HD Video Generator 🚀 — AI Video Upscaler & Super-Resolution CLI

🌟 HD Video Generator is an open-source, lightweight, and high-performance command-line (CLI) tool to upscale Standard Definition (SD) videos to High Definition (HD/4K) using state-of-the-art Real-ESRGAN deep learning super-resolution models with ONNX Runtime and OpenCV.

---

📌 Table of Contents 📑

• 📖 Overview
• ✨ Key Features
• 🏷️ Keywords & Tags
• 📋 Prerequisites
• ⚙️ Setup & Installation
• 🧠 Model Download
• 📁 Project Structure
• 🚀 Usage & CLI Examples
• 🎬 Supported Formats & Capabilities
• ❓ Troubleshooting & FAQ
• 🤝 Contributing
• ⭐ Star History
• 📄 License

---

📖 Overview 🔍

Are your vintage home videos, old animations, or low-resolution clips blurry and pixelated? 📽️ HD Video Generator uses cutting-edge artificial intelligence and computer vision to restore, enhance, and upscale video frames to crystal-clear high definition. Powered by the optimized RealESR_Gx4_fp16 ONNX model, it delivers 4x spatial resolution improvements with sharp edges, rich colors, and reduced compression artifacts. 🎨✨

---

✨ Key Features 💡

• 🤖 AI-Powered 4x Video Upscaling: Harnesses the deep learning power of Real-ESRGAN (RealESR_Gx4_fp16) to reconstruct fine textures and realistic details.
• ⚡ High-Performance ONNX Inference: Utilizes onnxruntime for blazing-fast, cross-platform, hardware-accelerated model execution.
• 🎥 Robust Frame Processing: Leverages opencv-python for accurate frame-by-frame decoding, high-quality transformation, and seamless re-encoding.
• 💻 Intuitive CLI Interface: Simple, developer-friendly command-line options with smart default output handling.
• 🔒 100% Local & Privacy-Friendly: All video processing runs locally on your machine—no cloud uploads, subscriptions, or API rate limits! 🛡️
• 📦 Lightweight & Extensible: Minimal dependencies and modular code structure for easy customization and pipeline integration. 🧩

---

🏷️ Keywords / Tags 🔖

video-upscaling · real-esrgan · super-resolution · ai-video-enhancer · onnxruntime · opencv · python · sd-to-hd · video-enhancement · deep-learning · computer-vision · image-restoration · video-processing

---

📋 Prerequisites 🛠️

• 🐍 Python: Version 3.8 or higher
• 🐙 Git: For cloning the repository
• 💻 OS: Windows, macOS, or Linux

---

⚙️ Setup & Installation 🚀

1. 📥 Clone the Repository

git clone https://github.com/ishandutta2007/HDGenerator.git
cd HDGenerator

2. 🌐 Create and Activate a Virtual Environment

• Windows (PowerShell / Command Prompt): 🪟

  python -m venv venv
  .\venv\Scripts\activate

• macOS / Linux: 🍎🐧

  python3 -m venv venv
  source venv/bin/activate

3. 📦 Install Dependencies

pip install -r requirements.txt

---

🧠 Model Download 💾

This project requires the Real-ESRGAN ONNX model weights (RealESR_Gx4_fp16.onnx):

1. 📥 Download RealESR_Gx4_fp16.onnx directly from the ONNX Model Zoo on Hugging Face (ai-forever/Real-ESRGAN).
2. 📁 Create a models/ directory in the project root (if not already present):

   mkdir models

3. 💾 Place RealESR_Gx4_fp16.onnx inside the models/ folder.

---

📁 Project Structure 🗂️

HDGenerator/
├── assets/
│   ├── banner.svg              # Dynamic SVG header banner
│   └── social-preview.gif      # Social preview animated GIF (640x320)
├── models/
│   └── RealESR_Gx4_fp16.onnx   # ONNX super-resolution model weights
├── src/
│   ├── main.py                 # CLI entry point & argument parser
│   └── upscaler.py             # Video decoding, inference, & encoding pipeline
├── videos/
│   └── sample.mp4              # Sample input / demo video
├── .gitignore
├── README.md
└── requirements.txt

---

🚀 Usage & CLI Examples 🎬

Run the pipeline from the project root directory using Python:

🌟 Basic Upscaling

Upscale a video using default output naming (<original_name>_upscaled.mp4):

python src/main.py --input "videos/sample.mp4"

🎯 Custom Output Destination

Specify an exact path for the enhanced video output:

python src/main.py --input "videos/sample.mp4" --output "videos/sample_4k_enhanced.mp4"

🧠 Custom Model Path

Specify a different ONNX model variant if needed:

python src/main.py --input "videos/sample.mp4" --model_path "models/RealESR_Gx4_fp16.onnx"

🎛️ CLI Command Options

Argument	Type	Default	Description
--input	str	(Required)	Filepath to the input SD video to upscale.
--output	str	<input_dir>/<name>_upscaled.mp4	Destination filepath for the upscaled HD video.
--model_path	str	models/RealESR_Gx4_fp16.onnx	Filepath to the .onnx super-resolution model.

---

🎬 Supported Formats & Capabilities 🎞️

• 📹 Input Formats: MP4, AVI, MOV, MKV, and other standard formats supported by OpenCV.
• 📐 Upscaling Factor: 4x spatial scaling (e.g., 480p SD $\rightarrow$ 1080p/4K HD).
• 🚀 Execution Providers: CPU and GPU (CUDA/DirectML) inference via onnxruntime / onnxruntime-gpu.

---

❓ Troubleshooting & FAQ 💡

⚡ Q: How do I enable GPU acceleration with ONNX Runtime?

To use NVIDIA CUDA acceleration, install onnxruntime-gpu instead of standard onnxruntime:

pip uninstall onnxruntime
pip install onnxruntime-gpu

Ensure compatible CUDA and cuDNN libraries are configured on your system.

📦 Q: Where can I find more Real-ESRGAN ONNX weights?

Alternative checkpoints and specialized anime/photo enhancement models can be downloaded from Hugging Face or converted from PyTorch weights to ONNX format.

---

🤝 Contributing 🌟

Contributions, issues, and feature requests are welcome! 💖

1. 🍴 Fork the repository
2. 🌿 Create your feature branch (git checkout -b feature/AmazingFeature)
3. 💾 Commit your changes (git commit -m 'feat: Add AmazingFeature')
4. 📤 Push to the branch (git push origin feature/AmazingFeature)
5. 🚀 Open a Pull Request

---

⭐ Star History

---

📄 License 📜

Distributed under the MIT License. See LICENSE for more information. ⚖️