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readme.md

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+---
+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.