LLaVA-Onevision: Your Guide to Using a Multimodal Model

Welcome to the world of multimodal large language models! In this blog post, we will walk you through how to use the LLaVA-Onevision model, an innovative tool designed for image-to-text tasks, and more. Whether you’re looking to push your AI capabilities or just curious about this technology, you’re in the right place!

Understanding the LLaVA-Onevision Model

The LLaVA-Onevision model is akin to a highly skilled translator, capable of interpreting complex images and converting them into textual descriptions. Picture a multilingual tour guide who seamlessly switches between languages as they point out fascinating sights – that’s how LLaVA-Onevision operates, but with images and text instead!

Developed by fine-tuning Qwen2 on various multimodal instruction-following data, this model can handle image outputs across single-image, multi-image, and even video scenarios. This versatility makes it an unprecedented tool for AI researchers and enthusiasts alike.

How to Use the Model

Before you dive in, ensure that you have the necessary dependencies. You will need to install the transformers library from the specified branch or have the transformers version 4.45.0. Below, we outline two methods for utilizing this model.

Using the Pipeline Method

Follow these steps to implement the LLaVA-Onevision model using the pipeline API:

from transformers import pipeline
from PIL import Image
import requests

model_id = "llava-hfllava-onevision-qwen2-0.5b-ov-hf"
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline("image-to-text", model=model_id)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)

conversation = [{
    "role": "user",
    "content": [
        {"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
        {"type": "image"}
    ]
}]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})

print(outputs)  # Outputs the generated text

Using Pure Transformers

If you’re looking for a more granular approach, you can run the following script using pure transformers:

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

model_id = "llava-hfllava-onevision-qwen2-0.5b-ov-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
).to(0)

processor = AutoProcessor.from_pretrained(model_id)

conversation = [{
    "role": "user",
    "content": [
        {"type": "text", "text": "What are these?"},
        {"type": "image"}
    ]
}]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

image_file = "http://images.cocodataset.org/val2017/00000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)

inputs = processor(images=raw_image, text=prompt, return_tensors="pt").to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)

print(processor.decode(output[0][2:], skip_special_tokens=True))  # Decodes the output

Optimizing Your Model

To enhance your experience with the LLaVA-Onevision, you may want to incorporate optimizations such as 4-bit quantization or Flash-Attention 2. By doing so, you can ensure smoother and faster generation of outputs, bringing your projects to life with even greater efficiency.

Troubleshooting

If you encounter issues while using the LLaVA-Onevision model, consider the following troubleshooting ideas:

• Ensure Dependencies are Installed: Double-check that you have the latest version of the transformers library and any additional libraries such as flash-attn for improvements.
• Check Prompt Formats: Ensure that your prompt is formatted correctly in accordance with the requirements for either method used.
• Device Compatibility: Ensure that you are using a CUDA-compatible GPU, especially if utilizing floating-point precision or quantization methods.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.