# AI Image Editor — Transform Photos with Text Prompts (`akash9078/ai-image-editor`) Actor AI-powered image-to-image editing using text prompts. Transform photos, product images, and artwork with natural language instructions. - **URL**: https://apify.com/akash9078/ai-image-editor.md - **Developed by:** [Akash Kumar Naik](https://apify.com/akash9078) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## AI Image Editor AI-powered image editing using text prompts. Transform photos, product images, and artwork with natural language instructions. ### What it does This actor edits images based on a text prompt using the RunPod `qwen-image-edit-2511-lora` endpoint. Upload an image, describe the changes you want, and receive an edited version. - 🖼️ Edit any image via URL using AI-powered image-to-image transformation - 📝 Natural language prompts — describe what you want changed - ⚡ Synchronous execution — waits for the result and returns it - 🎨 Supports JPEG, PNG, and WebP output formats - 🔧 Optional LoRA adapters and advanced parameters ### Input ```json { "imageUrl": "https://example.com/photo.jpg", "prompt": "A futuristic city with glowing neon lights", "outputFormat": "jpeg", "aspectRatio": "1024*1024", "seed": -1 } ```` #### Input fields | Field | Type | Required | Description | |-------|------|----------|-------------| | `imageUrl` | string | ✅ | URL of the image to edit | | `prompt` | string | ✅ | Text description of the desired edit | | `outputFormat` | string | ❌ | Output format: `jpeg`, `png`, `webp` (default: `jpeg`) | | `aspectRatio` | string | ❌ | Output dimensions: `1024*1024`, `1024*1280`, `1280*1024`, `1280*1280`, `1280*1536`, `1536*1080` (default: `1024*1024`) | | `seed` | integer | ❌ | Random seed for reproducibility. `-1` = random (default: `-1`) | | `numInferenceSteps` | integer | ❌ | Number of denoising steps (1–100) | | `guidance` | number | ❌ | Guidance scale for prompt adherence (1–20) | | `enableBase64Output` | boolean | ❌ | Return image as base64 in response (default: `false`) | ### Output The actor produces two outputs: #### 📊 Dataset — Run Results Metadata about the editing job, including a public URL to the edited image: ```json { "status": "COMPLETED", "prompt": "A futuristic city with glowing neon lights", "originalImageUrl": "https://example.com/photo.jpg", "outputFormat": "jpeg", "imageSize": 245760, "imageExtension": "jpg", "contentType": "image/jpeg", "publicImageUrl": "https://api.apify.com/v2/key-value-stores/xxx/records/edited-image", "delayTime": 1500, "executionTime": 8200, "runpodJobId": "abc-123" } ``` #### 🖼️ Key-Value Store — Edited Image The generated/edited image is saved as `edited-image` in the default key-value store. It is accessible via a public URL that is returned in the dataset results. ### Use Cases - 👗 Fashion & e-commerce — change backgrounds, lighting, or styles on product photos - 🏠 Real estate — enhance property images, change time of day, add furnishings - 🎨 Creative workflows — iterate on concept art and illustrations - 📸 Photography — apply stylistic edits, lighting corrections, or scene modifications - 🛍️ Marketing — quickly generate variations of campaign imagery ### Tips & Best Practices - **Be specific in prompts** — detailed descriptions yield better edits - **Use seeds** — set a fixed seed to reproduce the same edit style across multiple images - **Start with defaults** — the default `1024*1024` size and format work well for most cases - **Check RunPod dashboard** — if jobs fail, verify your API key and endpoint availability - **Timeout** — the actor waits up to 5 minutes for RunPod to complete the job # Actor input Schema ## `imageUrl` (type: `string`): URL of the image to edit. Supports direct image URLs and public Google Drive links. ## `prompt` (type: `string`): Text prompt describing the desired image edit ## `outputFormat` (type: `string`): Format of the output image ## `aspectRatio` (type: `string`): Dimensions of the output image ## `seed` (type: `integer`): Random seed for reproducibility. Use -1 for random. ## `numInferenceSteps` (type: `integer`): Number of denoising steps. Higher = better quality but slower. ## `guidance` (type: `number`): How closely to follow the prompt. Higher = stricter adherence. ## `enableBase64Output` (type: `boolean`): Return image as base64 string in the response ## Actor input object example ```json { "imageUrl": "https://drive.google.com/file/d/1S5sCnyVmnRHxk233EfZ6byjClr8pxaUU/view?usp=drive_link", "prompt": "Professional product photography of a young white female model wearing/holding the provided [PRODUCT], 9:16 vertical Pinterest aspect ratio, front-facing portrait with direct confident eye contact, natural soft studio lighting with large softbox and fill reflector, high-resolution 8K commercial quality, minimalist aesthetic with product clearly showcased as hero element, choose from backgrounds: prefer natural landscape.", "outputFormat": "jpeg", "aspectRatio": "1024*1024", "seed": -1, "numInferenceSteps": 28, "guidance": 7.5, "enableBase64Output": false } ``` # Actor