Workflow Templates

🎬 AI Video Studio Bot - Telegram to YouTube Shorts, TikTok and Instagram Reels Automation Transform text into viral shorts — all from your phone 📱✨ 🎥 Watch It In Action 🔗 Full Demo: youtu.be/OI_oJ_2F1O0 🚀 What This Workflow Does Imagine having a full-stack AI video production studio in your pocket — no editing software, no dashboard hopping, no prompt engineering. Just pure creation magic through Telegram. This n8n workflow transforms Telegram into your personal AI video factory that: Your Message → AI Magic → Viral Short → Auto-Published ⏱️ 30 seconds 🎬 2-5 minutes 📤 Done! The Complete Pipeline: 📱 Message Telegram Bot - Send text, image, or voice memo 🤖 AI Prompt Generation - GPT-4 crafts perfect video prompts 🎬 Video Creation - Veo 3, Sora 2, or Seedance generates your short 📤 Auto-Upload - Instantly publishes to YouTube Shorts 🔁 Extend & Iterate - One-tap video extension (Veo only) No manual work. No technical skills. No limits. 💡 Why This Changes Everything | Traditional Way | This Workflow | |----------------|---------------| | ❌ Open 5+ platforms | ✅ One Telegram chat | | ❌ 30 min per video | ✅ 5 min per video | | ❌ Complex prompts needed | ✅ AI writes prompts for you | | ❌ Manual uploads | ✅ Auto-publishes everywhere | | ❌ Desktop only | ✅ Works from your phone | Result: Create 10+ YouTube Shorts during your lunch break 🚀 🎨 Video Styles - Choose Your Vibe Control everything with simple Telegram commands: | Command | Style | Perfect For | |---------|-------|------------| | /general | 🎭 Creative Shorts | Product demos, hooks, viral content | | /lost | 👻 Found Footage | Mystery, horror, urban exploration | | /3d | 🎮 3D Objects | Talking products, explainers, memes | | /story | 📖 Emotional Stories | Multi-scene narratives, brand stories | No command? AI intelligently picks the best style for your message. 🤖 AI Models - Pick Your Engine Choose your video generation model right from Telegram: Veo 3 / Veo 3 Fast ⚡ Best for: Quick iterations, realistic scenes Speed: 2-3 minutes Unique: Video extension support Sora 2 🎬 Best for: Cinematic quality, long sequences Speed: 4-5 minutes Unique: Best motion consistency Seedance 1.5 Pro 🌊 Best for: Artistic effects, fluid motion Speed: 3-4 minutes Unique: Stylized aesthetics Select directly in-chat with interactive buttons! ⚡ Power Features 🎯 Smart Video Generation AI analyzes your message intent Generates optimal prompts automatically Adapts to text, images, or voice input 📤 Auto-Publishing Pipeline Uploads to YouTube Shorts instantly AI-generated titles, descriptions, tags SEO-optimized for maximum reach 🔄 Extend & Refine One-tap video extension (Veo only) Keep the vibe, extend the story No re-generation needed 💳 Credit Management Real-time credit checking Prevents failed generations Session-based tracking with Redis 🔔 Status Monitoring Real-time generation updates Webhook polling for long jobs Graceful error handling & cancellation 🗂️ Session Storage Redis-powered state management Resume interrupted workflows Track generation history 🎪 Perfect For | Creator Type | Use Case | |-------------|----------| | 🎥 Faceless Channels | Generate endless Shorts without showing face | | 🏢 Agencies | Scale content production 10x for clients | | 📱 Solo Creators | Daily Shorts from your phone, no laptop needed | | 🤖 AI Farms | Automate content pipelines end-to-end | | 🧪 Experimenters | Rapid prototyping of video ideas | | 📊 Marketers | A/B test video concepts at scale | 🛠️ Tech Stack Telegram Bot API → User interface OpenAI GPT-4 → Prompt generation KIE.ai → Video generation (Veo/Sora/Seedance) YouTube Data API → Auto-publishing Redis → Session & state management S3-compatible → Video storage n8n → Orchestration layer Requirements: ✅ Telegram Bot Token ✅ OpenAI API Key ✅ KIE.ai Account (Veo/Sora/Seedance access) ✅ YouTube OAuth Credentials ✅ Redis Instance (recommended) ✅ S3-compatible Storage ✅ n8n Instance (cloud or self-hosted) 🎬 Real-World Workflow Example You: "A golden retriever puppy discovering snow for the first time" Bot: ✨ Generating your video... 📊 Credits: 50 remaining 🎬 Using: Veo 3 Fast ⏱️ ETA: 2 minutes 2 minutes later: ✅ Your video is ready! 📺 Uploaded to YouTube Shorts 🔗 Link: youtube.com/shorts/abc123 👁️ Views: 0 → 1.2K (24 hours) [Extend Video] [Generate New] Result: Viral short created from your phone while waiting for coffee ☕ 🔧 Customization Ideas 🎨 Extend the Platform Add TikTok publishing Include Instagram Reels Add Twitter video posts Support LinkedIn video 🎙️ Alternative Inputs Replace Telegram with WhatsApp Add Discord bot interface Support Slack commands Email-to-video pipeline 🎭 Creative Variations Swap OpenAI for Claude/Gemini Add custom style presets Include watermarking steps Generate captions automatically 📊 Analytics & Tracking Log all generations to Google Sheets Track video performance metrics A/B test title/thumbnail combinations Monitor credit usage trends 📊 Success Metrics After using this workflow for 30 days: | Metric | Before | After | Improvement | |--------|--------|-------|-------------| | ⏱️ Time per video | 45 min | 5 min | 9x faster | | 📹 Videos/week | 5 | 50+ | 10x volume | | 💰 Cost per video | $15 | $2 | 7.5x cheaper | | 📱 Creation location | Desktop only | Anywhere | ∞ flexibility | | 🧠 Prompt writing | Manual | Automated | No skill needed | 🚀 Quick Start Import workflow to n8n Add credentials (Telegram, OpenAI, KIE.ai, YouTube, Redis) Configure video storage (S3) Activate workflow Message your bot and watch the magic happen Setup time: ~20 minutes First video: ~5 minutes after setup 🏷️ Tags telegram ai-video youtube-shorts automation content-creation openai veo sora seedance text-to-video social-media creator-tools faceless-channel redis s3 n8n-workflow telegram-bot video-automation shorts-generator 📜 License MIT License - Use freely, modify, share, monetize! ⚡ Stop editing. Start generating. Scale your content empire. ⚡ Created by Joe Venner | Built with ❤️ and n8n

