Open vs. Closed: The Two Sides of AI Image Models

I’ve always been fascinated by the idea of creating something—not motivated alone by the thought of creating a masterpiece (reaching the end goal), but also excited about the feeling of fully immersing and dedicating myself to something meaningful (enjoying the journey of reaching that end goal). That’s one reason why my foray into art using AI quickly turned into an obsession. I wasn’t just intrigued by what these tools could create; I was also captivated by how they worked.

And so, the deeper I went, the more I noticed a divide—two distinct approaches shaping this technology. One invited exploration, while the other guarded its secrets.

The open source models like Stable Diffusion and Flux are available to the public, letting developers, researchers, and even hobbyists peek inside, tweak things, and experiment freely, while the closed-source models, like Midjourney, are more like a pre-made dessert—delicious, but the real ingredient behind their sweetness locked away from the prying eyes.

Open-source models let users download them, experiment with different datasets, and even run them offline on their local machine. For someone like me who’s obsessed with AI and creativity, open source feels like freedom—the freedom to learn, to create, and to contribute.

I understand that AI developers/ machine learning experts spend a lot of time and money to work upon polishing their creations, and at some point, they need to generate profits for their investors, feed their families, and make business. However, all that does not diminish the fact that there’s something deeply exciting about the open-source AI models!

One, they’re free! And two, they’re democratic in spirit—built to be shared, adapted, and improved by anyone with the curiosity to learn. And the open source community is something entirely amazing altogether, holding its own even higher up on the pedestal than the AI itself! Models like Stable Diffusion allow such artists and developers from the community to shape the tool to fit their visions. I’ve seen people use it to create things vastly different: from surreal fantasy worlds to real-world educational content! For the open source community, it’s not just a tool; it’s a playground! And that’s the beauty of open source: the community shows up to enjoy all of it— together!

But as freeing as open-source tools are, there’s a reason closed-source models like Midjourney still attract so many people. For one, they’re fabulously polished and come wrapped with wonderful, user-friendly GUIs! You don’t need to know anything about programming or machine learning to use them. You just describe what you want, and the AI handles the rest—no setup, no coding, no fuss. That works for most people, and I get that! Who needs additional complexity in their lives? Just pay them and enjoy the fruits of a polished, end-product, without getting into the meat of things, and frankly, not everyone needs to!

Closed-source models also tend to focus heavily on quality control. Midjourney, for example, is known for producing stunningly polished results that often look better straight out of the box compared to open-source alternatives (however, that seems to be changing really fast and the open-source models seem to be catching up). For many, the trade-off is worth it. They’re not looking to tinker; they just want something that works.

But this is where the debate gets interesting—because while closed-source models prioritize simplicity and refinement, they also limit freedom. You’re using someone else’s tool, on their terms, and almost always paying for the privilege. With open-source models, you might have to climb a steeper learning curve, but you gain independence. You’re not tied to any one company’s vision of how AI should work. Instead, you get to shape it yourself.

So, which one’s better? That depends on what you need. If you’re someone who just wants quick results, closed-source models might feel like a dream come true. But if you’re someone who likes to dig deeper, explore possibilities, and customize tools, open source might be your perfect match. You’ll find people out there who’ll only swear by one of them while criticizing the other (Fun fact: As open as the open source community sounds, there are places such as some sub-reddits where your posts will get deleted if you choose to talk about anything closed source).

And then there are hybrid people like I: who dabble in both, as per their needs, tracing leaps in various technologies across the board. However, the open-source models still hold a special place in my heart. They’re raw and flexible, like clay waiting to be moulded. Merging trained models is by far the most creative thing I’ve done in my life after a long, long time and the results have been fabulous; all thanks to open source. For me, it matters that they invite curiosity, experimentation, and—most importantly—learning. That’s why I spend so much time demystifying AI through my work. I want people to know that AI isn’t this mysterious black box. It’s something we can all understand, explore, and even shape.

That’s also the driving force behind my #demystifyai posts on Instagram. I want to take these complex topics—like faceswaps vs. AI models, open vs. closed-source models, etc.—and make them accessible to everyone. Whether you’re someone who’s curious about AI or someone who’s nervous about it, my goal is to help you feel like you belong in this conversation.

So, if you’ve ever felt intimidated by AI, I hope this article helps you see that it’s not as scary as it seems. Whether you prefer the freedom of open-source tools or the polish of closed-source ones, there’s room for everyone in this space. And if you want to keep learning, exploring, and demystifying AI with me, you know where to find me—on Instagram, sharing more #demystifyai posts to help us all understand this fascinating world a little better.