TL;DR; Experience the quality of Decor8 AI by signing up for the leading API for developing your AI-powered interior design app with support for virtual staging. For integration assistance, feel free to contact me at akhilesh@immex.tech or reach the team at decor8@immex.tech.
Welcome to this in-depth guide for developers and operators exploring how you can use the latest AI models to build interior design and virtual staging apps. If you have been following up news in this area, you might have already seen the success of interiorai.com and if you're wondering if there is an opportunity to build such app, you have found the right place.
Creating a high-quality, AI-powered virtual staging and interior design solution from scratch is an immense challenge. Beyond generating aesthetically pleasing results, the process involves maintaining spatial proportions, realism, and practicality, while ensuring performance and scalability.
This blog post is about Decor8 AI's API for Virtual Staging & Interior Design where I'll try to convince you why you should consider Decor8 AI Platform for building interior design and virtual staging features in your app. Decor8 AI offers a robust API that solves these challenges, enabling developers to seamlessly integrate virtual staging and interior design features into their applications. By providing both an SDK and a REST API, Decor8 AI simplifies complex tasks, allowing developers to focus on delivering enhanced, engaging user experiences. Developers can focus on the distribution of the app (Marketing and Sales) while Decor8 AI can keep the quality high. Consider Decor8 AI as your engineering / development partner.
Now, let’s jump into the unique challenges of building AI-powered solutions and why Decor8 AI’s API is the optimal choice for developers.
The Challenges of Building AI Interior Design Solutions:
I've compiled this list based on our own experience building Decor8 AI and while I don't claim Decor8 AI has solved all problems, but we have solved most of them to varying degree of success. The proof is in the quality of the generated designs.
So, here are some challenges that I faced when building Decor8 AI (as well as AI Landscape Design Features of DreamzAR App)
1. Handling Input Image Quality:
Variety of input image formats: In offering users the ability to upload room photos and receive AI-generated designs, the mobile phone is an ideal tool due to its convenience. However, different phones produce different formats—iPhones, for example, use .HEIC/HEIF in addition to PNG or JPG. Since AI models are often trained using PNG images, input formats need to be converted to PNG before processing.
Poor Lighting and Dark Rooms: Generative AI relies heavily on the quality of input images. Poor lighting or dark environments reduce pixel clarity, making it harder for AI to interpret the image accurately. Preprocessing, such as improving brightness or contrast, helps ensure the AI can generate the desired output, making proper image preparation essential before using the AI model.
2. Preserving Room Layout and Proportions:
One of the key challenges in AI-driven interior design is maintaining the integrity of room layouts. AI models are trained on diverse images from various sources—captured at different angles, with varying camera settings. This leads to inconsistencies in object sizes, such as windows or furniture, even if the room dimensions are similar. Since AI works with pixels, it may bias towards common settings, risking inaccurate proportions in uploaded images.
To avoid issues like walls or windows being added or altered incorrectly, it’s crucial to ensure that the AI understands the room's real dimensions before generating designs. AI may mistakenly manipulate layouts based on its training data, creating unrealistic modifications. For example, imagine prospective buyers visiting a property that doesn’t match the virtually staged images—this breaks trust.
To control this, developers must guide the AI carefully using positive and negative prompts, scheduling, and prompt strength. This fine-tuning ensures the AI respects the existing room layout, requiring iterative testing to find the optimal settings for virtual staging.
3. Realism in Generated Designs:
For virtual staging to be effective, realism is non-negotiable. A design must accurately reflect the textures of materials, the interplay of light and shadow, and how furniture interacts within the space. Without lifelike details, the entire experience falls apart, failing to convince users that the space is livable or marketable.
Decor8 AI excels at delivering this realism. With the combination of image processing (pre- and post-image generation) ensure that textures—from hardwood floors to fabric upholstery—are rendered accurately, with precise lighting and spatial arrangements. Unlike other virtual staging AI solutions, Decor8 AI produces photorealistic results that elevate user engagement and trust. This realism extends to fine details like shadows, material reflections, and appropriate scaling, setting Decor8 AI apart as a superior tool for realistic interior design generation. Its focus on practical, usable layouts also ensures that the AI-generated spaces are not only beautiful but functional, making it a standout solution in a crowded field of virtual staging tools.
