Workflow Templates

Bytedance Seededit 3.0 Image Generator Description Text-guided image editing model that preserves original details while making targeted modifications like lighting changes, object removal, and style conversion Overview This n8n workflow integrates with the Replicate API to use the bytedance/seededit-3.0 model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Text prompt for image generation image** (string): Input image to edit Optional Parameters seed** (integer, default: None): Random seed. Set for reproducible generation guidance_scale** (number, default: 5.5): Prompt adherence. Higher = more literal. How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: bytedance/seededit-3.0 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Ibm Granite Granite Speech 3.3 8b Text Generator Description Granite-speech-3.3-8b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Overview This n8n workflow integrates with the Replicate API to use the ibm-granite/granite-speech-3.3-8b model. This powerful AI model can generate high-quality text content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Optional Parameters seed** (integer, default: None): Random seed. Leave blank to randomize the seed. audio** (array, default: None): Audio inputs for the model. top_k** (integer, default: 50): The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering). top_p** (number, default: 0.9): A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751). prompt** (string, default: ): User prompt to send to the model. max_tokens** (integer, default: 512): The maximum number of tokens the model should generate as output. min_tokens** (integer, default: 0): The minimum number of tokens the model should generate as output. temperature** (number, default: 0.6): The value used to modulate the next token probabilities. chat_template** (string, default: None): A template to format the prompt with. If not provided, the default prompt template will be used. system_prompt** (string, default: None): System prompt to send to the model.The chat template provides a good default. How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate text content Access the generated output from the final node API Reference Model: ibm-granite/granite-speech-3.3-8b API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of text generation parameters

Fire V Sekai.mediapipe Labeler Image Generator Description Mediapipe Blendshape Labeler - Predicts the blend shapes of an image. Overview This n8n workflow integrates with the Replicate API to use the fire/v-sekai.mediapipe-labeler model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters media_path** (string): Input image, video, or training zip file Optional Parameters test_mode** (boolean, default: False): Enable test mode for quick verification max_people** (integer, default: 100): Maximum number of people to detect (1-100) export_train** (boolean, default: True): Export training zip containing json annotations and frame pngs aligned_media** (string, default: None): Optional video that is aligned with the input video's annotations frame_sample_rate** (integer, default: 1): Process every nth frame for video input How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: fire/v-sekai.mediapipe-labeler API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Campaign tracking is pivotal; it enables marketers to evaluate the efficacy of various strategies and channels. UTM parameters are particularly essential as they provide granular details about the source, medium, and campaign effectiveness. However, when this data is not automatically integrated into a centralized system, it can become a tedious and error-prone process to manually collate and analyze it. Retrieving UTM data from Shopify and storing it in Baserow enables oy to do more with this data. For example you could build a campaign database in Baserow and automatically add campaign revenue to it using this workflow template. This template will help you: Automatically retrieve UTM parameters from Shopify orders using the Shopify Admin API Process marketing data through n8n Store this data into Baserow, providing you with a dynamic, responsive base for campaign tracking and decision-making This template will demonstrate the follwing concepts in n8n: use the Schedule trigger node use the GraphQL node to call the Shopify Admin API split larger incoming datasets into n8n items with the Split node transform the data structure with the Set node control flow with the If node store data in Baserow with the Baserow node How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. You will need a running Baserow instance for this. You can also sign up for a free account at https://baserow.io/ Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/VBeN-3129RM

Overview This template helps n8n cloud plan users execute all executions to a CSV for easy data analysis. Identify what workflows are generating the most executions or could be optimized. How this workflow works Click "Test Workflow" to manually execute the workflow Open the "Convert to CSV" node to access the binary data of the CSV file Download the CSV file Nodes included: n8n node Convert to File No Operation, do nothing - replace with another Set up steps Import the workflow to your workspace Add your n8n API credential Benefits of Exporting n8n Cloud Executions to CSV Exporting n8n Cloud executions to CSV offers significant advantages for enhancing workflow management and data analysis capabilities. Here are three key benefits: Enhanced Data Analysis: Comprehensive Insights: Exporting execution data allows for in-depth analysis of workflow performance, helping identify bottlenecks and optimize processes. Custom Reporting: CSV files can be easily imported into various data analysis tools (e.g., Excel, Google Sheets, or BI software) to create custom reports and visualizations tailored to specific business needs. Improved Workflow Monitoring: Historical Data Review: Accessing historical execution data enables users to track workflow changes and their impacts over time, facilitating better decision-making. Error Tracking and Debugging: By reviewing execution logs, users can quickly identify and address errors or failures, ensuring smoother and more reliable workflow operations. Regulatory Compliance and Auditing: Audit Trails: Keeping a record of all executions provides a clear audit trail, essential for regulatory compliance and internal audits. Data Retention: Exported data ensures that execution records are preserved according to organizational data retention policies, safeguarding against data loss. By leveraging the capabilities of CSV exports, users can gain valuable insights, streamline workflow management, and ensure robust data handling practices, ultimately driving better performance and efficiency in their n8n Cloud operations.

