AI-Powered Image Generation & Publication System (Imagetron)
The system is the AI-powered image generation and management application. It is sophisticated enough to give the understanding about my capabilities. Yet it is simple enough to overview in one go.
This is private application I built on the request of one of my business partners. There is no NDA limitation so I can tell about it in a rather detailed way.
The system streamlines the creation, management, and publication of AI-generated images for artists and content creators. By automating the entire workflow—from prompt generation, prompt and image management, to image upload—Imagetron saves time, enhances productivity and diminishes cognitive load of routine operations by approximately x3-5 times.
Key Features:
- Prompt Generation: Leveraging ChatGPT, Imagetron helps an artist crafting high-quality prompts to fuel AI image creation.
- Seamless Image Generation: Integrates with Midjourney via a 3rd party REST API to generate images with zero manual action.
- Storage & Automated Upload: Temporarily stores images in S3 and automates uploading to platforms like Adobe Stock via dedicated browser extension.
- Comfortable User Interface: A Vue-based frontend lets users manage prompts and images comfortably, while backend automation handles the heavy lifting. It co-operates with the dedicated browser extension to automate image upload submissions.
Overview
The scheme presents the engineering process (gray, left side) and the final application (blue, right side) as a whole, providing insight into how complex even moderate engineering endeavors can be, and how I handle the complexity in concrete products.
The engineering process is presented on the left side, is professionally structured and emphasizing approaches like decoupling, "right from the first time", zero defects, zero waste and long-term evolvability.
Unlike the wide-spread view of engineers as "coders" or "programmers," real-world applications demand far more—including requirements elicitation and specification, architecting, code modeling, infrastructure planning, and expectations around long-term product evolution.
The final system, shown on the right, implements a robust, modular composition of prompt and image services, persistence layers, a monitoring stack, and UI access points. It manages the entire lifecycle of AI-generated images—from prompt to delivery—while enabling developers to maintain quality, visibility, and operational control.
[HINT] Add the SVG Navigator extension to your Chrome to comfortably view the diagrams with zoom and pan in the separate tab.
Midjourney Image Generation Domain Overview
The diagram presents the Midjourney commands hierarchy to give you a quick grasp of the problem domain context being automated.
The diagram is taken out as-is from the actual design documents, links in the legend are just kept for brevity, not usable because leading to private repository.
Sea-Level Use Cases by DDD Subdomain
Prompts
| Use Case | Actor |
|---|---|
| Obtain Image Prompt Subjects | Artist, Backend, ChatGPT |
| Obtain Image Prompts | Artist, Backend, ChatGPT |
| [CRUD] Retrieve prompts Subjects Chat history | Artist, Backend |
| [CRUD] Remove prompts Subjects Chat history | Artist, Backend |
| [CRUD] Retrieve prompts list | Artist, Backend |
| [CRUD] Remove the prompt | Artist, Backend |
| [CRUD] Retrieve specific prompt | Artist |
| Obtain different Prompt Title | Artist, Backend, ChatGPT |
| Manage Image Prompt Manual Statuses | Artist |
Images Subdomain
| Use Case | Actor |
|---|---|
| Generate initial image versions (Imagine) | Artist, Backend, Midjourney API Intermediary |
| Regenerate image versions (Reroll) | Artist, Backend, Midjourney API Intermediary |
| Create image variations | Artist, Backend, Midjourney API Intermediary |
| Upscale selected image version | Artist, Backend, Midjourney API Intermediary |
| Handle Midjourney commands results: download generated images or process errors (webhook) | Backend (with SSE), Midjourney API Intermediary |
| Retrieve images list | Artist, Backend |
The Application Architecture
The following C4 container diagram provides a high-level architectural blueprint, focusing on key system components and their interactions to guide both technical and non-technical stakeholders during design, implementation and evolvement phases.
During the design phase, it allows engineering team to visualize component relationships before implementation, aligns non-technical stakeholders on system scope and external integrations, identify potential bottlenecks (e.g., reliance on external APIs like Midjourney/Adobe Stock).
In implementation phase this diagram serves for engineers as a foundational reference for structuring code repositories, separating modules, and defining critical interface contracts.
The diagram acts as a living artifact that bridges conceptual design with practical implementation, ensuring consistency as the system evolves.
Models
Here are a the examples of a multiple engineering models I created to decompose domain and system complexity, enabling zero-defect coding and further system evolution through professional modularization and decoupling.