Challenge 5 - Interior Design with GenAI

<p><strong>Challenge 5:</strong> Interior Design with GenAI</p><p><strong>Level:</strong> Medium</p><p><strong>Description:&nbsp;</strong>Emma is an interior designer recently hired to help a new homeowner enhance the aesthetics of several rooms in her house—including spaces like the private office, kitchen, bedroom, bathroom, and garden. However, to kick off the project, Emma is asked to focus on just one room of her choice. The client, having just purchased the property, is currently working with a limited budget. She wants to avoid expensive renovations or purchasing new furniture. Instead, her goal is to refresh the space using what she already has—relying on changes in decoration, style, accessories, or layout to improve the room’s functionality and visual appeal. Emma’s task is to produce multiple photorealistic visualizations showing how the selected room could look after a restyle. Rather than spending time on traditional mood boards or mockups, she’s seeking a faster, more data-driven approach—specifically by using AI and the generative capabilities of vision models.</p><p>To help Emma, you decide to build a workflow that relies on a powerful image generation and editing model. Start by selecting a room (e.g., private office) and defining its existing essential furniture pieces (e.g., a desk, chair, bookshelf, or lamp), which must remain consistent throughout the process. Then, generate a base image of the room in its current state. From there, use the model to create three stylistically distinct reinterpretations—e.g., as minimalist, industrial, and bohemian—by modifying only the decorative elements, accessories, or layout. Finally, save the resulting images and display them side by side in a table for easy comparison. Can you design a solution that loops through various styles and prompts the model to produce compelling visual proposals Emma’s client will love?</p><ul><li><p><strong>Beginner-friendly objectives: </strong>1. Connect to a vision model of your choice (for example, OpenAI's GPT Image 1 or DALL-3). 2. Type a prompt to generate a base image of the room with the existing essential furniture pieces (you choose which room and which furniture pieces). Tweak the settings of the image generator to your liking. 3. Save the generated image.</p></li><li><p><strong>Intermediate-friendly objectives: </strong>1. Instead of typing the prompt directly in the image generator, define in a table the base room (e.g., “private office”) and a few furniture items (e.g., desk, chair) to compose a parameterized prompt that generates the image of the base room. 2. Edit the image of the base room by prompting the vision model a second time (note: OpenAI's GPT Image 1 supports image editing). Ask the model with a parameterized prompt to create three stylistically distinct reinterpretation of the room (you choose which style) by modifying only the decorative elements, accessories, or layout - not the furniture pieces. 3. Display the edited images and save them.</p></li><li><p><strong>Advanced objectives: </strong>1. Save each edited image immediately after it is generated, rather than waiting until all styles have been processed. Ensure that each image file is named automatically according to the style it represents (e.g., <em>minimalist.png</em>, <em>bohemian.png</em>, <em>industrial.png</em>) for easy identification.</p></li></ul><p><strong>Solution Summary: </strong>To solve this challenge, we leverage OpenAI’s image generation and editing capabilities to automate the creation of photorealistic room redesigns using fixed furniture pieces. The process starts with structured input of room type and essential furniture, followed by generation of a base image. A loop mechanism iterates through predefined styles (e.g., minimalist, industrial, bohemian), to edit the base image only with decorative elements and layout. Each stylized image is saved and visualized side-by-side for comparative inspection. The solution combines parameterized prompt creation, generative AI, and data-driven automation to streamline interior design ideation.</p><p><strong>Solution Details:</strong> We begin our solution by using a Table Creator node to define the room type, list of essential furniture pieces, and the target design styles. To securely connect to the OpenAI API, we configure access using the Credentials Configuration and OpenAI Authenticator nodes. We then process the initial data with a GroupBy node to prepare it for prompt engineering, and use the Expression node to build a parameterized prompt describing the base room. This prompt is injected as a flow variable into the OpenAI Image Generator node, which generates a photorealistic image of the room with its existing furniture. The output image is saved and simultaneously passed as input to a second OpenAI Image Generator node. This node is wrapped within a loop controlled by a Table Row to Variable Loop Start, which iterates over a table of parameterized prompts with predefined styles. For each iteration, we inject a style-specific prompt, requesting the model to edit only decorative elements, layout, and accessories in the input image—while keeping the core furniture unchanged. Edited images are saved immediately after generation using the Image Writer (Table) node within the loop. To ensure that we can dynamically assign filenames based on the style, we configure the Image Writer (Table) node to pick the name from the RowID column. After all iterations complete, we collect the outputs via a Loop End node, transpose the resulting table using a Table Transposer, and display the images side-by-side for comparison using a Table View node.</p>