readme.md

---
license: mit
tags:
- text-to-video
- prompt-engineering
- video-generation
- llm
- rag
- research
datasets:
- junchenfu/llmpopcorn_prompts
pipeline_tag: text-generation
---

# LLMPopcorn Usage Instructions

Welcome to LLMPopcorn! This guide will help you generate video titles and prompts, as well as create AI-generated videos based on those prompts.

## Prerequisites

### Install Required Python Packages

Before running the scripts, ensure that you have installed the necessary Python packages. You can do this by executing the following command:

```bash
pip install torch transformers diffusers tqdm numpy pandas sentence-transformers faiss-cpu openai huggingface_hub safetensors
```

**Download the Dataset**:  
Download the Microlens dataset and place it in the `Microlens` folder for use with `PE.py`.

## Step 1: Generate Video Titles and Prompts

To generate video titles and prompts, run the `LLMPopcorn.py` script:
```bash
python LLMPopcorn.py
```

To enhance LLMPopcorn, execute the `PE.py` script:
```bash
python PE.py
```

## Step 2: Generate AI Videos

To create AI-generated videos, execute the `generating_images_videos_three.py` script:
```bash
python generating_images_videos_three.py
```

## Step 3: Clone the Evaluation Code

Then, following the instructions in the MMRA repository, you can evaluate the generated videos.

## Tutorial: Using the Prompts Dataset

You can easily download and use the structured prompts directly from Hugging Face:

### 1. Install `datasets`
```bash
pip install datasets
```

### 2. Load the Dataset in Python
```python
from datasets import load_dataset

# Load the LLMPopcorn prompts
dataset = load_dataset("junchenfu/llmpopcorn_prompts")

# Access the data (abstract or concrete)
for item in dataset["train"]:
    print(f"Type: {item['type']}, Prompt: {item['prompt']}")
```

This dataset contains both abstract and concrete prompts, which you can use as input for the video generation scripts in Step 2.