Welcome to the world of video generation with LAMP! Here, we will guide you through the process of using LAMP for creating videos based on a few input examples. With LAMP, you can generate high-quality videos from just 8 to 16 training videos using a single GPU. Let’s dive in!

Getting Started

Before we begin with the installation, you must have the following prerequisites:

• Operating System: Ubuntu 18.04
• CUDA Version: 11.3
• Python Version: 3.8

Installation Steps

Follow these steps to install LAMP and its dependencies:

# Clone the repository
git clone https://github.com/RQ-Wu/LAMP.git
cd LAMP

# Create a virtual environment
conda create -n LAMP python=3.8
conda activate LAMP

# Install the required packages
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt
pip install xformers==0.0.13

Understanding the Code Installation

Think of installing LAMP as setting up a new kitchen for cooking. You need to make sure you have all the necessary equipment and ingredients ready before you start.

• The git clone command is like purchasing a kitchen set; it brings all the tools you need into your home.
• Creating a virtual environment with conda is like arranging a special cooking space to keep things organized.
• Installing packages with pip ensures you have spices and ingredients that add flavor to your dishes, allowing you to create something beautiful.

Now that your kitchen is set, you’re ready to cook up some amazing videos!

Preparing Weights and Data

1. Download pre-trained T2I diffusion models from Hugging Face. LAMP uses the Stable Diffusion v1.4 model as its backbone.

2. For training data, you can collect your own videos or access pre-made datasets from various sources like Pexels.

Training the Model

After you set up everything, let’s train the model:

# Training code to learn a motion pattern
CUDA_VISIBLE_DEVICES=X accelerate launch train_lamp.py config=configs/horse-run.yaml

# Training code for video editing
CUDA_VISIBLE_DEVICES=X accelerate launch train_lamp.py config=configs/run.yaml

Generating Video through Inference

Now that we’ve trained our model, let’s use it to generate videos:

# Motion Pattern generation
python inference_script.py --weight .my_weight/turn_to_smile/unet --pretrain_weight .checkpoints/stable-diffusion-v1-4 --first_frame_path .benchmark/turn_to_smile/head_photo_of_a_cute_girl,_comic_style.png --prompt "head photo of a cute girl, comic style, turns to smile"

# Video Editing
python inference_script.py --weight .outputs/run/unet --pretrain_weight .checkpoints/stable-diffusion-v1-4 --first_frame_path .benchmark/editing_a_girl_runs_beside_a_river,_Van_Gogh_style.png --length 24 --editing

Troubleshooting

If you encounter issues while following these steps, consider the following troubleshooting ideas:

• Check if your CUDA version matches the one specified in the documentation.
• Ensure all the required packages are correctly installed in your virtual environment.
• If you face GPU memory issues, reduce the batch size or simplify the model architecture.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now you’re equipped to generate stunning videos using LAMP! Dive into the fascinating world of video creation and let your creativity flow!