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README.md
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---
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language: en
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license: mit
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tags:
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- vision
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- image-captioning
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---
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# InstructBLIP model
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InstructBLIP model using Flan-T5-xl as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al.
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Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
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## Model description
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InstructBLIP is a visual instruction tuned version of [BLIP-2](https://huggingface.co/docs/transformers/main/model_doc/blip-2). Refer to the paper for details.
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![InstructBLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg)
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## Intended uses & limitations
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Usage is as follows:
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```
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from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
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import torch
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from PIL import Image
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import requests
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model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-flan-t5-xl")
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processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.to(device)
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url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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prompt = "What is unusual about this image?"
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inputs = processor(images=image, text=prompt, return_tensors="pt")
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outputs = model.generate(
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**inputs,
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do_sample=False,
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num_beams=1,
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max_length=256,
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min_length=1,
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top_p=0.9,
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repetition_penalty=1.5,
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length_penalty=1.0,
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temperature=1,
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)
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generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
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print(generated_text)
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```
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Note that this shows unconditional generation of text given an image. You can also make the model continue a text prompt.
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### How to use
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For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/instructblip).
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