Who is this for? For developers and organizations that need to convert HTML files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Converts HTML to file. Converts the HTML file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

This n8n workflow automates the process of researching companies by gathering relevant data such as traffic volume, foundation details, funding information, founders, and more. The workflow leverages the ProspectLens API, which is particularly useful for researching companies commonly found on Crunchbase and LinkedIn. ProspectLens is an API that provides very detailed company data. All you need to do is supply the company's domain name. You can obtain your ProspectLens API key here: https://apiroad.net/marketplace/apis/prospectlens In n8n, create a new "HTTP Header" credential. Set x-apiroad-key as the "Name" and enter your APIRoad API key as the "Value". Use this credential in the HTTP Request node of the workflow.

contract input: length of the strings and number of copies output: random strings as specified. randomness determined by Crypto node (generate/base64) How to run the workflow Import the workflow into your n8n project Click the Form Trigger to specify the length of the strings and how many copies to generate The workflow runs then displays the generated random strings How to set up No additional set up is necessary to execute the workflow manually. integration Patterns of interests formTrigger node to submit a form, then use form (end) node to display results at the end of the triggered workflow. set(dup)-summarize(concatenate) to run a part of the workflow multiple times then merge the results to one piece of data

Fire Flux Image Generator Description The image generation model tailored for local development and personal use Overview This n8n workflow integrates with the Replicate API to use the fire/flux model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image Optional Parameters seed** (integer, default: 0): Random seed. Set for reproducible generation go_fast** (boolean, default: True): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 megapixels** (string, default: 1): Approximate number of megapixels for generated image num_outputs** (integer, default: 1): Number of outputs to generate aspect_ratio** (string, default: 2:1): Aspect ratio for the generated image output_format** (string, default: png): Format of the output images output_quality** (integer, default: 80): Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs num_inference_steps** (integer, default: 4): Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster. disable_safety_checker** (boolean, default: False): Disable safety checker for generated images. How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: fire/flux API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Run Apache Airflow DAG and Retrieve XCom Value What this workflow does This workflow integrates the Apache Airflow API DAGRun and XCom. It enables n8n to trigger Airflow DAGs and retrieve the execution results. Preparation: Update Airflow API Link Prefix Navigate to the airflow-api node. Update the prefix of the Airflow API link in the format: http(s)://ip:port. Example: https://airflow.example.com Configure Authentication Go to the Airflow: dag_run node. Update the Basic Auth credentials with your Airflow username and password. Repeat this step for Airflow: dag_run - state and Airflow: dag_run - get result nodes. Security Note: Using Basic Authentication requires storing credentials in plaintext. If possible, consider using API Keys or Tokens for enhanced security. An example is setting Airflow's API Authentication to basic\_auth. Choose other authentication methods if needed. Ensure the user account has the following permissions: can create on DAG Runs, can read on DAG Runs, can read on XComs, can edit on DAGs, and can read on DAGs. How to Use: To execute this workflow, use the Execute Sub-workflow node with the following input parameters: dag\_id**: The DAG ID (name) in Airflow that you want to trigger. task\_id**: The Task ID (name) from which you want to retrieve the XCom return\_value. conf**: Input data for the Airflow DAG run. wait**: Delay (in seconds) between each Airflow: dag_run - state check. wait\_time**: The maximum time (in seconds) to wait for Airflow: dag_run - state before returning an error. Output: The workflow returns the XCom result from Airflow: dag_run - get result. The XCom return_value is stored in the value field.

Receive Webhook Notification The workflow starts when a webhook receives a POST request from Whapi, notifying that a new participant has joined a WhatsApp group. Filter the Event The workflow checks two conditions: The event is for the correct WhatsApp group (matching the specific group ID). The action type is "add" (meaning a user was added to the group). Send Welcome Message If both conditions are met, the workflow sends a personalized welcome message to the new participant via Whapi. The message explains the group rules and how the user can earn points and participate in weekly raffles. Create Airtable Record After sending the welcome message, the workflow creates a new record in the Airtable database for the new participant. The record includes: The participant’s WhatsApp ID An initial engagement count of 100 points The date of the last interaction (set to today) Result Every new group member is automatically welcomed and registered in your engagement database with starter points, ready to participate in your group’s activities and rewards. This workflow ensures new users are greeted, informed, and instantly included in your engagement tracking system.

This workflow will allow you to enrich in real-time a form submission from Webflow using Datagma. Based on the result of this workflow, a specific Calendly link will be shown on the website. If the process outcome is '1', a link for a one-on-one demo will be provided. If the process outcome is '2', a link for a group demo will be shown. Full guide here: Real-time Lead Routing