Welcome to the world of LLaVA-OneVision! This blog post aims to guide you through using the LLaVA-OneVision model, designed for multimodal capabilities, allowing it to analyze and interact with images and various data types. Get ready to dive into the details!

Table of Contents

• Model Summary
• Using LLaVA-OneVision
• Limitations
• Training Details
• License Information
• Citation

Model Summary

The LLaVA-OneVision model comprises 0.5772B parameters and has been trained on the LLaVA-OneVision Dataset. It is based on the Qwen2 language model with a context window of 32K tokens. Here are some key details:

• Repository: LLaVA-VLLLaVA-NeXT
• Project Website: llava-onevision.lmms-lab.com
• Paper: LLaVA-OneVision Paper
• Languages Supported: English, Chinese

Using LLaVA-OneVision

Intended Use

The model interacts with images, videos, and multi-image inputs. Feel free to share your outcomes in the Community tab!

Generation Process

To leverage LLaVA-OneVision in your projects, follow these steps:

python
# Install via pip
pip install git+https://github.com/LLaVA-VLLLaVA-NeXT.git

# Import necessary modules
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates
from PIL import Image
import requests
import copy
import torch

# Load pretrained model
pretrained = "lmms-lab/llava-onevision-qwen2-7b-si"
model_name = "llava_qwen"
device = "cuda"
device_map = None

tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, model_name, device_map=device_map)

# Prepare the image
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
image_tensor = process_images(image, image_processor, model.config)
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]

# Set up the conversation template
conv_template = "qwen_1_5"
question = DEFAULT_IMAGE_TOKEN + " What is shown in this image?"

conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()

# Tokenize input
input_ids = tokenizer.encode(prompt_question, return_tensors='pt').unsqueeze(0).to(device)

# Generate response
cont = model.generate(input_ids, images=image_tensor, do_sample=False, temperature=0, max_new_tokens=4096)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)

# Print outputs
print(text_outputs)

Imagine using LLaVA-OneVision as a highly skilled art critic. Just as the critic studies various artworks, the model processes images, analyzes them, and generates descriptive and insightful commentary based on given questions. The process starts with loading the model and image, followed by asking questions about the images, just like asking a critic during a gallery tour to elaborate on what they see!

Limitations

Like any advanced model, LLaVA-OneVision has its constraints. Be mindful of the following:

• Performance may vary based on the quality and type of the input data.
• Some tasks may lead to inaccuracies due to the differing context in input scenarios.
• While multimodal, certain complexities in data interaction may still pose challenges.

Training Details

The model was trained using a series of stages:

• Pretraining Stage: Leveraging 558K data
• Mid Stage: A mixture of 4.7M high-quality synthetic data
• Final Image Stage: Focused on single images across 3.6M data points
• OneVision Stage: Combined 1.6M single/multi-image/video data

License Information

The model is licensed under the Apache 2.0 license, ensuring usability and flexibility for developers.

Citation

If you wish to reference this work, make sure to cite accordingly.

Troubleshooting

If you encounter any challenges while using LLaVA-OneVision, consider the following troubleshooting tips:

• Ensure your dependencies are up to date, especially the torch library.
• The image format should be supported (JPEG, PNG) to avoid loading errors.
• Check if your GPU is correctly set up and accessible for model loading.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.