output Schema ## `dataset` (type: `string`): Metadata including the public image URL ## `outputUrl` (type: `string`): Public URL to download the edited image directly # 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 = { "imageUrl": "https://drive.google.com/file/d/1S5sCnyVmnRHxk233EfZ6byjClr8pxaUU/view?usp=drive_link", "prompt": "Professional product photography of a young white female model wearing/holding the provided [PRODUCT], 9:16 vertical Pinterest aspect ratio, front-facing portrait with direct confident eye contact, natural soft studio lighting with large softbox and fill reflector, high-resolution 8K commercial quality, minimalist aesthetic with product clearly showcased as hero element, choose from backgrounds: prefer natural landscape.", "outputFormat": "jpeg", "aspectRatio": "1024*1024", "seed": -1, "numInferenceSteps": 28, "guidance": 7.5, "enableBase64Output": false }; // Run the Actor and wait for it to finish const run = await client.actor("akash9078/ai-image-editor").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 = { "imageUrl": "https://drive.google.com/file/d/1S5sCnyVmnRHxk233EfZ6byjClr8pxaUU/view?usp=drive_link", "prompt": "Professional product photography of a young white female model wearing/holding the provided [PRODUCT], 9:16 vertical Pinterest aspect ratio, front-facing portrait with direct confident eye contact, natural soft studio lighting with large softbox and fill reflector, high-resolution 8K commercial quality, minimalist aesthetic with product clearly showcased as hero element, choose from backgrounds: prefer natural landscape.", "outputFormat": "jpeg", "aspectRatio": "1024*1024", "seed": -1, "numInferenceSteps": 28, "guidance": 7.5, "enableBase64Output": False, } # Run the Actor and wait for it to finish run = client.actor("akash9078/ai-image-editor").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 '{ "imageUrl": "https://drive.google.com/file/d/1S5sCnyVmnRHxk233EfZ6byjClr8pxaUU/view?usp=drive_link", "prompt": "Professional product photography of a young white female model wearing/holding the provided [PRODUCT], 9:16 vertical Pinterest aspect ratio, front-facing portrait with direct confident eye contact, natural soft studio lighting with large softbox and fill reflector, high-resolution 8K commercial quality, minimalist aesthetic with product clearly showcased as hero element, choose from backgrounds: prefer natural landscape.", "outputFormat": "jpeg", "aspectRatio": "1024*1024", "seed": -1, "numInferenceSteps": 28, "guidance": 7.5, "enableBase64Output": false }' | apify call akash9078/ai-image-editor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=akash9078/ai-image-editor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Image Editor — Transform Photos with Text Prompts", "description": "AI-powered image-to-image editing using text prompts. Transform photos, product images, and artwork with natural language instructions.", "version": "0.1", "x-build-id": "d8ox9kwm1L5BFZjm2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/akash9078~ai-image-editor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-akash9078-ai-image-editor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/akash9078~ai-image-editor/runs": { "post": { "operationId": "runs-sync-akash9078-ai-image-editor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/akash9078~ai-image-editor/run-sync": { "post": { "operationId": "run-sync-akash9078-ai-image-editor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "imageUrl", "prompt" ], "properties": { "imageUrl": { "title": "Source Image URL", "pattern": "^https?://.+", "type": "string", "description": "URL of the image to edit. Supports direct image URLs and public Google Drive links.", "default": "https://drive.google.com/file/d/1S5sCnyVmnRHxk233EfZ6byjClr8pxaUU/view?usp=drive_link" }, "prompt": { "title": "Edit Prompt", "type": "string", "description": "Text prompt describing the desired image edit" }, "outputFormat": { "title": "Output Format", "enum": [ "jpeg", "png", "webp" ], "type": "string", "description": "Format of the output image", "default": "jpeg" }, "aspectRatio": { "title": "Aspect Ratio", "enum": [ "1024*1024", "1024*1280", "1280*1024", "1280*1280", "1280*1536", "1536*1080" ], "type": "string", "description": "Dimensions of the output image", "default": "1024*1024" }, "seed": { "title": "Random Seed", "type": "integer", "description": "Random seed for reproducibility. Use -1 for random.", "default": -1 }, "numInferenceSteps": { "title": "Number of Inference Steps", "minimum": 1, "maximum": 100, "type": "integer", "description": "Number of denoising steps. Higher = better quality but slower." }, "guidance": { "title": "Guidance Scale", "minimum": 1, "maximum": 20, "type": "number", "description": "How closely to follow the prompt. Higher = stricter adherence." }, "enableBase64Output": { "title": "Enable Base64 Output", "type": "boolean", "description": "Return image as base64 string in the response", "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 } } } } } } } } } } ```