Back to Catalog

Free AI image generator - n8n automation workflow with Gemini/ChatGPT

Agent CircleAgent Circle
78600 views
2/3/2026
Official Page

This n8n template demonstrates how to use AI to generate custom images from scratch - fully automated, prompt-driven, and ready to deploy at scale.

Use cases are many: You can use it for marketing visuals, character art, digital posters, storyboards, or even daily image generation for your personal purposes.

How It Works

  • The flow is triggered by a chat message in N8N or via Telegram. The default image size is 1080 x 1920 pixels. To use a different size, update the values in the “Fields - Set Values” node before triggering the workflow.
  • The input is parsed into a clean, structured prompt using a multi-step transformation process.
  • Our AI Agent sends the final prompt to Google Gemini’s image model for generation (you can also integrate with OpenAI or other chat models).
  • The raw image data created by the AI Agent will be run through a number of codes to make sure it's feasible for your preview if needed and downloading.
  • Then, we use an HTTP node to fetch the result so you can preview the image.
  • You can send it back to the chat message in N8N or Telegram, or save it locally to your disk.

How To Use

  • Download the workflow package.
  • Import the package into your N8N interface.
  • Set up the credentials in the following nodes for tool access and usability: "Telegram Trigger"; "AI Agent - Create Image From Prompt"; "Telegram Response" or "Save Image To Disk" (based on your wish).
  • Activate the "Telegram Response" OR "Save Image To Disk" node to specify where you want to save your image later.
  • Open the chat interface (via N8N or Telegram).
  • Type your image prompt or detailed descriptions and send.
  • Wait for the process to run and finish in a few seconds.
  • Check the result in your desired saving location.

Requirements

  • Google Gemini account with image generation access.
  • Telegram bot access and chat setup (optional).
  • Connection to local storage (optional).

How To Customize

  • We’re setting the default image size to 1080 x 1920 pixels and the default image model to "flux". You can customize both of these values in the “Fields – Set Values” node. Supported image model options include: "flux", "kontext", "turbo", and "gptimage".
  • In the “AI Agent – Create Image From Prompt” node, you can also change the AI chat model. By default, it uses Google Gemini, but you can easily replace it with OpenAI ChatGPT, Microsoft AI Copilot, or any other compatible provider.

Need Help?

Join our community on different platforms for support, inspiration and tips from others.

Website: https://www.agentcircle.ai/ Etsy: https://www.etsy.com/shop/AgentCircle Gumroad: http://agentcircle.gumroad.com/ Discord Global: https://discord.gg/d8SkCzKwnP FB Page Global: https://www.facebook.com/agentcircle/ FB Group Global: https://www.facebook.com/groups/aiagentcircle/ X: https://x.com/agent_circle YouTube: https://www.youtube.com/@agentcircle LinkedIn: https://www.linkedin.com/company/agentcircle

Free AI Image Generator - n8n Automation Workflow with Gemini/ChatGPT

This n8n workflow provides a simple yet powerful way to generate AI images on demand using a chat interface, specifically Telegram. It acts as a backend for an AI image generation bot, allowing users to request images and receive them directly in their chat.

What it does

This workflow automates the following steps:

  1. Listens for chat messages: It triggers when a new message is received in a configured Telegram chat.
  2. Processes the message: It extracts the user's message, which is expected to be a prompt for image generation.
  3. Generates an image prompt (AI Agent): It uses an AI Agent (likely powered by a large language model like Gemini or ChatGPT) to refine or create an optimal image generation prompt based on the user's input.
  4. Generates the image (HTTP Request): It sends the refined prompt to an external AI image generation API via an HTTP Request.
  5. Saves the image locally: It saves the generated image (binary data) to a local disk.
  6. Sends the image back to the user: It sends the generated image as a photo back to the user in the Telegram chat.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Telegram Bot Token: A Telegram bot token and a chat ID where the bot will listen for messages and send images.
  • AI Image Generation API: Access to an AI image generation API (e.g., DALL-E, Stable Diffusion, Midjourney, etc.) that can be called via an HTTP request. You will need its API endpoint and any necessary authentication (API key).
  • Google Gemini Chat Model Credential: A Google Gemini credential configured in n8n for the AI Agent.
  • Local Storage Access: The n8n instance needs write access to a local directory to save the generated images temporarily.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Telegram Trigger:
    • Select your Telegram credential.
    • Ensure it's configured to listen for new messages.
  3. Configure AI Agent (Google Gemini Chat Model):
    • Select your Google Gemini Chat Model credential.
    • Review the agent's configuration to ensure it's set up to generate image prompts.
  4. Configure HTTP Request:
    • Update the HTTP Request node with the URL of your chosen AI image generation API.
    • Add any required headers (e.g., Authorization with your API key) and body parameters for sending the image prompt. The prompt will likely come from the output of the "AI Agent" node.
  5. Configure Read/Write Files from Disk:
    • Specify the directory where the generated images should be saved.
    • Ensure the filename is dynamic (e.g., using a timestamp or a unique ID) to avoid overwriting.
  6. Configure Telegram (Send Image):
    • Select your Telegram credential.
    • Set the Chat ID to the chat_id from the initial Telegram Trigger node.
    • Configure it to send a photo, referencing the binary data output from the "Read/Write Files from Disk" node.
  7. Activate the Workflow: Once all credentials and configurations are set, activate the workflow.

Now, when you send a message to your Telegram bot, it will process your request, generate an image, and send it back to you!

