# AI Image Upscaler - 2x & 4x Resolution Enhancement (`ntriqpro/image-upscale-mcp`) Actor Make blurry photos crystal clear. Upscale images to 2x or 4x resolution without losing quality. - **URL**: https://apify.com/ntriqpro/image-upscale-mcp.md - **Developed by:** [daehwan kim](https://apify.com/ntriqpro) (community) - **Categories:** MCP servers, AI, Developer tools - **Stats:** 7 total users, 3 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## Image Upscale MCP - AI Super Resolution 2x/4x AI-powered image upscaling with optional face enhancement. Uses Real-ESRGAN (Real Enhanced Super-Resolution GAN) for high-quality 2x or 4x super resolution. ### What It Does This MCP (Model Context Protocol) actor provides two tools for intelligent image upscaling: #### Tool 1: `upscale_image` Upscale any image by 2x or 4x without additional processing. - **Input**: Image URL + scale factor (2 or 4) - **Output**: Base64 upscaled image + metadata (original/upscaled dimensions, processing time) - **Pricing**: $0.08 per image #### Tool 2: `enhance_face` Upscale images with face enhancement (better quality for portraits, profile pictures). - **Input**: Image URL + scale factor (2 or 4) - **Output**: Base64 upscaled image + metadata - **Pricing**: $0.10 per image ### How to Use #### Basic Upscaling (2x) ```json { "image_url": "https://example.com/photo.jpg", "scale": 2 } ```` #### 4x Super Resolution (Default) ```json { "image_url": "https://example.com/photo.jpg" } ``` #### Face Enhancement for Portrait ```json { "tool_name": "enhance_face", "image_url": "https://example.com/portrait.jpg", "scale": 4 } ``` ### API Response Example ```json { "image_base64": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==", "original_size": { "width": 512, "height": 512 }, "upscaled_size": { "width": 2048, "height": 2048 }, "scale": 4, "model": "realesrgan-x4plus", "processing_time": 3500 } ``` ### Technical Details - **AI Model**: Real-ESRGAN (BSD-3-Clause License) - **Backend Server**: ai.ntriq.co.kr - **Supported Scales**: 2x, 4x - **Input Formats**: URL-based (image must be publicly accessible) - **Output Format**: Base64-encoded PNG/JPG - **Processing Time**: 2-6 seconds per image (depending on size and scale) - **Timeout**: 195 seconds (180s processing + 15s buffer) ### Pricing | Tool | Price | Use Case | |------|-------|----------| | `upscale_image` | $0.08 | General upscaling | | `enhance_face` | $0.10 | Portraits & faces | Charged per execution. Free tier: Not available (paid actor). ### Error Handling - **Missing Image URL**: Returns error 400 - **Invalid Scale**: Only 2 and 4 accepted - **Server Timeout**: Returns error after 195 seconds - **Network Error**: Returns detailed error message ### Attribution Powered by **Real-ESRGAN** (BSD-3-Clause License) - GitHub: https://github.com/xinntao/Real-ESRGAN - Paper: Real-ESRGAN: Practical Blind Real-World Super-Resolution with Generative Adversarial Networks ### Requirements - Public image URL (no authentication required) - Image size: Recommended max 4MB - Scale factor: 2 or 4 ### Status Production-ready. Pay-per-event charged by Apify. ### Support For issues or feature requests, contact the development team. *** ### 🔗 Related Actors by ntriqpro Extend this actor with the ntriqpro intelligence network: - [**supply-chain-risk-mcp**](https://apify.com/ntriqpro/supply-chain-risk-mcp) — MCP server for supply chain risk - [**video-intelligence-mcp**](https://apify.com/ntriqpro/video-intelligence-mcp) — MCP server for video intelligence - [**content-factory-mcp**](https://apify.com/ntriqpro/content-factory-mcp) — MCP server for content-factory ### ⭐ Love it? Leave a Review Your rating helps professionals discover this actor. [Rate it here](https://apify.com/ntriqpro/image-upscale-mcp/reviews). # Actor input Schema ## `standbyTimeout` (type: `integer`): How long (in seconds) the MCP server stays active waiting for tool calls before shutting down. Default is 300 seconds (5 minutes). Increase for long-running sessions. ## `logLevel` (type: `string`): Controls how much detail appears in the Actor log. Use 'info' for normal operation, 'debug' for troubleshooting, or 'error' for minimal output. ## Actor input object example ```json { "standbyTimeout": 300, "logLevel": "info" } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "standbyTimeout": 300, "logLevel": "info" }; // Run the Actor and wait for it to finish const run = await client.actor("ntriqpro/image-upscale-mcp").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 = { "standbyTimeout": 300, "logLevel": "info", } # Run the Actor and wait for it to finish run = client.actor("ntriqpro/image-upscale-mcp").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 '{ "standbyTimeout": 300, "logLevel": "info" }' | apify call ntriqpro/image-upscale-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ntriqpro/image-upscale-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Image Upscaler - 2x & 4x Resolution Enhancement", "description": "Make blurry photos crystal clear. Upscale images to 2x or 4x resolution without losing quality.", "version": "1.0", "x-build-id": "yyEvWVspAp75jedZA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ntriqpro~image-upscale-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ntriqpro-image-upscale-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ntriqpro~image-upscale-mcp/runs": { "post": { "operationId": "runs-sync-ntriqpro-image-upscale-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ntriqpro~image-upscale-mcp/run-sync": { "post": { "operationId": "run-sync-ntriqpro-image-upscale-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "standbyTimeout": { "title": "Standby Timeout (seconds)", "minimum": 60, "maximum": 3600, "type": "integer", "description": "How long (in seconds) the MCP server stays active waiting for tool calls before shutting down. Default is 300 seconds (5 minutes). Increase for long-running sessions." }, "logLevel": { "title": "Log Level", "enum": [ "info", "debug", "error" ], "type": "string", "description": "Controls how much detail appears in the Actor log. Use 'info' for normal operation, 'debug' for troubleshooting, or 'error' for minimal output." } } }, "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 } } } } } } } } } } ```