
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") |
|
|
| def generate_caption(image): |
| inputs = processor(image, return_tensors="pt") |
|
|
| with torch.no_grad(): |
| out = model.generate(**inputs, max_length=50, num_beams=5, early_stopping=True, no_repeat_ngram_size=2) |
|
|
| caption = processor.decode(out[0], skip_special_tokens=True) |
| return caption |
