# shimanto008/midjourney-automation-tool architecture

> Architecture diagram of the GitHub repository shimanto008/midjourney-automation-tool, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-16). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/shimanto008/midjourney-automation-tool
- Repository: https://github.com/shimanto008/midjourney-automation-tool

## Overview

1. **Repository shape**
   This is a very small, single-purpose desktop automation repo. The visible runtime is essentially one Python application, `mid_journey_automation.py`, plus a packaged Windows executable, `mid_journey_automation.exe`. There are no separate services, libraries, or submodules in the tree.

2. **Deployment model**
   The app is designed to run locally on Windows as a standalone tool. The `.exe` suggests the Python program has been bundled for distribution so end users do not need Python installed. Architecturally, this is a monolithic client-side utility, not a networked system.

3. **Primary user workflow**
   The user supplies a DOCX file containing MidJourney prompts, configures automation settings in the app UI, starts the process, then switches focus to the MidJourney window. The tool then takes over input automation until the prompt set is exhausted or the user stops it.

4. **Input ingestion boundary**
   The main external input is the DOCX prompt document. The README also mentions automatic CSV export, so the document likely passes through a conversion/parsing step before being consumed by the automation pipeline. In diagram terms, the DOCX file is the inbound data source feeding the app.

5. **Prompt processing pipeline**
   Prompts are likely normalized into an internal queue or list, then grouped according to batch settings. The batch mode described in the README implies the app can send prompts in chunks with pauses between chunks, which is an important scheduling boundary in the architecture.

6. **Automation engine**
   The core behavior is keyboard/mouse automation against the MidJourney UI. The app does not talk to MidJourney through an API; it interacts with the active desktop window by typing prompts and clicking into the text input. This makes the MidJourney application itself the main external runtime dependency.

7. **Control and pacing subsystem**
   The tool exposes runtime controls such as typing speed, delay tuning, pause/resume, and batch size. These settings affect how the automation engine emits keystrokes and when it pauses. This subsystem is the primary difference between “prompt list processing” and “actual automation execution.”

8. **Safety and interruption path**
   The README describes an emergency stop by moving the mouse to the top-left corner, which strongly suggests a global fail-safe built into the automation layer. That means the app has an interrupt path that can preempt the typing/clicking loop outside the normal UI controls.

9. **Progress and state tracking**
   Progress tracking is called out as a feature, so the app probably maintains internal state about current prompt index, batch position, and running/paused status. That state is surfaced back to the UI so the user can see how far the automation has progressed.

10. **Important architectural boundary**
    The most important boundary is between the local automation app and the external MidJourney window. Everything inside the repo is about preparing prompts, pacing them, and controlling execution; the actual image-generation work happens entirely outside the repo in MidJourney.

11. **What to draw in an architecture diagram**
    A good diagram would show: user selects DOCX -> app parses/converts prompts -> internal queue/batching/controller -> keyboard/mouse automation -> MidJourney desktop window. Side paths should show settings input into the controller, progress state back to the UI, and a fail-safe/stop path interrupting the automation loop.

Main entry points: [`mid_journey_automation.py`](https://github.com/shimanto008/midjourney-automation-tool/blob/main/mid_journey_automation.py) (Automation App), [`mid_journey_automation.exe`](https://github.com/shimanto008/midjourney-automation-tool/blob/main/mid_journey_automation.exe) (Windows EXE)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_local_tool["Local Tool"]
  node_packaged_exe["Windows EXE<br/>distribution artifact"]
  node_python_app["Automation App<br/>python runtime"]
  node_ui_controls["Controls UI<br/>settings surface"]
  node_prompt_parser["Prompt Parser<br/>ingestion"]
  node_prompt_queue["Prompt Queue<br/>work list"]
  node_batch_scheduler{{"Batch Scheduler<br/>pacing"}}
  node_automation_engine["Automation Engine<br/>mouse-keyboard automation"]
  node_state_progress[("Progress State<br/>runtime state")]
  node_failsafe_stop(("Fail-safe Stop<br/>interrupt path"))
end

subgraph group_external["External"]
  node_user(("User<br/>operator"))
  node_docx_prompts["DOCX Prompts<br/>input document"]
  node_midjourney_window["MidJourney App<br/>desktop target"]
end

