From Idea to Implementation: Lessons in Generative AI while Building Decor8AI

Generative AI and Me!

Generative AI is like a creative partner that steps in where human imagination might hit a wall. Whether you’re crafting a compelling article with ChatGPT.com , composing a catchy tune with Suno.ai , or creating stunning artwork with Midjourney.com or Stable Diffusion or Flux-1, AI has become an invaluable tool in the creative process. As someone on the internet joked—AI (robot) was supposed to do my dishes while I learned to paint, but here I am still doing the dishes while AI happily creates masterpieces.

Jokes aside, it’s not about replacing experts or creatives; it’s about equipping everyone with tools that save time and money while pushing the boundaries of what's possible. When expert creators use these tools, they can significantly advance their art, leveraging AI to explore new frontiers and enhance their creative output.

AI models learn from vast amounts of data, absorbing the styles and techniques of countless creators before them. The magic happens when these models blend what they’ve seen to generate something novel—an article, an image, a piece of music—that feels fresh and innovative. The results can be so impressive that we sometimes forget the AI is simply remixing and reimagining existing elements.

The Reality of Making AI Work

If you’ve been following the AI boom, you’ve likely seen the dazzling demos and success stories that make AI models look like they can do anything with just a simple API call. I’ve been there, too—enthusiastic and eager to see if AI could solve my own challenges. But while these models can produce remarkable results, making them work consistently, especially for specific use cases like interior design, is far from easy.

Off-the-shelf AI models can be incredibly tempting—after all, they promise quick results and a fast track to integrating AI into your projects. But, as anyone who’s ventured down this path knows, these models come with their quirks. They often feel like a black box, where you input your carefully crafted prompt and hope the model spits out something close to what you imagined. More often than not, though, you’ll find yourself tweaking, adjusting, and experimenting endlessly to get even a passable result. And if you’re aiming for something truly remarkable, something that stands out in a crowded market? Well, that’s where the real challenge begins. It’s not just about coaxing a good output—it’s about fine-tuning every aspect to achieve something extraordinary, a process that can be frustratingly elusive.

Moreover, the barrier to entry in this space is surprisingly low. While only a few have the expertise and resources to build entirely new models, many more are using existing models with little to no modification or with only incremental training. With some coding knowledge and the right tools, almost anyone can cobble together a basic product and launch it. This accessibility is both a blessing and a curse. The ease of entry has led to a crowded market, with countless competitors vying for attention with their AI-powered creations. However, while launching a product might be easy, creating something that truly resonates with users and consistently delivers high-quality results is a different challenge altogether. It’s in that gap—between “good enough” and truly exceptional—where the real work, and real value, lie. In a landscape where everyone can throw something together, the true differentiator is how well you can push beyond the basics to create something that not only works but delights.

The GPU Struggle: When Magic Comes at a Cost

Once you’ve tailored an AI model to your needs, the next challenge is running it cost-effectively. Dedicated GPUs are powerful but expensive, creating a vicious cycle: no customers means no revenue, and no revenue means you can’t afford the GPUs.

Note: I’ve bootstrapped my startup with no outside capital, which makes this issue particularly challenging for me. Your situation might be different.

The pay-as-you-go option seems cheaper, but it has its own issues. If you don’t have enough customers to keep the model busy, it gets offloaded from the GPU to save costs. When a request finally comes in, the model takes a long time to boot (as it has to download its checkpoint files and load them into Memory/GPU), leading to slow response times. This delay frustrates users, potentially driving them away, and leaves you stuck in a cycle of underperformance and lost opportunities.

So, How Did I Get Generative AI into My Apps?

Like many other founders/entrepreneurs—I’ve been navigating these challenges to integrate Generative AI into my products. With DreamzAR for landscape design and Decor8 AI for interior design , I’ve used models like ChatGPT and Stable Diffusion, constantly experimenting to get them to align with my vision. Sometimes they do what I want, sometimes they don’t, and often, I’m left scratching my head, wondering why.

If you’ve tried using open-source text-to-image models for your projects, you know the struggles:

1. Non-deterministic Outputs: You never know exactly what you’ll get.
2. Quality Control Issues: Changing a prompt can make the result better—or worse.
3. Maintenance Headaches: New model versions don’t always mean better results. Often, everything breaks, and you’re left starting from scratch.

As a trained software engineer, this unpredictability can drive you nuts. But I’ve persisted, iterating through different model versions, tweaking parameters, and trying to squeeze the best performance out of each one. It’s an exhausting process, but necessary if you want to build something that truly works, and that people will actually use.

Challenge Accepted!

Keeping GPU costs manageable while preserving the promised "near real-time" customer experience is the prime challenge. My products are consumer-facing, and users don’t have much time to spare. If a new design doesn’t show up within the next few seconds, they simply ignore it and move on. I’ve spent countless hours on the following:

1. Visually Building AI Processing Pipelines with ComfyUI: This is something I learned later in the process, but it’s been an eye-opener. ComfyUI allows you to mix and match various steps and iterate quickly. You gain a deep understanding of the inner workings of AI pipelines by swapping out certain steps with alternatives, or by adding and removing steps to compare performance and quality trade-offs. Finished workflows can be exported as JSON files and run on a ComfyUI server, which you can host on platforms like runpod.io or runcomfy.com (and a few others).
2. Reducing Model Prediction Time: This involves tweaking the step count, prompt, and prompt strength, and manually observing the outputs after each change.
3. Concurrency: If there are steps in the pipeline that can be performed in parallel, execute them in parallel. If the user is waiting for results in the interface, show necessary progress indicators and use webhooks to make the first result available as soon as it comes out of the model.
4. Keeping Round-Trip Time Low: Reduce image sizes on the client side before they even reach the server. Remember, all diffusion models take a long time if they have to work with a large number of pixels. Applying downsizing, downsampling, and later upscaling can help achieve similar results with a shorter round-trip time.
5. Choosing the Right API Provider: I’ve tested both models—renting dedicated GPUs and using pay-as-you-go models—with multiple providers. I’ve chosen replicate.com as my trusted partner for my AI model needs; they’ve been incredibly supportive in my AI endeavors. I’ve also tried runpod.io 's serverless endpoints (pay-as-you-go) as well as PODs (dedicated GPU) for running my models. There are price and performance trade-offs to consider, but all of these options are viable depending on your situation.

When Do You Stop Experimenting?

Here’s the thing: you don’t. You keep an eye out for new models, keep experimenting, keep comparing results. I’ve started building a visual regression test suite to help me figure out whether a new model version is actually better or just different. Writing automated tests for generative AI is like herding cats, but it’s essential. And hey, there might be an enterprise product idea here—everyone could use a reliable test suite for generative AI, right?

Why Am I Telling You This?

Because I’ve been through the wringer, and I’ve come out the other side with something I believe in: a platform for interior design powered by AI that actually works. I’ve built apps for iPhone, Android, and the web to bring these services to users. But I didn’t stop there—I created an API so others can use the same AI magic in their apps. Further, I have added No-Code Bubble Plugin for Interior Design and Virtual Staging for even faster integration with your Bubble app.

Let's be clear - I want to sell you the API.

I know, I know—the apps built using this API might end up being my competitors. And you know what? I’m okay with that. As I said earlier, distribution is king. I’d be thrilled if my work reached millions of users through your app’s stellar distribution, while I focus on perfecting the AI model for interior design. Everyone wins—you, your customers, and me.

Ready to Give It a Try?

If you’re interested, let’s connect. I’m more than happy to help you integrate the API into your app or platform. Reach out at [email protected] or visit Decor8 AI Interior Design & Virtual Staging API page or get started with API docs .