Open-Sora/eval
2024-05-03 08:20:26 +00:00
..
vae fix flolpips 2024-05-03 08:18:51 +00:00
vbench
vbench_i2v a bunch of update 2024-04-24 09:23:24 +00:00
launch.sh update report 2024-04-24 09:27:33 +00:00
multiple.sh a bunch of update 2024-04-24 09:23:24 +00:00
README.md update eval readme 2024-05-03 08:20:26 +00:00
sample.sh a bunch of update 2024-04-24 09:23:24 +00:00

Evalution

Human evaluation

To conduct human evaluation, we need to generate various samples. We provide many prompts in assets/texts, and defined some test setting covering different resolution, duration and aspect ratio in eval/sample.sh. To facilitate the usage of multiple GPUs, we split sampling tasks into several parts.

# image (1)
bash eval/sample.sh /path/to/ckpt -1
# video (2a 2b 2c ...)
bash eval/sample.sh /path/to/ckpt -2a
# launch 8 jobs at once (you must read the script to understand the details)
bash eval/launch.sh /path/to/ckpt

VBench

VBench is a benchmark for short text to video generation. We provide a script for easily generating samples required by VBench.

# vbench tasks (4a 4b 4c ...)
bash eval/sample.sh /path/to/ckpt -4a
# launch 8 jobs at once (you must read the script to understand the details)
bash eval/launch.sh /path/to/ckpt

After generation, install the VBench package according to their instructions. Then, run the following commands to evaluate the generated samples.

bash eval/vbench/vbench.sh /path/to/video_folder

VBench-i2v

VBench-i2v is a benchmark for short image to video generation (beta version).

TBD

VAE

Dependencies

pip install decord
pip install pytorchvideo
pip install lpips
pip install scipy
# Also, if torchvision.transforms.augentation still use `functional_tensor` and cause error,change to use `_functional_tensor`, follow https://blog.csdn.net/lanxing147/article/details/136625264

Commands: carefule to change the setting to training setting

# metric can any one or list of: ssim, psnr, lpips, flolpips
python eval/vae/eval_common_metric.py --batch_size 2 --real_video_dir <path/to/original/videos> --generated_video_dir <path/to/generated/videos> --device cuda --sample_fps 24 --crop_size 256 --resolution 256 --num_frames 17 --sample_rate 1 --metric ssim psnr lpips flolpips