https://github.com/tsjdev-apps/ai-image-generation-console
Interactive console app for AI image generation with Azure OpenAI & OpenAI. Supports DALL-E 3, gpt-image-1, and gpt-image-1-mini. Built with .NET 10 & Spectre.Console.
https://github.com/tsjdev-apps/ai-image-generation-console
Last synced: 5 months ago
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Interactive console app for AI image generation with Azure OpenAI & OpenAI. Supports DALL-E 3, gpt-image-1, and gpt-image-1-mini. Built with .NET 10 & Spectre.Console.
- Host: GitHub
- URL: https://github.com/tsjdev-apps/ai-image-generation-console
- Owner: tsjdev-apps
- License: mit
- Created: 2025-12-14T17:55:07.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-12-17T17:24:09.000Z (6 months ago)
- Last Synced: 2025-12-20T20:49:06.982Z (6 months ago)
- Language: C#
- Size: 6.38 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AI Image Generation Console
A powerful and user-friendly console application for generating images using AI models from **Azure OpenAI** and **OpenAI**. Built with .NET 10 and featuring an elegant terminal UI powered by Spectre.Console.
## ✨ Features
- 🌐 **Multi-Provider Support**: Seamlessly switch between Azure OpenAI and OpenAI
- 🧠 **Multiple Model Support**: Generate images using DALL-E 3, GPT-Image-1, GPT-Image-1-Mini, and GPT-Image-1.5
- ⚙️ **Customizable Settings**: Configure image size, quality, and style per model
- 💾 **Automatic Saving**: Images are automatically saved to temporary files with timestamps
- ⏱️ **Performance Tracking**: Real-time generation time tracking for each model
- 🎨 **Beautiful UI**: Rich console interface with colors, animations, and clickable file links
- 🔒 **Secure Input**: Masked API key input for security
- 🛡️ **Robust Error Handling**: Graceful handling of API errors with detailed feedback
- 📊 **Generation Summary**: Track success and failure counts across multiple models
## 🖼️ Screenshots
### Main Menu
### Azure OpenAI Configuration
### Model Selection (OpenAI)
### Image Generation Progress
### Generation Results
**DALL-E 3**
**GPT-Image-1.5**
## 🚀 Getting Started
### Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0) or higher
- An API key from either:
- [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/ai-foundry/models/openai/)
- [OpenAI Platform](https://platform.openai.com/)
### Installation
1. **Clone the repository**
```bash
git clone https://github.com/tsjdev-apps/ai-image-generation-console.git
cd ImageGenerationConsole
```
1. **Build the project**
```bash
dotnet build
```
1. **Run the application**
```bash
dotnet run
```
## 🧭 Usage
### Azure OpenAI
1. Select **Azure OpenAI** as your provider
2. Enter your Azure OpenAI endpoint (e.g., `https://your-resource.openai.azure.com/`)
3. Enter your Azure OpenAI API key (input will be masked)
4. Specify your deployments in the format: `deploymentName:modelType`
- Example: `myDallE:dall-e-3,myGptImage:gpt-image-1`
- Supported model types:
- `dall-e-3`
- `gpt-image-1`
- `gpt-image-1-mini`
- `gpt-image-1.5`
5. Enter your image generation prompt
6. Wait for the images to be generated and saved
### OpenAI
1. Select **OpenAI** as your provider
2. Enter your OpenAI API key (input will be masked)
3. Select one or more models using the multi-select menu:
- Use `Space` to toggle selection
- Use `Enter` to confirm
4. Enter your image generation prompt
5. Wait for the images to be generated and saved
### Output
Generated images are saved to your system's temporary directory with the following naming convention:
```plaintext
{modelName}_{timestamp}.png
```
Example: `dall-e-3_20240115_143052.png`
The console will display clickable file paths for easy access to the generated images.
## 🧩 Model Configuration
### DALL-E 3
- **Size**: 1792x1024
- **Quality**: Standard
- **Style**: Vivid
- **Format**: URL
### GPT-Image-1
- **Size**: 1536x1024
- **Quality**: High (Azure) / Default (OpenAI)
- **Format**: Bytes (Azure) / Default (OpenAI)
### GPT-Image-1-Mini
- **Size**: 1536x1024
- **Quality**: Medium
- **Format**: Bytes (Azure) / Default (OpenAI)
### GPT-Image-1.5
- **Size**: 1536x1024
- **Quality**: Default
- **Format**: Bytes (Azure) / Default (OpenAI)
## 🏗️ Architecture
The application is structured into three main components:
```plaintext
ImageGenerationConsole/
📄 Program.cs # Main application logic and orchestration
📂 Helpers/
📄 ConsoleHelper.cs # UI utilities using Spectre.Console
📂 Utils/
📄 Statics.cs # Centralized string constants and messages
```
### Key Design Principles
- 🎯 **Separation of Concerns**: UI, business logic, and constants are well separated
- 🛡️ **Error Resilience**: Comprehensive error handling at every level
- 😊 **User Experience**: Clear prompts, validation, and feedback
- 🧰 **Maintainability**: Centralized strings for easy localization
- 🚀 **Extensibility**: Easy to add new models or providers
## 🧰 Dependencies
- **[Azure.AI.OpenAI](https://www.nuget.org/packages/Azure.AI.OpenAI)**: Azure OpenAI SDK
- **[OpenAI](https://www.nuget.org/packages/OpenAI)**: OpenAI SDK
- **[Spectre.Console](https://www.nuget.org/packages/Spectre.Console)**: Rich console UI framework
## 🩹 Error Handling
The application handles various error scenarios gracefully:
- 🚫 **401 Unauthorized**: Invalid API key
- ⏳ **429 Too Many Requests**: Rate limit exceeded
- 🔎 **404 Not Found**: Model or deployment not found
- 🌐 **Network Errors**: Connection issues during image download
- 🗂️ **File System Errors**: Permission or I/O issues when saving files
## ✍️ Blog Post
If you are more interested into details, please see the following posts on [medium.com](https://medium.com/@tsjdevapps) or in my [personal blog](https://www.tsjdev-apps.de):
- [Building a Multi-Model Image Generation Console App with Azure OpenAI and OpenAI](https://medium.com/medialesson/building-a-multi-model-image-generation-console-app-with-azure-openai-and-openai-fa5b9b790e7e)
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
1. Fork the project
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.