This article provides a complete, practical walkthrough of the Splitout Extractfromfile Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title:**  
   AI-Powered Visual Style Transfer with n8n, Google Imagen 3.0, and Gemini 2.0
   
   **Meta Description:**  
   Discover how to build a no-code image generation workflow using n8n with Google’s Imagen 3.0 and Gemini 2.0 models. Automatically generate styled visuals with user-defined prompts and reference images.
   
   **Keywords:**  
   n8n workflow, AI image generation, visual style transfer, Google Imagen 3.0, Gemini 2.0, Cloudinary API, Gmail API, style-based image generation, no-code automation, generative AI workflow
   
   ---
   
   ## AI-Powered Visual Style Transfer Using n8n and Google’s Imagen 3.0
   
   AI has revolutionized creative workflows, making it possible to automate design tasks once limited to skilled artists and expensive tools. One stellar example of this innovation is a recently built n8n workflow that allows users to generate images in the visual style of any reference image. By combining Google’s cutting-edge AI models—Imagen 3.0 and Gemini 2.0—with n8n’s powerful workflow automation engine, this solution empowers users to turn imagination into stunning visuals without writing a single line of code.
   
   ### What This Workflow Does
   
   This n8n automation workflow lets users upload a source image—that is, an image whose visual style they admire—and a prompt for a target image they'd like to generate in that style. Behind the scenes, the workflow:
   
   1. Extracts the visual style of the source image using Google Gemini 2.0's multimodal image understanding capabilities.
   2. Combines this extracted style with the user’s prompt.
   3. Sends the merged prompt to Google Imagen 3.0 to generate AI-based image outputs.
   4. Renders the images onto a beautiful HTML gallery page.
   5. Optionally, sends results via email if the user provides one.
   
   It is a seamless end-to-end journey from idea to art, all happening within a secure, customizable, no-code platform.
   
   ---
   
   ### How the Workflow Actually Works
   
   Let’s break down the life cycle of the workflow to appreciate its elegance:
   
   #### 1. User Input via Form Interface
   
   The process kicks off with a user-facing form, thanks to n8n’s built-in Form Trigger node. It asks for:
   
   - A Source Image URL (whose style we want to mimic)
   - A Target Prompt (a textual description of the image to generate)
   - Optional number of images (1–4)
   - Optional email address
   
   Form validation ensures the Source Image is a valid URL before proceeding.
   
   #### 2. Prepare Inputs and Download Style Image
   
   Once the form is submitted:
   
   - User inputs are parsed and stored via a Set node.
   - The workflow downloads the Source Image using an HTTP Request node.
   - The downloaded image is converted into base64 format for transmission in the next step.
   
   #### 3. Visual Style Analysis with Gemini 2.0
   
   The base64-encoded image and a style-prompting instruction are sent to Gemini 2.0. Its task? Describe the visual style of the image in natural language.
   
   Gemini understands elements like color palettes, brush strokes, lighting, artistic influence, and overall mood. It even omits brand-specific or copyrighted characters to keep results generic and ethical.
   
   Sample prompt sent to Gemini:
   
   > “Describe the visual style of this image. Do not include any character names or IP in the description. Include names of any famous artists associated with this style if known.”
   
   This prompt ensures useful, reusable descriptions perfect for style referencing.
   
   #### 4. Style-Infused Image Generation with Imagen 3.0
   
   Using Gemini’s output and the user’s original prompt, a combined prompt is assembled:
   
   > “{Gemini Style Description} Generate the following image: {Target Prompt}”
   
   This merged content is submitted via API to Google Imagen 3.0—Google's powerful text-to-image model. The workflow lets users request up to 4 variations.
   
   Each resulting image is returned as a Base64-encoded image file.
   
   #### 5. Image Conversion, Cloudinary Upload & HTML Gallery
   
   Each image:
   
   - Is converted into a proper image file using a Convert to File node.
   - Uploaded to Cloudinary, a media hosting service.
   
   Next, all image URLs are leveraged to generate an HTML gallery using the HTML node. This includes:
   
   - A descriptive heading based on the user’s prompt
   - The actual Source Image used for style extraction
   - The full style description from Gemini
   - A clickable image gallery of all generated visuals
   
   #### 6. Output Delivery
   
   The HTML is converted into a downloadable HTML file. Depending on whether the user supplied an email address:
   
   - If email is present: Results are delivered via Gmail through n8n's Gmail node.
   - If not: The HTML file is offered as an instant download directly via the final Form Completion node.
   
   ---
   
   ### Third-Party APIs Used
   
   This workflow seamlessly integrates the following APIs:
   
   1. **Google Gemini 2.0 (PaLM API):**  
      Used to analyze visual styles of input source images.
   
   2. **Google Imagen 3.0:**  
      Performs text-to-image generation using style-enhanced prompts.
   
   3. **Cloudinary API:**  
      Handles image hosting to generate publicly accessible image URLs.
   
   4. **Gmail API:**  
      Sends the HTML gallery to user-provided email addresses.
   
   ---
   
   ### Ideal Use Cases
   
   - Creating stylized variants of logos or branding assets.
   - Exploring design options for marketing materials rapidly.
   - Artistic experimentation or mood board creation.
   - Bridging non-illustrators and creative professionals.
   
   Since everything works without code, even non-technical users can implement or adapt the solution.
   
   ---
   
   ### Getting Started
   
   To use this workflow:
   
   - Make sure your n8n instance is running and connected to Google Gemini, Imagen, Cloudinary, and Gmail credentials.
   - Share the public form link with team members or clients.
   - Profit from faster, more human-centered design workflows!
   
   ---
   
   ### Customizations and Scalability
   
   Want more control? You can easily adapt this:
   
   - Swap Google Imagen for DALL·E or Stable Diffusion.
   - Replace Cloudinary with S3 or other CDNs.
   - Use a Webhook Trigger instead of a Form to create a full API endpoint.
   - Extend delivery options, such as Slack or internal CMS integration.
   
   ---
   
   ### Final Thoughts
   
   This workflow demonstrates the creative superpowers unlocked by combining modern generative AI tools with a no-code automation platform like n8n. Whether you're a marketer testing design variants or an AI enthusiast exploring hybrid automation, this is a powerful and flexible starting point for visual content generation at scale.
   
   ---
   
   Happy automating, and remember—great design is now just a workflow away.
   
   ---
   
   Need help? Join the n8n Discord Community or visit the official Forum to share ideas and get support.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.