# Nano Banana 2 (no API key) (`jindrich.bar/nano-banana-2`) Actor Generate photorealistic images with the latest Nano Banana model for text-to-image generation. No need for API key, just use your Apify account. - **URL**: https://apify.com/jindrich.bar/nano-banana-2.md - **Developed by:** [Jindřich Bär](https://apify.com/jindrich.bar) (community) - **Categories:** AI, Developer tools, Other - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $0.08 / image This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Nano Banana 2 Generate vibrant, stylized images from text prompts in seconds using Nano Banana 2 — a compact but capable image generation model built for speed without sacrificing creative range. Whether you need quick concept sketches, colorful illustrations, or eye-catching social content, Nano Banana 2 delivers results fast and consistently. Nano Banana 2 is optimized for low latency and high throughput, making it ideal for workflows that require rapid iteration — brainstorming visual ideas, batch-generating assets, or powering real-time applications where waiting seconds matters. ### What can Nano Banana 2 generate? **Illustrations and concept art** — flat design graphics, character concepts, environment sketches, and stylized scenes with bold colors and clean shapes. **Social and marketing content** — eye-catching thumbnails, ad creatives, banners, and social media visuals generated directly from a brief text description. **Playful and whimsical imagery** — cartoon animals, surreal landscapes, imaginative food art, and anything that calls for a light and energetic visual style. **Rapid prototyping** — generate multiple visual directions from a single prompt to explore ideas before committing to a final design. ### Examples | Prompt | Output | |--------|--------| | Prompt: A rolling countryside landscape knitted from yarn fields of green yellow and brown yarn stretch across undulating hills textured stiches \[truncated] | ![](https://lh3.googleusercontent.com/p3cPxXCb2Ysm6wZh6kXHN7-n9a1wGQqmvbPgPCt_osoPtviV3iOXE5MEU1TAuE_YexScQpG8lv0l3A7cK9ZrWH-4bRMoMzbMffbgfiduRA=w2880-h1620-n-nu-rw-lo) | | Prompt: Extreme macro photograph capturing the breathtaking iridescence of a Blue Morpho butterfly wing, emphasizing abstract beauty and intricate structural marvel. \[truncated] | ![](https://lh3.googleusercontent.com/QntNnmnSqGie9nfFNL-NWI5upJIC6kV8JugTVny1l_knkK-mGdGcF1hMPXH09oAEG19eNzQOXUTweV0xWsgdlVdrq91Ytcxx0gkpv8mzYnSzofFx=w2880-h1620-n-nu-rw-lo) | | Prompt: A horizontally oriented rectangular stamp features the Mission District's vibrant culture, portrayed in shades of warm terracotta orange \[truncated] | ![](https://lh3.googleusercontent.com/D4RtpFiZNJqangcsC5XuRppmwwdYBfV3Eum_F1kx693AgBvlHKv6dHad3j8O7zppAfLDkET_VjGR_ut-0j9pX7tO1_kwi1ZlYBvPuZPOSVw=w2880-h1620-n-nu-rw-lo) | ### Input The Actor takes a single required field: - **prompt** — a text description of the image you want to generate. The more specific you are about style, mood, colors, and subject, the better the result. Short prompts work too — Nano Banana 2 makes confident creative choices when details are left open. ### Output The generated image is stored in the Actor's default key-value store under the `OUTPUT` key and is also pushed to the dataset with the prompt and a public image URL. ### Why run Nano Banana 2 on Apify? **Scheduling** — run image generation on any cron schedule. Automatically produce fresh visual content daily, batch-generate assets on a fixed cadence, or refresh creatives on a weekly cycle — all without writing orchestration code. **Integrations** — send generated images directly to Slack, Google Drive, Dropbox, email, or any webhook. Chain Nano Banana 2 with other Apify Actors to build complete pipelines — for example, pull trending topics from the web and automatically generate matching visuals. **API & SDKs** — call this Actor from Python, JavaScript, or any HTTP client. Integrate fast image generation into your existing apps, pipelines, or internal tools. The Apify API handles queuing, retries, and storage for you. **MCP Server** — this Actor works with the [Apify MCP Server](https://apify.com/apify/actors-mcp-server), letting you call it directly from AI assistants like Claude or any MCP-compatible client. Ask your assistant to generate an image and it happens — no code required. **Monitoring & storage** — every run is logged with full input/output history. Generated images are persisted in Apify's key-value store and datasets so you always have a reliable record and can retrieve or share results later. # Actor input Schema ## `prompt` (type: `string`): Text prompt describing the image to generate. ## Actor input object example ```json {} ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("jindrich.bar/nano-banana-2").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 = {} # Run the Actor and wait for it to finish run = client.actor("jindrich.bar/nano-banana-2").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 '{}' | apify call jindrich.bar/nano-banana-2 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jindrich.bar/nano-banana-2", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Nano Banana 2 (no API key)", "description": "Generate photorealistic images with the latest Nano Banana model for text-to-image generation. No need for API key, just use your Apify account.", "version": "0.0", "x-build-id": "tItzk7iDLSq7sTK6y" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jindrich.bar~nano-banana-2/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jindrich.bar-nano-banana-2", "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/jindrich.bar~nano-banana-2/runs": { "post": { "operationId": "runs-sync-jindrich.bar-nano-banana-2", "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/jindrich.bar~nano-banana-2/run-sync": { "post": { "operationId": "run-sync-jindrich.bar-nano-banana-2", "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": [ "prompt" ], "properties": { "prompt": { "title": "Prompt", "type": "string", "description": "Text prompt describing the image to generate." } } }, "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 } } } } } } } } } } ```