How it works This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions. Think of it like this: instead of a general-knowledge AI, you're building an expert librarian. The workflow is split into two main parts: Part 1: Indexing the Knowledge (Building the Library) This is a one-time process you run manually. The workflow automatically scrapes all pages of the n8n documentation, breaks them down into small, digestible chunks, and uses an AI model to create a special numerical representation (an "embedding") for each chunk. These embeddings are then stored in n8n's built-in Simple Vector Store. This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph. Important: This in-memory knowledge base is temporary. It will be erased if you restart your n8n instance, and you will need to run the indexing process again. Part 2: The AI Agent (The Expert Librarian) This is the chat interface. When you ask a question, the AI agent doesn't guess the answer. Instead, it uses your question to find the most relevant "index cards" (chunks) from the knowledge base it just built. It then feeds these specific, relevant chunks to a powerful language model (Gemini) with a strict instruction: "Answer the user's question using ONLY this information." This ensures the answers are accurate, factual, and grounded in your provided documents. Set up steps Setup time: 2 minutes (plus 15-20 minutes for indexing) This template uses n8n's built-in tools, removing the need for an external database. Follow these simple steps to get started. Configure Google AI Credentials: You will need a Google AI API key for the Gemini models. In your n8n workflow, go to any of the three Gemini nodes (e.g., Gemini 2.5 Flash). Click the Credential dropdown and select + Create New Credential. Enter your Gemini API key and save. Apply Credentials to All Nodes: Your new Google AI credential is now saved. Go to the other two Gemini nodes (Gemini Chunk Embedding and Gemini Query Embedding) and select your newly created credential from the dropdown list. Build the Knowledge Base: Find the Start Indexing manual trigger node at the top-left of the workflow. Click its "Execute workflow" button to start the indexing process. ⚠️ Be Patient: This will take 15-20 minutes as it scrapes and processes the entire n8n documentation. You only need to do this once per n8n session. If you restart n8n, you must run this step again. Chat with Your Expert Agent: Once the indexing is complete, Activate the entire workflow using the toggle at the top of the screen. Open the RAG Chatbot chat trigger node (bottom-left) and copy its Public URL. Open the URL in a new tab and start asking questions about n8n! For example: "How does the IF node work?" or "What is a sub-workflow?".

