CogVideoX-5b-i2v Physics-IQ Benchmark

January 6, 2026 · View on GitHub

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

This repository applies that evaluation protocol to CogVideoX.

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

Environment & Requirements

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%