node_user -->|"configures"| node_ui_controls
node_user -->|"supplies"| node_docx_prompts
node_docx_prompts -->|"ingested by"| node_prompt_parser
node_python_app -->|"runs"| node_prompt_parser
node_prompt_parser -->|"normalizes"| node_prompt_queue
node_prompt_queue -->|"feeds"| node_batch_scheduler
node_ui_controls -->|"tunes"| node_batch_scheduler
node_batch_scheduler -->|"dispatches"| node_automation_engine
node_automation_engine -->|"controls"| node_midjourney_window
node_automation_engine -->|"updates"| node_state_progress
node_state_progress -->|"renders"| node_ui_controls
node_failsafe_stop -->|"interrupts"| node_automation_engine
node_user -->|"triggers"| node_failsafe_stop
node_packaged_exe -->|"bundles"| node_python_app

click node_packaged_exe "https://github.com/shimanto008/midjourney-automation-tool/blob/main/mid_journey_automation.exe"
click node_python_app "https://github.com/shimanto008/midjourney-automation-tool/blob/main/mid_journey_automation.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_packaged_exe,node_python_app,node_ui_controls,node_prompt_parser,node_prompt_queue,node_batch_scheduler,node_automation_engine,node_state_progress,node_failsafe_stop toneBlue
class node_user,node_docx_prompts,node_midjourney_window toneAmber
```

## Components

### Local Tool

Standalone Windows automation app and its internal control flow.

- [Windows EXE](https://github.com/shimanto008/midjourney-automation-tool/blob/main/mid_journey_automation.exe): distribution artifact · Bundled standalone executable for Windows distribution. · `mid_journey_automation.exe`
- [Automation App](https://github.com/shimanto008/midjourney-automation-tool/blob/main/mid_journey_automation.py): python runtime · Main application entrypoint that coordinates prompt processing and desktop automation. · `mid_journey_automation.py`
- **Controls UI**: settings surface · User-facing configuration for speed, delay, batch size, and run state.
- **Prompt Parser**: ingestion · Reads the DOCX input and converts prompts into internal records.
- **Prompt Queue**: work list · Normalized prompt list ready for ordered execution.
- **Batch Scheduler**: pacing · Groups prompts into batches and inserts pauses between chunks.
- **Automation Engine**: mouse-keyboard automation · Types prompts and clicks into the active MidJourney window.
- **Progress State**: runtime state · Tracks current prompt, batch position, and running or paused status.
- **Fail-safe Stop**: interrupt path · Emergency interruption that can preempt the automation loop.

### External

Inputs and runtime dependencies outside the repository.

- **User**: operator · Starts the workflow, configures automation, and can pause or stop execution.
- **DOCX Prompts**: input document · Source document containing MidJourney prompts.
- **MidJourney App**: desktop target · External MidJourney window that receives automated typing and clicks.

## Connections

- User → Controls UI: configures · User sets speed, delay, batching, and run controls.
- User → DOCX Prompts: supplies · User provides the prompt document.
- DOCX Prompts → Prompt Parser: ingested by · DOCX content is parsed into prompts.
- Automation App → Prompt Parser: runs · Application coordinates document ingestion.
- Prompt Parser → Prompt Queue: normalizes · Parsed prompts are assembled into an internal queue.
- Prompt Queue → Batch Scheduler: feeds · Queue entries are scheduled in batches.
- Controls UI → Batch Scheduler: tunes · User settings affect pacing and batch size.
- Batch Scheduler → Automation Engine: dispatches · Batched prompts are handed to the automation layer.
- Automation Engine → MidJourney App: controls · Keystrokes and clicks are sent to the active desktop app.
- Automation Engine → Progress State: updates · Execution state advances as prompts are sent.
- Progress State → Controls UI: renders · Progress and running state are shown back in the UI.
- Fail-safe Stop → Automation Engine: interrupts · Emergency stop can abort the automation loop.
- User → Fail-safe Stop: triggers · User can stop or pause the run, including emergency interruption.
- Windows EXE → Automation App: bundles · Standalone executable distributes the Python application.