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Introduction Tired of spending time crafting the perfect AI prompt? This workflow takes your simple ideas like "write a blog post" and automatically transforms them into detailed, structured prompts that actually work. 🎯 What This Does Automatically converts simple user prompts like "write a blog post" into structured, professional AI prompts with metadata, variables, and clear instructions. Perfect for everybody, all industries and organizations who are wanting to eliminate prompt engineering works. 🔄 How It Works Google Sheets Trigger monitors for new prompts AI Enhancement Pipeline uses Gemini + Groq to add structure & context Field Completion auto-generates missing metadata (topic, categories) Quality Assurance validates & stores complete results 🚀 Setup Requirements AI APIs**: Gemini, Telegram, Groq API keys Google Sheets**: 2 sheets (Main, ModifiedPrompt) 5 minutes setup time** - detailed instructions in blue sticky notes Set up steps Setup time: < 5 minutes Create a Google Spreadsheet with two tabs (sheets): OriginalPrompts and ModifiedPrompts. OriginalPrompts columns: Original Prompt ID | Model | Original Prompt | Created Time ModifiedPrompts columns (example): Modified Prompt ID | Original Prompt ID | Topic | Topic Categories | Modified Prompt | Prompt Title | Prompt Type | Model Used | Improvement Notes | Updated Time | Created Time | isProcessed Add and attach credentials in n8n: Google Sheets OAuth2 (required for getting new prompt) Gemini and Groq API credentials (required for AI Agent) Telegram credential (required for notifications) Save & Activate the workflow. Add a test row to OriginalPrompts, for example: Original Prompt ID: 1 — Original Prompt: "Write a short blog post about AI ethics". Wait ~30–60s and check ModifiedPrompts for the enhanced output. That’s it ! Once it configured, drop short ideas into your sheet and get professional prompts back automatically. Your prompts get better, your AI outputs improve, and you save hours of manual prompt crafting.

This is an end-to-end workflow for creating a simple OpenAI Assistant. The whole process is done with n8n nodes and do not require any programming experience. The workflow is divided into three main steps: Step 1: Get a Google Drive File and Upload to OpenAI The workflow starts by retrieving a file from Google Drive using the "Get File" node. The example file used is a Music Festival document. The retrieved file is then uploaded to OpenAI using the "Upload File to OpenAI" node. Run this section only once. The file is stored persistently on the OpenAI side. Step 2: Set Up a New Assistant In this step, a new assistant is created using the "Create new Assistant" node. The assistant is given a name, description, and system prompt. The uploaded file from Step 1 is attached as a knowledge source for the assistant. Same as for Step 1, run this section only once. Step 3: Chat with the Assistant The "Chat Trigger" node initiates the conversation with the assistant. The "OpenAI Assistant" node handles the conversation, using the assistant created in Step 2. Step 4: Expand the Assistant This step provides resources for ideas on how to expand the Assistant's capabilities: Create a WhatsApp bot Create a simple Telegram bot Create a Telegram AI bot (YouTube video) By following this workflow, users can create their own AI-powered assistants using OpenAI's API and integrate them with various platforms like WhatsApp and Telegram.

Creativeathive Lemaar Door Mockedup AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the creativeathive/lemaar-door-mockedup model. This powerful AI model can generate high-quality other 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. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: creativeathive/lemaar-door-mockedup API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Spuuntries Ilearnmate Icts AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the spuuntries/ilearnmate-icts model. This powerful AI model can generate high-quality other 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): Seed for reproducibility of example generation and vector training. Set to 0 for random behavior. num_examples_per_side** (integer, default: 3): Number of descriptive examples to generate for each side of the contrast. More examples might lead to better vectors but will increase generation time. attributes_to_generate** (string, default: girly,modestly,verbose,happy): Comma-separated list of attributes for which to generate control vectors (e.g., 'girly,modestly,verbose,happy') 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 other content Access the generated output from the final node API Reference Model: spuuntries/ilearnmate-icts API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Justingirard Draft Ui Designer Image Generator Description An experiment: a fine-tuned FLUX model for UI design generation Overview This n8n workflow integrates with the Replicate API to use the justingirard/draft-ui-designer 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. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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: justingirard/draft-ui-designer API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

This workflow demonstrates the use of the Split In Batches node and the Wait node to avoid API rate limits. Customer Datastore node: The workflow fetches data from the Customer Datastore node. Based on your use case, replace it with a relevant node. Split In Batches node: This node splits the items into a single item. Based on the API limit, you can configure the Batch Size. HTTP Request node: This node makes API calls to a placeholder URL. If the Split In Batches node returns 5 items, the HTTP Request node will make 5 different API calls. Wait node: This node will pause the workflow for the time you specify. On resume, the Split In Batches node gets executed node, and the next batch is processed. Replace Me (NoOp node): This node is optional. If you want to continue your workflow and process the items, replace this node with the corresponding node(s).

