MakeLongVideo - Pytorch

Implementation of long video generation based on diffusion model.

Setup

Requirements

python3 -m pip install -r requirements.txt

Training

Prepare Stable Diffusion v1-4 pretrained weights

download from huggingface and put it in directory 'checkpoints' which is configured in configs/makelongvideo.yaml

Download webvid dataset

download webvid dataset into directory 'data/webvid' using https://github.com/m-bain/webvid repo. Then prepare dataset using command

python3 genvideocap.py

Download LAION400M dataset

download laion400m into directory 'data/laion400m'

Train

first train using resolution 128x128

accelerate launch --config_file ./configs/multigpu.yaml train.py --config configs/makelongvideo.yaml

then finetune in resolution 256x256, modify last line of configs/makelongvideo256x256.yaml according to your local epoch checkpoint

accelerate launch --config_file ./configs/multigpu.yaml train.py --config configs/makelongvideo256x256.yaml

Inference

# unwrap checkpoint first
TORCH_DISTRIBUTED_DEBUG=DETAIL accelerate launch train.py --config configs/makelongvideo.yaml --unwrap ./outputs/makelongvideo/checkpoint-3000

inference directly

python3 infer.py  --width 256 --height 256 --prompt "a panda is surfing"

inference using latents initialized by sample video

python3 infer.py  --width 256 --height 256 --prompt "a panda is surfing" --sample_video_path your_sample_video

inference by sample frame rate 6 (actual frame rate is 24/6==4)

python3 infer.py  --width 256 --height 256 --prompt "a panda is surfing" --speed 6

Todo

• generate 24 frames video of 256x256
• add fps control
• release pretrained checkpoint
• improve resolution to 512x512
• 1~2minutes video generation
• make story video

References

• Make-A-Video: https://github.com/lucidrains/make-a-video-pytorch
• Tune-A-Video: https://github.com/showlab/Tune-A-Video
• diffusers: https://github.com/huggingface/diffusers

Citations

@misc{Singer2022,
    author  = {Uriel Singer},
    url     = {https://makeavideo.studio/Make-A-Video.pdf}
}

@article{wu2022tuneavideo,
    title   = {Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation},
    author  = {Wu, Jay Zhangjie and Ge, Yixiao and Wang, Xintao and Lei, Stan Weixian and Gu, Yuchao and Hsu, Wynne and Shan, Ying and Qie, Xiaohu and Shou, Mike Zheng},
    journal={arXiv preprint arXiv:2212.11565},
    year    = {2022},
    note    = {under review}
}