Generate Images from Text with IBM Granite Vision 3.3 2B AI Model
Generate Images from Text with IBM Granite Vision 3.3 2B AI Model 🌍 Overview This workflow uses the ibm-granite/granite-vision-3.3-2b model (hosted on Replicate) to generate AI images. It starts manually, sends a request to the Replicate API, waits for the result, and finally outputs the generated image link. Think of it as your AI art assistant — you click once, and it handles the full request/response cycle for image generation. 🟢 Section 1: Trigger & API Setup 🔗 Nodes: Manual Trigger* → Starts when you click *Execute. Set API Key** → Stores your Replicate API Key safely in the workflow. 💡 Beginner takeaway: This section is like turning the key in the ignition. You start the workflow, and it loads your credentials so you can talk to Replicate’s API. 📈 Advantage: Keeps your API key stored inside the workflow instead of hard-coding it everywhere. 🟦 Section 2: Create Prediction 🔗 Nodes: HTTP Request (Create Prediction)** → Sends a request to Replicate with the chosen model (granite-vision-3.3-2b) and input parameters (seed, temperature, max\_tokens, etc.). 💡 Beginner takeaway: This is where the workflow actually asks the AI model to generate an image. 📈 Advantage: You can tweak parameters like creativity (temperature) or randomness (seed) to control results. 🟣 Section 3: Polling & Status Check 🔗 Nodes: Extract Prediction ID (Code)** → Saves the unique job ID. Wait (2s)** → Pauses before checking status. Check Prediction Status (HTTP Request)** → Calls Replicate to see if the image is ready. If Condition (Check If Complete)** → ✅ If status = succeeded → move to result 🔄 Else → go back to Wait and check again 💡 Beginner takeaway: Since image generation takes a few seconds, this section keeps asking the AI “are you done yet?” until the image is ready. 📈 Advantage: No need to guess — the workflow waits automatically and retries until success. 🔵 Section 4: Process Result 🔗 Nodes: Process Result (Code)** → Extracts the final data: ✅ Status ✅ Output image URL ✅ Metrics (time taken, etc.) ✅ Model info 💡 Beginner takeaway: This section collects the finished image link and prepares it neatly for you. 📈 Advantage: You get structured output that you can save, display, or use in another workflow (like auto-sending images to Slack or saving to Google Drive). 📊 Final Overview Table | Section | Nodes | Purpose | Benefit | | -------------------- | ---------------------------------- | --------------------------- | --------------------------- | | 🟢 Trigger & Setup | Manual Trigger, Set API Key | Start + load credentials | Secure API key management | | 🟦 Create Prediction | HTTP Request | Ask AI to generate image | Control creativity & output | | 🟣 Polling | Extract ID, Wait, Check Status, If | Repeatedly check job status | Auto-wait until done | | 🔵 Process Result | Process Result | Extract image + details | Get clean output for reuse | 🚀 Why This Workflow is Useful Automates full API cycle** → From request to final image URL Handles delays automatically** → Keeps checking until your image is ready Customizable parameters** → Adjust creativity, randomness, and token limits Reusable** → Connect it to email, Slack, Notion, or storage for instant sharing Beginner-friendly* → Just plug in your API key and hit *Execute. An n8n automation workflow template by Yaron Been.
Yaron Been
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