Skip to content
KNIME Documentation
Main Navigation Get startedBuildWork with KnimeAccount and adminRelease notesDeveloper tools
Community Hub
DevelopersKnime 2.0
Sidebar Navigation

Getting Started

Installation

User Guide

Release Notes

Install Extensions

Building Workflows

Components

Flow Control

Expressions

File Handling

Deployment

Integrated Deployment

Workflow Invocation

Best Practices

Advanced Features

AI Extension

Get Started

Build an Agent

Inject context with RAG

LLM Providers

Tutorials

Summarize product reviews

Product FAQ assistant

Restaurant agent

Your first local agent

Task extractor

Reporting

Kerberos

Extensions

Database

Overview

Tutorials

Read and filter data from a database

Write a transformed dataset to a database

Concepts

Ports and Sessions

Query Generation

Type Mapping

Deprecated Framework

Connecting

Connect: Dedicated Node

Connect: File-Based Database

Connect: Generic DB Connector

Register a JDBC Driver

Install Third-Party Driver Plug-in

Drivers on Hub and Server

Kerberos Constrained Delegation

Reading and Transforming

Read Data

Transform Data Visually

Run a Custom SQL Query

Parameterize a Query

Writing and Modifying

Insert, Update, or Merge

Delete Rows

Bulk Load Data

Create and Remove Tables

Use Transactions

Reference

Connector Node Settings

JDBC Driver Reference

Deprecated Node Mapping

Migrate Deprecated Workflows

Python

Overview

Tutorials

Python Scripting Quickstart

Data App Development Using Plotly

Data Processing

K-AI Scripting Assistant

Data Type Conversion

Batch Processing

Jupyter Integration

Environment Management

Pixi Configuration

Dependency Management

Environment Persistence

Channel Mirrors & Air-Gapped Setups

Conda Migration

Alternative Configurations

Conda and Miniforge Setup

Conda Environment Propagation

Manual Python Configuration

Legacy Node Migration

Concepts

Pixi Backend Architecture

Storage Logic

Python Environments on Executors

Reference

Python Scripting API

Bundled Packages

Executor and Hub Configuration

Troubleshooting Guide

API Reference

Inputs and outputs

Classes

Views

Utility functions

R

Deep Learning

Snowflake

Google Cloud

Azure

AWS

Big Data

Big Data Admin

Big Data Secured Cluster

Databricks

Microsoft Fabric

Power BI

Tableau

Report Designer

H2O

Changelogs

Version 5.12

Version 5.11

Version 5.10

Version 5.9

Version 5.8

Version 5.7

Version 5.6

Version 5.5

Version 5.3

Extensions

Generate or edit images with AI ​

Generate new images or modify existing ones using AI image models with the KNIME AI Extension.

When this is useful ​

Use image generation or editing when you want to:

  • Produce visuals automatically from structured or unstructured data
  • Create illustrations or mockups as part of an automated workflow
  • Include generated images in Data Apps

If you only need text-based output, see Prompt a model instead.

Generate or edit images in KNIME ​

Prerequisites
  • A provider and credentials configured
    (see Providers: Reference table)
  • An image-capable model available via an LLM Selector
  • KNIME AI Extensions installed

Create the image prompt ​

Image prompts are plain text instructions that describe what the model should generate or modify.

You can create prompts dynamically using nodes such as:

  • Table Creator
  • String Manipulation
  • Expression

Example prompt:

txt
Create a realistic illustration of a modern coffee shop interior.

Each input row represents a separate image generation request.

(Optional) Provide an input image ​

To edit an existing image, pass it as input to the image generation node.

  1. Click the plus (+) icon on the node to add an additional table input port.
  2. Connect the table containing the image you want to modify to the newly added table input port of the image generation node.

Generate or edit the image ​

Use the appropriate Image Generator node:

  • OpenAI Image Generator

  • Gemini Image Generator

Each input row produces one or more images as output.

Store or display the result ​

The generated or edited images are returned as image outputs and can be:

  • written to disk
  • displayed in views
  • passed to downstream workflows
  • embedded in reports or applications

Result ​

After execution, the workflow produces one or more images generated or edited according to the provided prompt and inputs.

To see a complete example of image generation in action, check out this workflow on the KNIME Hub:

➡️ Generate product image variants with AI

Next steps ​

Combine image generation with text prompting for richer workflows

Pager
Next pageInstallation