mindone/scripts

• Utility Scripts
• • eval_videos_metrics.py
  • • • CLIP Score for Frame Consistency
      • CLIP Score for Textual Alignment
  • Reference

Utility Scripts

This folder is a collection of utility scripts, listed and explained below.

All scripts need to be run in the root path of project, unless otherwise noted.

eval_videos_metrics.py

This script contains code and scripts for diffusion model evaluation, e.g.,

• CLIP Score For frame Consistency
• CLIP Score for Textual Alignment

Note that all the above metrics are computed based on neural network models.

A convincing evaluation for diffusion models requires both visually qualitative comparision and quantitative measure. A higher CLIP score does not necessarily show one model is better than another.

CLIP Score for Frame Consistency

To compute the CLIP score on all frames of output video and report the average cosine similarity between all video frame pairs, please run

python ./scripts/eval_videos_metrics.py --video_data_dir <path-to-video-dir> --video_caption_path <path-to-video-caption-path> --model_name <HF-model-name>  --metric clip_score_frame

CLIP Score for Textual Alignment

To compute the average CLIP score between all frames of the output video and the corresponding editing prompts, please run

python ./scripts/eval_videos_metrics.py --video_data_dir <path-to-video-dir> --video_caption_path <path-to-video-caption-path> --model_name <HF-model-name>  --metric clip_score_text

Format of .csv:

video,caption
video_name1.mp4,"an airliner is taxiing on the tarmac at Dubai Airport"
video_name2.mp4,"a pigeon sitting on the street near the house"
...

Reference

[1] https://github.com/showlab/loveu-tgve-2023/tree/main