CogVideoX-5b-i2v Physics-IQ Benchmark

This repository contains all artifacts required to reproduce Physics-IQ–style benchmarking for CogVideoX, including evaluation scripts, generated samples, and results.

Overview

Physics-IQ benchmarks aim to evaluate a video generation model’s physical reasoning and temporal consistency, such as:

• Object permanence
• Gravity and motion consistency
• Cause–effect relationships
• Temporal coherence across frames

Important: This benchmark is intended for informational and comparative analysis. It does not claim state-of-the-art performance.

Environment & Requirements

Hardware (Recommended)

Running CogVideoX reliably for this benchmark requires significant compute:

• GPU: NVIDIA H100
• VRAM: ≥ 40GB (minimum), 80GB recommended
• RAM: ≥ 64GB
• Disk: ≥ 100GB free (videos + intermediate artifacts)

Lower-end GPUs may run out of memory or require aggressive offloading.

Software

• Python 3.10+
• CUDA-compatible PyTorch
• ffmpeg / ffprobe
• Jupyter Notebook

Video Generation

• Videos are generated using CogVideoX-5b-I2V
• Outputs are saved as .mp4
• Frame rate and resolution are kept consistent across runs

Results

Achieved a physics-IQ-benchmark of 32.3%