souga-ai-video 0.1.0

嗖嘎AI视频生成器：PyQt6 桌面端，支持批量图片/视频生成

AI Tools - Image & Video Generation

A comprehensive desktop application for AI-powered image and video generation with batch processing capabilities. Built with PyQt6, Peewee ORM, and managed with uv.

Features

• Image Generation: Generate images using Google Nano Banana API
• Video Generation: Create videos using OpenAI Sora2 API with text and optional image inputs
• Batch Processing: Process multiple prompts simultaneously
• Task Management: Track progress and manage generation tasks
• Database Storage: SQLite database for persistent task history
• User-Friendly Interface: Modern PyQt6-based GUI

Requirements

• Python 3.8+
• uv (Python package manager)
• API Keys for Google Nano Banana and OpenAI Sora2

Installation

1. Install uv (if not already installed)

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone and Setup Project

git clone <repository-url>
cd ai-tools

3. Install Dependencies

uv pip install -e .

4. Configure API Keys

Copy the example environment file and add your API keys:

cp .env.example .env

Edit .env file with your API keys:

# Google API Key for Nano Banana image generation
GOOGLE_API_KEY=your_google_api_key_here

# OpenAI API Key for Sora2 video generation
OPENAI_API_KEY=your_openai_api_key_here

# Optional: API Endpoints (defaults will be used if not set)
GOOGLE_NANO_BANANA_ENDPOINT=https://api.google.com/nano-banana/v1/generate
OPENAI_SORA2_ENDPOINT=https://api.openai.com/v1/video/generations

# Batch processing settings
MAX_BATCH_SIZE=10
MAX_CONCURRENT_TASKS=3

Usage

Run the Application

uv run ai-tools

Or directly:

python -m ai_tools.main

Image Generation

1. Navigate to the "Image Generation" tab
2. Enter your prompt in the text area
3. For batch processing:
   • Check "Enable Batch Processing"
   • Enter multiple prompts (one per line)
   • Or load prompts from a text file
4. Click "Generate Image(s)"
5. Monitor progress and view results

Video Generation

1. Navigate to the "Video Generation" tab
2. Enter your text prompt
3. Optionally select an input image for image-to-video generation
4. Configure video settings (duration, FPS, resolution)
5. For batch processing:
   • Check "Enable Batch Processing"
   • Enter multiple prompts
6. Click "Generate Video(s)"

Batch Management

1. Navigate to the "Batch Manager" tab
2. View all batch jobs and their status
3. Cancel or delete batches as needed
4. View individual tasks within each batch

Task History

1. Navigate to the "Task History" tab
2. Filter tasks by type, status, or date range
3. View output files
4. Retry failed or cancelled tasks
5. Delete unwanted tasks

Project Structure

ai-tools/
├── src/
│   └── ai_tools/
│       ├── __init__.py
│       ├── main.py              # Application entry point
│       ├── config.py            # Configuration management
│       ├── database.py          # Database models and operations
│       ├── ui/                  # User interface components
│       │   ├── main_window.py   # Main application window
│       │   ├── image_generation_tab.py
│       │   ├── video_generation_tab.py
│       │   ├── batch_manager_tab.py
│       │   ├── task_history_tab.py
│       │   └── workers.py       # Background worker threads
│       └── generators/          # AI generation modules
│           ├── image_generator.py
│           └── video_generator.py
├── pyproject.toml               # Project configuration
├── .env.example                 # Environment variables template
└── README.md

API Integration

Google Nano Banana API

Used for image generation. Requires a valid Google API key.

Features:

• Text-to-image generation
• Configurable image dimensions
• Batch processing support

OpenAI Sora2 API

Used for video generation. Requires a valid OpenAI API key.

Features:

• Text-to-video generation
• Image-to-video generation (with optional input image)
• Configurable duration, FPS, and resolution
• Batch processing support

Database Schema

The application uses SQLite with the following main tables:

• tasks: Individual generation tasks
• batches: Groups of related tasks
• batch_tasks: Junction table for batch-task relationships

Configuration

Configuration is managed through environment variables and the config.py module:

• API keys and endpoints
• Batch processing limits
• Output directory settings
• UI preferences

Error Handling

The application includes comprehensive error handling:

• API connection failures
• Invalid API keys
• File system errors
• Database operations
• Network timeouts

Troubleshooting

Common Issues

1. Missing API Keys: Ensure your .env file contains valid API keys
2. Database Errors: Check write permissions in the output directory
3. Network Issues: Verify internet connectivity and API endpoint availability
4. Memory Issues: Reduce batch size for large processing jobs

Logs and Debugging

The application logs detailed information about:

• API requests and responses
• Database operations
• File system operations
• Error conditions

Check the console output for detailed error messages.

Development

Adding New Features

1. Update the database schema if needed
2. Add new UI components in the ui/ directory
3. Implement generation logic in the generators/ directory
4. Update configuration in config.py
5. Add appropriate error handling

Testing

# Run the application
uv run ai-tools

# Test image generation
# Test video generation
# Test batch processing
# Verify database operations

License

MIT License - see LICENSE file for details.

Support

For issues and feature requests, please use the GitHub issue tracker.