4. Design Practicality:
If you look at how generative AI really works, it won't surprise you to see weird artifacts in the generated images e.g.. Half or broken furniture or pieces of furniture attached to other furniture or protruding from the walls. This creates a problem as your users are not expecting anything like this and if you want to retain their trust you want something that's better or at least at par with what online interior design services (backed by human designers) will produce. Otherwise, it defeats the purpose completely.
With a combination of properly setup image masks, text prompts and AI model tunable parameters (and sometimes, fine-tuning with the most relevant data) one can active the most realistic, actionable designs - which can pass the reality test when you use Google Lens to visually search for the furniture appearing in the generated designs. Decor8 AI has achieved this level of realism in its output images.
5. Resolution of the Generated Images :
Generating every pixel of an image means that higher resolutions take longer to process, but more pixels are necessary for detailed designs. This creates a trade-off between image quality and generation speed, especially for real-time use cases. While AI models are evolving, current limitations still require balance.
Decor8 AI optimizes this by applying smart resizing, downscaling, and upscaling during its image generation process. This allows the AI to work efficiently with smaller images while still delivering high-quality, detailed results in a timely manner.
6. Choosing the Right AI Model:
There are numerous AI models. DALL-E, Stable Diffusion, Google's Gemini, Black Forest Lab's Flux and many more. Each model has unique quirks, trained on different datasets and varying image sizes. What works for one often doesn’t apply to another. This makes finding the right model for interior design a process of trial and error, which can be frustrating as a developer. The non-deterministic nature of these models provides little insight into how decisions are made.
At Decor8 AI, we've tested several models, manually reviewing outputs for quality. Though not scalable, this approach ensures we consistently deliver top-tier results.
7. Training and Retesting the AI Model:
As API developers, we don’t build the foundation models (which can cost millions) but fine-tune them with data specific to our domain, like room images and furniture styles. This process doesn’t stop at initial training—continuous retesting and retraining are needed to maintain and improve model performance.
How do you ensure seamless user experience while making these updates? By using the API. The API abstracts complexity and provides a consistent interface to your app. Decor8 AI handles these backend complexities, saving your time and resources.
8. Keeping the Model Updated:
Once you have chosen the AI model, and have put it in the production via services like replicate.com you can relax and see it doing its magic until.. you learn a new version of the model has dropped or entirely new foundation models has been made available. As a developer of the app, you have a desire to try out new model as you saw the hype around the new model on x.com
While its very exciting to try out new AI models regularly to keep up with new design trends, user preferences, and technological advancements, it does require the developers to constantly monitor and update the usage of the models by testing a variety of parameters that worked (after a lot of trial and error) with prior AI model and you have to repeat it again. this is very time consuming, and at times frustrating since generative ai models are non-deterministic by design. what worked with previous model doesnt necessarily work as is with new versions.
9. Scalability and Cost Efficiency with GPU Workloads:
Generative AI models rely heavily on GPUs for faster execution, but GPUs are costly and need to be utilized effectively. This becomes critical when your app’s workload fluctuates as it scales. Pay-per-use APIs like Decor8 AI allow you to only pay for what you use, making it easier to grow your app without upfront infrastructure costs. As your app and its demand grow, the API scales with you.
Decor8 AI’s infrastructure is designed to handle increased traffic, complex designs, and fast response times, so developers can focus on growth without backend concerns.
10. Security of Data and Models:
Security is a major concern when developing AI-powered solutions, especially when handling sensitive user data and proprietary models. Protecting API access, user information, and the integrity of design models is critical.
Decor8 AI API provides an API Key for secure access and you can rotate (change) the API Key as per your requirements. Decor8 AI processes all the input data over https and stores intermediate and output data in secure cloud storage.
Wrapping up
I'm hoping I have convinced you to explore Decor8 API if you're building AI powered interior design and virtual staging app. To make it easy to integrate we have created a set of tools for you.
Access to Decor8 AI API and Documentation:
SDK
Developers can access the Decor8 AI SDK on GitHub, where you'll find SDKs for Python, JavaScript, and Dart/Flutter.API
Building an interior design app for mobile or desktop or for the web is easier than ever.
API Documentation
You can download OpenAPI spec and using open source tools build a client for your choice of programming language.
Visit: https://api-docs.decor8.ai/
No-Code or Low-Code support
Experience the quality of Decor8 AI by signing up for the leading API for developing your AI-powered interior design app with support for virtual staging. For integration assistance, feel free to contact me at akhilesh@immex.tech or reach the team at decor8@immex.tech.
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