Related Templates

Daily Magento 2 customer sync to Google Contacts & Sheets without duplicates

Automatically sync newly registered Magento 2 customers to Google Contacts and Google Sheets every 24 hours — with full duplication control and seamless automation. This workflow is a plug-and-play customer contact automation system designed for Magento 2 store owners, marketers, and CRM teams. It fetches customer records registered within the last 24 hours (from 00:00:00 to 23:59:59), checks against an existing Google Sheet to avoid reprocessing, and syncs only the new ones into Google Contacts. This ensures your contact list is always fresh and up to date — without clutter or duplicates. ✅ What This Workflow Does: Automates Customer Syncing Every day, it fetches newly registered Magento 2 customers via API based on the exact date range (midnight to midnight). Deduplicates Using Google Sheets A master Google Sheet tracks already-synced emails. Before adding a customer, the workflow checks this list and skips if already present. Creates Google Contacts Automatically For each unique customer, it creates a new contact in your Google Contacts, saving fields like first name, last name, and email. Logs New Entries to Google Sheets In Google Sheets, it even records magento 2 customer group, createdat, websiteid & store_id After syncing, it adds each new email to the tracking sheet, building a cumulative record of synced contacts. Fully Scheduled & Automated Can be scheduled with the Cron node to run daily (e.g., 12:05 AM) with no manual intervention required. 🔧 Modules Used: HTTP Request (Magento 2 API) Date & Time (for filtering registrations) Google Sheets (for reading/writing synced emails) Google Contacts (for contact creation) Set, IF, and Merge nodes (for control logic) Cron (for scheduling the automation) 💼 Use Cases: Keep your email marketing tools synced with Magento 2 customer data. Build a CRM-friendly contact base in Google Contacts without duplicates. Share customer data with sales or support teams through synced Google Sheets. Reduce manual work and human error in data transfer processes. 🔒 Credentials Required Magento 2 Bearer Auth: Set up as a credential in n8n using your Magento 2 API access token. Google API 📂 Category E-commerce → Magento 2 (Adobe Commerce) 💬 Need Help? 💡 Having trouble setting it up or want to customize this workflow further? Feel free to reach out — I’m happy to help with setup, customization, or Magento 2 API integration issues. Contact: Author 👤 Author Kanaka Kishore Kandregula Certified Magento 2 Developer https://gravatar.com/kmyprojects https://www.linkedin.com/in/kanakakishore

Kanaka Kishore KandregulaBy Kanaka Kishore Kandregula
163

Send RSS feed data to webhook

Filters articles based on keywords, checks against MongoDB for unique links, then sends results to different webhooks

daveBy dave
3968

Run bulk RAG queries from CSV with Lookio

This template processes a CSV of questions and returns an enriched CSV with RAG-based answers produced by your Lookio assistant. Upload a CSV that contains a column named Query, and the workflow will loop through every row, call the Lookio API, and append a Response column containing the assistant's answer. It's ideal for batch tasks like drafting RFP responses, pre-filling support replies, generating knowledge-checked summaries, or validating large lists of product/customer questions against your internal documentation. Who is this for? Knowledge managers & technical writers: Produce draft answers to large question sets using your company docs. Sales & proposal teams: Auto-generate RFP answer drafts informed by internal docs. Support & operations teams: Bulk-enrich FAQs or support ticket templates with authoritative responses. Automation builders: Integrate Lookio-powered retrieval into bulk data pipelines. What it does / What problem does this solve? Automates bulk queries: Eliminates the manual process of running many individual lookups. Ensures answers are grounded: Responses come from your uploaded documents via Lookio, reducing hallucinations. Produces ready-to-use output: Delivers an enriched CSV with a new Response column for downstream use. Simple UX: Users only need to upload a CSV with a Query column and download the resulting file. How it works Form submission: User uploads a CSV via the Form Trigger. Extract & validate: Extract all rows reads the CSV and Aggregate rows checks for a Query column. Per-row loop: Split Out and Loop Over Queries iterate rows; Isolate the Query column normalizes data. Call Lookio: Lookio API call posts each query to your assistant and returns the answer. Build output: Prepare output appends Response values and Generate enriched CSV creates the downloadable file delivered by Form ending and file download. Why use Lookio for high quality RAG? While building a native RAG pipeline in n8n offers granular control, achieving consistently high-quality and reliable results requires significant effort in data processing, chunking strategy, and retrieval logic optimization. Lookio is designed to address these challenges by providing a managed RAG service accessible via a simple API. It handles the entire backend pipeline—from processing various document formats to employing advanced retrieval techniques—allowing you to integrate a production-ready knowledge source into your workflows. This approach lets you focus on building your automation in n8n, rather than managing the complexities of a RAG infrastructure. How to set up Create a Lookio assistant: Sign up at https://www.lookio.app/, upload documents, and create an assistant. Get credentials: Copy your Lookio API Key and Assistant ID. Configure the workflow nodes: In the Lookio API call HTTP Request node, replace the apikey header value with your Lookio API Key and update assistantid with your Assistant ID (replace placeholders like <your-lookio-api-key> and <your-assistant-id>). Ensure the Form Trigger is enabled and accepts a .csv file. CSV format: Ensure the input CSV has a column named Query (case-sensitive as configured). Activate the workflow: Run a test upload and download the enriched CSV. Requirements An n8n instance with the ability to host Forms and run workflows A Lookio account (API Key) and an Assistant ID How to take it further Add rate limiting / retries: Insert error handling and delay nodes to respect API limits for large batches. Improve the speed: You could drastically reduce the processing time by parallelizing the queries instead of doing them one after the other in the loop. For that, you could use HTTP request nodes that would trigger your sort of sub-workflow. Store results: Add an Airtable or Google Sheets node to archive questions and responses for audit and reuse. Post-process answers: Add an LLM node to summarize or standardize responses, or to add confidence flags. Trigger variations: Replace the Form Trigger with a Google Drive or Airtable trigger to process CSVs automatically from a folder or table.

Guillaume DuvernayBy Guillaume Duvernay
293