1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61# 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
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
### 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%**
---