Who is this for? This workflow is perfect for anyone looking to automate the process of replacing variables in Google Docs with data from form. What problem does this workflow solve? This workflow automates the process of filling Google Docs templates with data coming from n8n forms or other variables. It’s especially useful for generating documents like contracts, invoices, or reports quickly and efficiently without manual intervention. What does this workflow do? The workflow receives data from a form in n8n. It uses the form data to replace the corresponding variables (e.g., {{example_variable}}) in a Google Docs template. The document is then generated with the new values, ready for further use, such as sending or archiving. How to set up this workflow? Prepare the template: Create a Google Docs template with variables in the {{variable}} format that you want to replace with form data. Modify the variables in the n8n form: Make sure the form fields correspond to the variables you want to replace in the Google Docs template. Connect to Google Docs: Set up the connection to Google Docs in n8n using the appropriate authentication credentials. Test the workflow: Run the workflow to ensure that the form data correctly replaces the variables in the Google Docs template. How to customize this workflow to your needs? Change the data source: You can modify the form or other data sources (e.g., API) from which the replacement values will be fetched. Customize the Google Docs template: Adapt the template to include additional fields for replacement as needed. Integrate with other applications: You can expand the workflow to include actions like sending the generated document via email, saving it to Google Drive, or passing it to other systems.

This template demonstrates how to trigger an AI Agent with Siri and Apple Shortcuts, showing a simple pattern for voice-activated workflows in n8n. It's easy to customize—add app nodes before the AI Agent step to pass additional context, or modify the Apple Shortcut to send inputs like text, geolocation, images, or files. Set Up Basic instructions in template itself. Requirements n8n account** (cloud or self-hosted) Apple Shortcuts app** on iOS or macOS. Dictation ("Siri") must be activated. Download the Shortcuts template here. Key Features: Voice-Controlled AI:** Trigger AI Agent via Siri for real-time voice replies. Customizable Inputs:** Modify Apple Shortcut to send text, images, geolocation, and more. Flexible Outputs:** Siri can return the AI’s response as text, files, or customize it to trigger CRUD actions in connected apps. Context-Aware:** Automatically feeds the current date and time to the AI Agent, with easy options to pass in more data. How It Works: Activate Siri and speak your request. Siri sends the transcribed text to the n8n workflow via Apple Shortcuts. AI Agent processes the request and generates a response. Siri reads the response, or the workflow can return geolocation, files, or even perform CRUD actions in apps. Inspiration: Custom Use Cases Tweak this template and make it your own. Capture Business Cards:** Snap a photo of a business card and record a voice note. Have the AI Agent draft a follow-up email in Gmail, ready to send. Voice-to-Task Automation:** Speak a new to-do item, and the workflow will add it to a Notion task board. Business English on the Fly:** Convert casual speech into polished business language, and save the refined text directly to your pasteboard, ready to be pasted into any app. "It's late because of you" -> "There has been a delay, and I believe your input may have contributed to it."

Automated Binary Data Extraction from Gmail to Google Drive Folder This workflow is designed to automate the process of handling emails with binary attachments. It triggers when a new email arrives in a specified Gmail account (or can be configured with a similar email trigger) and is set to download any binary attachments. The workflow then filters the email to confirm it contains binary data (attachments). If attachments are present, it proceeds to retrieve the full email details, including all binary data. A crucial step is the creation of a new Google Drive folder. This folder is dynamically named using the email's subject and the current timestamp, for example, "[Email Subject] - [Current Timestamp]". Following this, the workflow separates each individual attachment from the email. Finally, these attachments are uploaded into the newly created Google Drive folder, with their original filenames preserved. The overall purpose of this workflow is to automatically organize and store email attachments into a structured Google Drive folder system. This workflow is compatible with any type of binary data found in an email, as the filter is designed to detect any binary data, not just PDFs. How It Works Trigger: The workflow initiates when a new email arrives in a specified Gmail account. Alternatively, it can be configured with a similar email trigger. Download Attachments: The workflow is set to automatically download any binary attachments from the incoming email. Filter Attachments: The workflow then filters the email to confirm it contains binary data (attachments). Retrieve Full Email Details: If attachments are present, the workflow proceeds to retrieve the complete details of the email, including all binary data. Create Google Drive Folder: A new folder is created in Google Drive. This folder is dynamically named using the email's subject and the current timestamp (e.g., "[Email Subject] - [Current Timestamp]"). Split Out Attachments: Each individual binary attachment from the email is separated into its own item within the workflow. Upload to Google Drive: Finally, these separated attachments are uploaded into the newly created Google Drive folder, retaining their original filenames. Need Help? Have Questions? For consulting and support, or if you have questions, please feel free to connect with me on LinkedIn or email michael.gullo@outlook.com.

Vcollos Trefilio AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the vcollos/trefilio model. This powerful AI model can generate high-quality other 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. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image 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 other content Access the generated output from the final node API Reference Model: vcollos/trefilio API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters