Fast Video

This is currently based on Open-Sora-1.2.0: https://github.com/PKU-YuanGroup/Open-Sora-Plan/tree/294993ca78bf65dec1c3b6fb25541432c545eda9

Envrironment

Change the index-url cuda version according to your system.

conda create -n fastvideo python=3.10.12
conda activate fastvideo
pip3 install torch==2.5.0 torchvision  --index-url https://download.pytorch.org/whl/cu121
pip3 install -U xformers==0.0.28.post2 --index-url https://download.pytorch.org/whl/cu121
cd .. && git clone  https://github.com/huggingface/diffusers
cd diffusers && git checkout mochi && pip install -e . && cd ../FastVideo

pip install -e . && pip install -e ".[train]"
sudo apt-get update && apt install screen && pip install watch gpustat

Prepare Data & Models

We've prepared some debug data to facilitate development. To make sure the training pipeline is correct, train on the debug data and make sure the model overfit on it (feed it the same text prompt and see if the output video is the same as the training data)

python scripts/download_hf.py --repo_id=Stealths-Video/dummyVid --local_dir=data/dummyVid --repo_type=dataset
python scripts/download_hf.py --repo_id=Stealths-Video/mochi --local_dir=data/mochi --repo_type=model
python scripts/download_hf.py --repo_id=Stealths-Video/Mochi-Synthetic-Data --local_dir=data/Mochi-Synthetic-Data --repo_type=dataset

How to overfit

bash t2v_debug_multi.sh

Make sure to edit data/Mochi-Synthetic-Data/videos2caption.json such that this is only one video in the dataset (you can copy multiple annotations of the same video). Also make sure to edit the prompt in scripts/overfit.shto match the prompt in the training data. I observe the overfitting after 50 steps.

TODO

• Delete all npu related stuff.
• Remove inpaint.
• Create dummy debug data.
• Add Mochi
• Add Mochi VAE