Instructions to use nnpy/blip-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nnpy/blip-image-captioning 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="nnpy/blip-image-captioning")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("nnpy/blip-image-captioning") model = AutoModelForImageTextToText.from_pretrained("nnpy/blip-image-captioning") - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: image-to-text | |
| ## Usage: | |
| ``` | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| import torch | |
| from PIL import Image | |
| processor = BlipProcessor.from_pretrained("prasanna2003/blip-image-captioning") | |
| if processor.tokenizer.eos_token is None: | |
| processor.tokenizer.eos_token = '<|eos|>' | |
| model = BlipForConditionalGeneration.from_pretrained("prasanna2003/blip-image-captioning") | |
| image = Image.open('file_name.jpg').convert('RGB') | |
| prompt = """Instruction: Generate a single line caption of the Image. | |
| output: """ | |
| inputs = processor(image, prompt, return_tensors="pt") | |
| output = model.generate(**inputs, max_length=100) | |
| print(processor.tokenizer.decode(output[0])) | |
| ``` |