# prompt-builder (`lupara90/prompt-builder`) Actor Transform brief, simple ideas into highly detailed, optimized image generation prompts tailored perfectly for Midjourney, Stable Diffusion, DALL-E 3, Nano Banana, and text-to-image assistant models simultaneously. - **URL**: https://apify.com/lupara90/prompt-builder.md - **Developed by:** [Ayushman Sen](https://apify.com/lupara90) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Multi-Engine Prompt Enhancer Transform brief, simple ideas into highly detailed, optimized image generation prompts tailored perfectly for **Midjourney**, **Stable Diffusion**, **DALL-E 3**, **Nano Banana**, and text-to-image assistant models simultaneously. Whether you are looking for an automated structural prompt template fallback or advanced LLM-powered context expansion via **OpenRouter**, this Actor handles the precise syntactic weightings, structural demands, and formatting flags that each image engine requires to deliver elite aesthetic results. --- ### 🚀 Features * 🧠 **Smart Context Expansion:** Translates a simple subject, style, and mood into an immersive visual scene description. * 🤖 **OpenRouter LLM Integration:** Connects seamlessly with cloud-hosted language models (such as Llama 3) to inject rich, intelligent prose modifiers. * ⚙️ **Multi-Engine Customization:** Instead of a generic prompt output, it structures the results into a precise array of specialized prompts: * **Midjourney:** Appends modern technical aspect ratio configurations (`--ar 16:9`) and execution parameters (`--v 6.0 --style raw`). * **Stable Diffusion (SDXL):** Injects highly weighted, comma-separated quality tags (`masterpiece`, `trending on artstation`, `vray render`) to maximize fidelity. * **DALL-E 3:** Structures prompts into natural, organic conversational prose to bypass internal system revisions and match its semantic parser. * **Nano Banana:** Formats with fast-inference micro-rendering token prefixes. * **GPT-2 Assistants:** Provides instructional prompt frames designed for text transformers. --- ### 📥 Input Parameters The Actor accepts the following properties via its user interface: | Field | Type | Required | Description | | :--- | :--- | :--- | :--- | | **Main subject** (`subject`) | `String` | **Yes** | The core entity or action you want to draw (e.g., `"An armadillo wearing a trench coat"`). | | **Art style** (`style`) | `String` | No | Predefined presets like `watercolor`, `cinematic`, `pixel art`, or your own custom artistic style. | | **Mood / atmosphere** (`mood`) | `String` | No | Atmospheric parameters like `mysterious`, `joyful`, `dark`, or custom lighting cues. | | **Enhance with LLM** (`useLLM`) | `Boolean` | No | When enabled, routes the base scene description through an LLM via OpenRouter for advanced creative prompt engineering. | --- ### 📤 Output Format The Actor returns a clean, structured JSON array containing objects mapped precisely to your target generators. This makes it incredibly easy to copy individual variants or stream the array directly into downstream automated imaging workflows. #### Output JSON Example ```json [ { "generator": "Midjourney", "optimizedPrompt": "A dramatic cinematic film still of a cyberpunk cat, shot on a 35mm anamorphic lens. Masterful chiaroscuro lighting, sharp focus on details... --ar 16:9 --v 6.0 --style raw", "notes": "Appended aspect ratio parameter and version configurations for Midjourney execution v6." }, { "generator": "Stable Diffusion", "optimizedPrompt": "A dramatic cinematic film still of a cyberpunk cat... masterpiece, highly detailed, sharp focus, 8k resolution, trending on artstation", "notes": "Appended explicit comma-separated quality tags to trigger optimal high-fidelity weights in SDXL checkpoint pathways." } ] #### Setup Requirement To use the LLM enhancement capability, you must provide your own API credentials: Go to your Actor's Settings tab in the Apify Console. Scroll down to Environment variables. Create a variable with the key OPENROUTER_API_KEY and paste your secret token as the value. # Actor input Schema ## `subject` (type: `string`): What should be in the image (e.g., 'a cyberpunk cat') ## `style` (type: `string`): e.g., 'watercolor', 'cinematic', 'pixel art' ## `mood` (type: `string`): e.g., 'mysterious', 'joyful', 'dark' ## `useLLM` (type: `boolean`): If true, calls an external LLM for richer phrasing ## Actor input object example ```json { "subject": "A cyberpunk cat", "style": "", "mood": "", "useLLM": false } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "subject": "A cyberpunk cat" }; // Run the Actor and wait for it to finish const run = await client.actor("lupara90/prompt-builder").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "subject": "A cyberpunk cat" } # Run the Actor and wait for it to finish run = client.actor("lupara90/prompt-builder").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "subject": "A cyberpunk cat" }' | apify call lupara90/prompt-builder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lupara90/prompt-builder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "prompt-builder", "description": "Transform brief, simple ideas into highly detailed, optimized image generation prompts tailored perfectly for Midjourney, Stable Diffusion, DALL-E 3, Nano Banana, and text-to-image assistant models simultaneously.", "version": "0.0", "x-build-id": "8nWXaFCLyxfbplldD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lupara90~prompt-builder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lupara90-prompt-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lupara90~prompt-builder/runs": { "post": { "operationId": "runs-sync-lupara90-prompt-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lupara90~prompt-builder/run-sync": { "post": { "operationId": "run-sync-lupara90-prompt-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "subject": { "title": "Main subject", "type": "string", "description": "What should be in the image (e.g., 'a cyberpunk cat')" }, "style": { "title": "Art style (optional)", "type": "string", "description": "e.g., 'watercolor', 'cinematic', 'pixel art'", "default": "" }, "mood": { "title": "Mood / atmosphere (optional)", "type": "string", "description": "e.g., 'mysterious', 'joyful', 'dark'", "default": "" }, "useLLM": { "title": "Enhance with LLM (optional)", "type": "boolean", "description": "If true, calls an external LLM for richer phrasing", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```