Welcome! In this guide, we will delve deep into the LLaVA-Video-LLaMA-3 model, aimed at enhancing video understanding capabilities through advanced AI methodologies. Whether you’re looking to leverage the model for your projects or simply wanting to understand its functionalities, we have you covered!

What is LLaVA-Video-LLaMA-3?

LLaVA-Video-LLaMA-3 is a state-of-the-art model designed for video understanding tasks. By using LLaMA-3 as its foundational language model, it integrates visual recognition and language comprehension in a seamless manner. Imagine it as a multilingual interpreter who can watch videos and describe their content in real-time!

Updates

• June 4, 2024: The codebase now supports video data fine-tuning for video understanding tasks.
• May 14, 2024: The base code has been upgraded to llava-next (llava-v1.6), ensuring compatibility with the latest models like LLaMA-3 and others.

Model Details

The architecture of LLaVA-Video-LLaMA-3 includes several interesting components:

• Video Frame Sampling: Utilizes CLIP-ViT-L-336px as the image encoder with a sampling rate dependent on the number of frames.
• Template: Follows the LLaVA-v1 conversational structure for engaging interactions.
• Architecture: Features a combination of visual encoder, MLP adapter, and LLM backbone to process video inputs effectively.

How to Use LLaVA-Video-LLaMA-3

To start harnessing the power of this model, follow these simple steps to set it up:

Step 1: Installation

First, install the LLaVA model via pip:

pip install git+https://github.com/Victorwz/LLaVA-Video-Llama-3.git

Step 2: Load the Model and Perform Inference

Below is a streamlined process to load the model:

python
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import requests
import cv2
import torch
import base64
import io
from io import BytesIO
import numpy as np

# Load model and processor
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = get_model_name_from_path('weizhiwang/LLaVA-Video-Llama-3')

tokenizer, model, image_processor, context_len = load_pretrained_model(
    'weizhiwang/LLaVA-Video-Llama-3', None, model_name, False, False, device=device)

# Prepare image input
url = 'https://github.com/PKU-YuanGroup/Video-LLaVA/raw/main/video/lava/serve/examples/sample_demo_1.mp4'

def read_video(video_url):
    response = requests.get(url)
    if response.status_code != 200:
        print("Failed to download video")
        exit()
    else:
        with open('tmp_video.mp4', 'wb') as f:
            for chunk in response.iter_content(chunk_size=1024):
                f.write(chunk)
        video = cv2.VideoCapture('tmp_video.mp4')
    base64Frames = []
    while video.isOpened():
        success, frame = video.read()
        if not success:
            break
        _, buffer = cv2.imencode('.jpg', frame)
        base64Frames.append(base64.b64encode(buffer).decode('utf-8'))
    video.release()
    print(len(base64Frames), "frames read.")
    return base64Frames

video_frames = read_video(video_url=url)
image_tensors = []
sampling_interval = int(len(video_frames) / 10)

for i in range(0, len(video_frames), sampling_interval):
    rawbytes = base64.b64decode(video_frames[i])
    image = Image.open(io.BytesIO(rawbytes)).convert('RGB')
    image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0].half().cuda()
    image_tensors.append(image_tensor)

# Prepare inputs for the model
text = '\n'.join([f"Image {i}" for i in range(len(image_tensors))]) + '\nWhy is this video funny?'
conv = conv_templates['llama_3'].copy()
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()

# Autoregressively generate text
with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images=image_tensors,
        do_sample=False,
        max_new_tokens=512,
        use_cache=True)

outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
print(outputs[0])

This code fetches a video, processes it into frames, and uses the model to generate descriptive text about its contents.

Troubleshooting

While using the LLaVA-Video-LLaMA-3 model, you might encounter issues. Here are some common problems and their solutions:

• Failed to download video: Ensure that the URL is correct and that the server is accessible.
• CUDA errors: If CUDA is not installed or configured correctly, make sure your GPU drivers and CUDA toolkit are set up properly. If you’re not using a GPU, check that the model is set to load on CPU.
• Missing libraries: Ensure all necessary packages are installed and up to date, especially `torch`, `cv2`, and `requests`.

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

Fine-Tuning LLaVA-Llama-3

If you’re interested in fine-tuning the model with your own video instruction data, you can refer to the forked LLaVA-Video-Llama-3 GitHub repository for detailed data preparation and scripts.

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.