Block 1 - Get File image
- Type / Role
- n8n-nodes-base.httpRequest - httpRequest
- Config choices
- Version 4.2
This workflow is provided as-is. Please review and test before using in production.
This workflow automates the process of transforming user submitted photos (also bad selfie ) into professional CV and LinkedIn headshots using the Nano Banana Pro AI model. From selfie To CV/Li...
n8n-nodes-base.httprequest, n8n-nodes-base.wait, n8n-nodes-base.if, n8n-nodes-base.googledrive, n8n-nodes-base.stickynote, n8n-nodes-base.telegramtrigger, n8n-nodes-base.switch, n8n-nodes-base.ftp
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Davide.
Original n8n.io sourceThis workflow automates the process of transforming user-submitted photos (also bad selfie) into professional CV and LinkedIn headshots using the Nano Banana Pro AI model.
| From selfie | To CV/Linkedin Headshot |
|---|---|
From receiving a photo to delivering a polished LinkedIn-style headshot, the workflow requires zero manual intervention.
Users can simply send a picture via Telegram—no need to log into dashboards or upload images manually.
Only the authorized Telegram user can trigger the workflow, preventing unauthorized usage.
The workflow includes a robust status-checking mechanism that:
You can run the workflow either:
This makes it usable in multiple environments.
Processed images are automatically stored in:
Thanks to detailed prompt engineering, the workflow consistently produces:
Perfect for LinkedIn, CVs, or corporate profiles.
Each step is isolated and can be modified:
The workflow supports two input methods:
Telegram Trigger Path: Users can send photos via Telegram, which are then processed through FTP upload and transformed into professional headshots.
Manual Trigger Path: Users can manually trigger the workflow with an image URL, bypassing the Telegram/FTP steps for direct processing.
The core process involves:
Authorization Setup:
Authorization: Key YOURAPIKEY)Storage Configuration:
ftp_path: Your server directory path (e.g., /public_html/images/)base_url: Corresponding base URL (e.g., https://website.com/images/)Input Method Selection:
API Endpoints:
File Naming:
yyyyLLddHHmmss-filename.extThe workflow handles the complete pipeline from image submission through AI processing to storage distribution, with proper error handling and status checking throughout.
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This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 25 workflow blocks. Download the JSON for the full node graph.
| Workflow | Transform selfies into professional LinkedIn headshots with Nano Banana Pro & Telegram |
|---|---|
| Complexity | advanced |
| Nodes | 25 |
| Categories | Content Creation, Multimodal AI |
| Author | Davide |
| Published | 03 Dec 2025 |
Use the JSON export at /data/workflows/11458/11458.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
This workflow automates the process of transforming user submitted photos (also bad selfie ) into professional CV and LinkedIn headshots using the Nano Banana Pro AI model. From selfie To CV/Li...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Content Creation, Multimodal AI use case.