InternVideo2.5 represents an advanced video multimodal large language model (MLLM), extending upon InternVL2.5 with the incorporation of long and rich context (LRC) modeling.

This enhancement facilitates improved perception of fine-grained details and the comprehension of extended temporal structures.

What is InternVideo2.5?

InternVideo2.5 is an open-source video understanding model that excels at tasks like:

• Video classification
• Action recognition
• Temporal localization
• Video captioning

Built on PyTorch, it leverages advanced architectures like Vision Transformers (ViTs) and is pretrained on large datasets for robust performance.

Prerequisites

Before proceeding with the installation, confirm that your system satisfies the following requirements:

• Operating System: Windows 10 or later
• Python Version: 3.8 or newer
• CUDA: Version 11.0 or higher (for GPU acceleration)
• Storage Requirements: A minimum of 20GB available for the model and dependencies
• RAM: At least 16GB (recommended)
• GPU (Optional but Recommended): NVIDIA GPU with a minimum of 8GB VRAM

Step 1: Install Python and pip

If Python is not already installed, obtain the latest version from the official Python website. Ensure that the installation process includes adding Python to the system's PATH environment variable.

To verify installation, execute the following commands in a command prompt:

python --version
pip --version

Step 2: Establish a Virtual Environment

Creating a virtual environment is strongly recommended to encapsulate dependencies specific to InternVideo2.5 and mitigate compatibility issues.

cd your_project_directory
python -m venv internvideo_env
internvideo_env\Scripts\activate

Step 3: Install Required Dependencies

Utilize pip to install the essential packages:

pip install transformers==4.40.1 av imageio decord opencv-python flash-attn --no-build-isolation

Step 4: Model Acquisition

Retrieve the InternVideo2.5 model from the Hugging Face Model Hub:

from transformers import AutoModel, AutoTokenizer

model_path = 'OpenGVLab/InternVideo2_5_Chat_8B'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()

Step 5: Environment Configuration

Ensure that system environment variables are correctly configured for CUDA:

setx PATH "%PATH%;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin"

Step 6: Data Preparation

Ensure input video files are in a supported format before processing with InternVideo2.5.

Step 7: Implementation Examples

Example 1: Extracting Key Frames from a Video

import cv2
import numpy as np

def extract_key_frames(video_path, output_folder, frame_interval=30):
    cap = cv2.VideoCapture(video_path)
    frame_count = 0

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        if frame_count % frame_interval == 0:
            output_path = f"{output_folder}/frame_{frame_count}.jpg"
            cv2.imwrite(output_path, frame)

        frame_count += 1

    cap.release()

extract_key_frames("sample_video.mp4", "frames_output")

Example 2: Speech Transcription via OpenAI Whisper

import whisper

model = whisper.load_model("base")
result = model.transcribe("sample_video.mp4")
print(result["text"])

Example 3: Automated Video Captioning with InternVideo2.5

import torch
from some_video_processing_module import load_video

def generate_video_captions(video_path):
    pixel_values, num_patches_list = load_video(video_path, num_segments=128, max_num=1)
    pixel_values = pixel_values.to(torch.bfloat16).to(model.device)

    question = "Describe this video in detail."
    video_prefix = "".join([f"Frame{i+1}: \n" for i in range(len(num_patches_list))])
    question = video_prefix + question
    
    output, _ = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=None, return_history=True)
    print(output)

generate_video_captions("sample_video.mp4")

Step 8: Executing the Model

To execute InternVideo2.5, run the relevant script:

python your_script_name.py

Running Your First Video Analysis

Input Video Preparation

• Supported formats: MP4, MOV, AVI
• Resolution: 1920x1080 or lower recommended
• Duration: Optimized for 30s-5min clips

# Enhanced video loader with error handling
def safe_load_video(path):
    try:
        vr = VideoReader(path, ctx=cpu(0))
        return vr
    except Exception as e:
        print(f"Error loading {path}: {str(e)}")
        return None

Comprehensive Processing Pipeline

1. Frame Extraction Strategies
   • Fixed interval sampling
   • Dynamic scene detection
   • Keyframe extraction
2. Multi-Modal Prompt Engineering

prompt_template = """
Analyze this video from {timestamp} to {duration}:
{query}

Consider these aspects:
- Object interactions
- Temporal relationships
- Scene context
- Action sequences
"""

Advanced Configuration Tips

Performance Optimization

# Enable advanced optimizations
model = AutoModel.from_pretrained(...).half().to('cuda')
model = torch.compile(model)  # PyTorch 2.0 feature

Memory Management

• Gradient Checkpointing: model.gradient_checkpointing_enable()
• Frame Chunking: Process video in 30s segments
• VRAM Monitoring: Use nvidia-smi -l 1

Troubleshooting Common Issues

Error: "CUDA Out of Memory"

1. Reduce batch size: num_segments=64
2. Enable garbage collection:

import gc
gc.collect()
torch.cuda.empty_cache()

Video Processing Errors

• Corrupted Files: Use ffprobe your_video.mp4

Codec Issues: Convert to H.264 using FFmpeg:

ffmpeg -i input.avi -c:v libx264 output.mp4

Citation

If utilizing InternVideo2.5 for research purposes, please cite:

@article{wang2025internvideo,
title={InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling},
author={Wang, Yi and Li, Xinhao and Yan, Ziang and others},
journal={arXiv preprint arXiv:2501.12386},
year={2025}
}

Real-World Application

1. Content Moderation: Automatically detect policy violations in video uploads
2. Sports Analytics: Track player movements and game dynamics
3. Educational Content: Generate automatic lecture summaries with key concepts

# Example: Educational Video Analyzer
def generate_lecture_summary(video_path):
    analysis = model.analyze(video_path)
    return f"""
    Lecture Summary:
    - Key Topics: {analysis['topics']}
    - Visual Aids: {analysis['diagrams']}
    - Recommended Study Points: {analysis['important_concepts']}
    """

Conclusion

By adhering to the aforementioned steps, users can successfully install and execute InternVideo2.5 on a Windows system, leveraging its capabilities for advanced video analysis and multimodal comprehension.

References

1. Run DeepSeek Janus-Pro 7B on Mac: A Comprehensive Guide Using ComfyUI
2. Run DeepSeek Janus-Pro 7B on Mac: Step-by-Step Guide
3. Run DeepSeek Janus-Pro 7B on Windows: A Complete Installation Guide
4. Installation and Running of InternVideo2.5 on macOS