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--- |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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inference: false |
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pipeline_tag: image-text-to-text |
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--- |
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## Sharded BLIP-2 Model Card - flan-t5-xl |
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<a href="https://colab.research.google.com/gist/pszemraj/0822b7f28b14405f10cfd382296873de/blip2-flan-t5-xl-sharded-example.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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This is a sharded version of the [blip2-flan-t5-xl](https://huggingface.co/Salesforce/blip2-flan-t5-xl) which leverages [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) for image-to-text tasks such as image captioning and visual question answering. |
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- this model repo is sharded so it can be easily loaded on low-RAM Colab runtimes :) |
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- Refer to the [original model card](https://huggingface.co/Salesforce/blip2-flan-t5-xl) for more details about the model description, intended uses, and limitations, as well as instructions for how to use the model on CPU and GPU in different precisions. |
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## Usage |
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Refer to the original model card for details or see [this blog post](https://huggingface.co/blog/blip-2#using-blip-2-with-hugging-face-transformers). Here is how you can use it on CPU: |
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Install |
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Requires the current `main` of transformers (_at time of writing_): |
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```bash |
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pip install accelerate git+https://github.com/huggingface/transformers.git -U -q |
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``` |
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Use (_this is for CPU, check out the original model card/blog for `fp16` and `int8` usage_) |
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```python |
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import requests |
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from PIL import Image |
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from transformers import BlipProcessor, Blip2ForConditionalGeneration |
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model_name = "ethzanalytics/blip2-flan-t5-xl-sharded" |
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processor = BlipProcessor.from_pretrained(model_name) |
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model = Blip2ForConditionalGeneration.from_pretrained(model_name) |
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img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' |
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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question = "how many dogs are in the picture?" |
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inputs = processor(raw_image, question, return_tensors="pt") |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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``` |
