Setting Up Text-to-Video on Ubuntu using LLaMA CPP

This guide provides detailed instructions for setting up a Text-to-Video system on Ubuntu using the LLaMA CPP model. We will use a pre-trained model to generate video content based on text input.

1. Install System Prerequisites

First, update your Ubuntu system and install the necessary dependencies. Open a terminal and run the following commands:

sudo apt update
sudo apt upgrade
sudo apt install python3 python3-pip git ffmpeg

2. Install Required Libraries

Install PyTorch and other required libraries:

pip install torch torchvision transformers moviepy

3. Clone the LLaMA CPP Repository

Next, clone the LLaMA CPP repository to your local machine:

git clone https://github.com/facebookresearch/llama.git
cd llama

4. Download Pre-trained Models

You will need to download pre-trained weights for the LLaMA model. Follow the instructions in the repository to download the model weights. This often involves accepting a license agreement. Once downloaded, place the model files in the appropriate directory within the cloned repository.

5. Prepare Your Text Input

Create a text file with your input text, which will be used to generate the video. For example:

echo "A beautiful sunset over the ocean" > input.txt

6. Create a Video Generation Script

The following script loads the model, processes the input text, and generates a video. Save this code in a file called text_to_video.py.

import torch
from transformers import LLaMATokenizer, LLaMAForCausalLM
import moviepy.editor as mpy

# Load the pre-trained LLaMA model and tokenizer
tokenizer = LLaMATokenizer.from_pretrained("llama/llama-7b")
model = LLaMAForCausalLM.from_pretrained("llama/llama-7b")

# Load input text
with open('input.txt', 'r') as file:
    input_text = file.read().strip()

# Tokenize input
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate text
with torch.no_grad():
    generated_ids = model.generate(input_ids, max_length=100)

generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

# Create a simple video using generated text as subtitles
def make_frame(t):
    return mpy.ColorClip((640, 480), color=(255, 255, 255)).set_duration(1)

video_clip = mpy.VideoClip(make_frame, duration=5)
video_clip = video_clip.set_duration(5).set_fps(24)

# Add text to video
video_clip = video_clip.set_duration(5).set_fps(24)
video_clip = video_clip.set_duration(5).set_fps(24).add_text(generated_text, color='black', fontsize=30, position='center')

# Write the result to a file
video_clip.write_videofile("output_video.mp4", fps=24)

This script processes the input text and generates a video where the generated text appears as subtitles.

7. Run the Video Generation Script

Run the script in your terminal to generate the video:

python3 text_to_video.py

This command will generate a video file named output_video.mp4 in the current directory.

8. View the Generated Video

You can view the generated video using any media player. For example, you can use ffplay:

ffplay output_video.mp4

9. Troubleshooting

If you encounter issues, consider the following:

  • Ensure that all libraries are installed correctly.
  • Check the compatibility of the model weights with your version of the LLaMA library.
  • Verify that the input text is properly formatted.

10. Conclusion

You have successfully set up a Text-to-Video generation system on Ubuntu using the LLaMA CPP model. This system can be expanded and refined to include more advanced features, such as different video styles and more complex text processing.