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

Rectified Flow Loss

CUDA_VISIBLE_DEVICES=2 torchrun --standalone --nproc_per_node 1 scripts/misc/eval_loss.py configs/opensora-v1-2/misc/eval_loss.py --data-path /mnt/nfs-207/sora_data/meta/img_1k.csv --ckpt-path /home/lishenggui/projects/sora/Open-Sora-dev/outputs/207-STDiT3-XL-2/epoch0-global_step9000/

CUDA_VISIBLE_DEVICES=3 torchrun --standalone --nproc_per_node 1 scripts/misc/eval_loss.py configs/opensora-v1-2/misc/eval_loss.py --data-path /mnt/nfs-207/sora_data/meta/vid_100.csv --ckpt-path /home/lishenggui/projects/sora/Open-Sora-dev/outputs/207-STDiT3-XL-2/epoch0-global_step9000/

CUDA_VISIBLE_DEVICES=3 torchrun --standalone --nproc_per_node 1 scripts/misc/eval_loss.py configs/opensora-v1-2/misc/eval_loss.py --data-path /mnt/nfs-207/sora_data/meta/vid_100.csv --ckpt-path /home/lishenggui/projects/sora/Open-Sora-dev/outputs/207-STDiT3-XL-2/epoch0-global_step9000/ --resolution 720p

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

• Install cupy: follow https://docs.cupy.dev/en/stable/install.html

• To use flolpips model, download from https://github.com/danier97/flolpips/blob/main/weights/v0.1/alex.pth and place it under: eval/vae/flolpips/weights/v0.1/alex.pth

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