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 --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]"
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 --repo_type=model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("google/mt5-xxl", cache_dir="data/.cache")
model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-xxl", cache_dir="data/.cache")

Debug Training

bash t2v_debug_single.sh

TODO

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