Clone the repository of VP2 and install the required dependencies as well as the data containing task instance specifications following the instructions of VP2.

We have made sure that there are no dependency conflicts between ivideogpt and vp2, so if you encounter trouble in the environments, you can follow these:

1. Create a new virtual environment.
2. Install the iVideoGPT dependencies as described in the Installation section of ivideopgt/README.md.
3. Copy the provided vp2_requirements.txt file into the downloaded VP2 folder.
4. Run pip install -r vp2_requirements.txt.

Follow the steps below to integrate the iVideoGPT interface into VP2:

1. Update the path in ivideogpt_interface.py: Go to line 12, where you will see sys.path.append("/dev/null/ivideogpt").Replace /dev/null/ivideogpt with the absolute path to your local iVideoGPT folder. This step ensures relative paths are imported correctly.
2. Place ivideogpt_interface.py in the correct location: Copy it to vp2/vp2/models/ivideogpt_interface.py. You should see torch_fitvid_interface.py in the same directory.
3. Place ivideogpt.yaml in the correct location: Copy it to vp2/vp2/scripts/configs/model/ivideogpt.yaml. You should see fitvid.yaml in the same directory.

To test your trained iVideoGPT models on VP2:

1. In ivideogpt.yaml, specify the following: config_name, pretrained_vqgan_name_or_path and pretrained_transformer_path
2. Refer to script.sh for example usage instructions (Note: The working directory for these instructions is assumed to be vp2/vp2).