Metadata-Version: 2.4
Name: special-attentions-pack
Version: 0.3.0
Summary: A collection of sparse attention mechanisms
Home-page: https://github.com/yourname/special_attentions
Author: Your Name
Author-email: you@example.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
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# SparseAttnFunctions  
**Efficient Implementations of Sparse Attention Mechanisms**  

This repository contains multiple optimized implementations of sparse attention techniques, specifically tailored for **CogVideo2B**. These modifications enhance computational efficiency and memory usage, making them ideal for large-scale video generation tasks.

### Key Features:  
• **Sparse Attention Variants**: Includes several approaches to sparse attention, such as windowed attention, block-sparse attention, and more.  
• **CogVideo2B Integration**: Customized to seamlessly integrate with the CogVideo2B framework, ensuring optimal performance.  
• **Efficiency**: Designed to reduce memory footprint and accelerate computation, especially for high-resolution video generation.  

### Use Cases:  
• Large-scale video generation tasks.  
• Applications requiring efficient attention mechanisms for long sequences.  

### Installation
• pip install .

### Contributing:  
Contributions are welcome! Please follow the [Contribution Guidelines](#) for more details.  

