This workflow automates the process of estimating a personโs fashion size from an uploaded image using an AI model. This workflow is an automated pipeline that uses an AI model to estimate a person's body measurements and clothing size from an image URL. --- Key Features ๐ Full Automation โ From image submission to result display, the process requires no manual steps. โ๏ธ Easy Integration โ Uses n8nโs native nodes and simple HTTP requests to connect with Fal.aiโs API. ๐ Rea
AI-powered body measurement & clothing size estimator from image with Fal.ai is a ready-made n8n workflow you import as a workflow JSON file โ no build required. It connects LinkedIn. It's free to download. Follow the 5-step import below to go live in minutes.

This workflow automates the process of estimating a personโs fashion size from an uploaded image using an AI model. This workflow is an automated pipeline that uses an AI model to estimate a person's body measurements and clothing size from an image URL. --- Key Features ๐ Full Automation โ From image submission to result display, the process requires no manual steps. โ๏ธ Easy Integration โ Uses n8nโs native nodes and simple HTTP requests to connect with Fal.aiโs API. ๐ Real-Time Processing โ Automatically waits and checks for the AI result, ensuring the user receives the output as soon as itโs ready. ๐งฉ Modular Design โ Each step (submit โ process โ check โ result) is clearly separated, making it easy to modify or extend (e.g., adding notifications or storing results in a database). ๐ก User-Friendly Interface โ The initial form and final result form make it accessible even for non-technical users. ๐ Secure โ Authentication to the Fal.ai API is handled through HTTP header authorization, keeping API keys protected. --- How it works 1. Form Trigger: The workflow starts with a public form where a user submits a URL of an image. 2. AI Processing Request: The submitted image URL is sent to the fal.run AI service (specifically, the "fashion-size-estimator" model) via a POST request. This initial request places the job in a queue and returns a unique requestid. 3. Polling for Completion: The AI processing is asynchronous and takes some time. The workflow enters a loop where it: Waits: Pauses for 10 seconds to give the AI model time to process the request. Checks Status: Uses the requestid to check the status of the job. Conditional Check: An IF node checks if the status is "COMPLETED". If NO (not completed), the loop repeats (wait, then check again). If YES, the workflow exits the loop. 4. Fetching and Displaying Results: Once processing is complete, the workflow retrieves the final result (containing the size, height, bust, waist, and hip measurements) and automatically displays it to the user on a "thank you" page. --- Set up steps To make this workflow operational, you need to configure the API authentication. 1. Obtain an API Key: Create an account at fal.ai Navigate to your account settings to generate an API key. 2. Configure Credentials in n8n: In your n8n instance, create a new HTTP Header Auth credential (you can name it "Fal.run API"). Set the Name field to Authorization. Set the Value field to Key YOURAPIKEY, replacing "YOURAPIKEY" with the actual key you obtained from fal.ai. Ensure this credential is correctly selected in the three HTTP Request nodes: "Send image to estimator", "Get status", and "Get result". --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
Download the workflow JSON file after purchase.
Open n8n โ click the menu โ Import from File.
Select the downloaded JSON and import.
Set up credentials for each node that requires them.
Click Execute Workflow to test, then activate.
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