How to Make AI Generated Images?

Why Everyone Is Making AI Images (And Why You're Not Yet)

Somewhere in the last few years, AI images stopped being a novelty and quietly became part of the internet's furniture. Since 2022, more than 15 billion AI-generated images have been created, with roughly 34 million new ones appearing every single day, according to market research from market.us. By late 2024, an estimated 71% of images shared on social media globally were AI-generated.

This isn't just hobbyists playing with portraits. Adoption has gone mainstream and professional at the same time: 62% of marketers now use generative AI for image assets, 83% of creative professionals use generative AI in some form, and 89% of small businesses rely on AI tools for daily operations, per the same market analysis. Enterprise use dominates over personal use — the professional segment holds more than 74% of the market. When Stanford's Samuel Goldberg describes the shift as a transition "between human-centered production and a more generative-AI-centered production," he's describing a change that has already reached your competitors.

If the numbers say everyone is doing this, the honest question is why you aren't. In practice, three barriers stop most people at the first search result:

• Too many tools. DALL-E 3, Midjourney, Flux, Stable Diffusion, Adobe Firefly, Microsoft Designer — and each one works completely differently.
• Unclear pricing. Plans range from free tiers to $60/month, with credits, GPU hours, and usage caps that make comparison genuinely hard.
• Confusing ownership rules. Some providers grant you full rights to your images; others require expensive plans before a business can legally own what it generates.

That last point deserves emphasis. Hands-on testing by CMSWire found that Midjourney requires companies with over $1 million in annual revenue to buy Pro or Mega plans before they own their generated assets, while OpenAI grants ownership outright — and the legal landscape around AI images "is constantly changing." Picking the wrong tool can mean paying for images you can't safely use in branded content.

None of these barriers is actually hard to clear. The core process is identical across every tool: you type a descriptive prompt, a trained neural network generates an image, and you refine. The real skill is matching the tool to your situation — conversational tools like DALL-E 3 for beginners, parameter-driven tools like Midjourney for marketers, developer-oriented tools like Stable Diffusion for coders.

And if you want to go beyond one-off generation, agent skills can automate the surrounding workflow — sourcing, prompting, and publishing. SkillzBench is building a free directory with security scanning, benchmarking, and a free MCP connector to help you find and test those skills safely. Pick your tool, write your first prompt, and you can have a usable image in under a minute today.

How AI Image Generation Actually Works (In Plain English)

You don't need a computer science degree to make AI images, but understanding a little of what's happening under the hood will make you dramatically better at prompting. Roughly 34 million new images are now generated every day, and nearly all of them come from one of two core technologies.

Diffusion models are the engine behind most modern tools, including DALL-E 3 and Stable Diffusion. They start with pure random noise — essentially TV static — and gradually refine it step by step until a coherent picture emerges. During training, the model learned this trick by having noise added to billions of image-and-caption pairs, then reversing the process. That's how it learned to connect your words to visual patterns.

GANs (Generative Adversarial Networks) take a different approach: two networks compete. One generates images; the other judges whether they look real, and the generator keeps improving until it can fool its critic. This rivalry has pushed quality so far that AI images are now nearly indistinguishable from real photos, and GANs still lead technology usage at 37.4% of the market.

Why does training data matter? Because the model can only draw from what it has seen. The more varied and representative the dataset, the more diverse and accurate your outputs; poor or biased datasets limit what the model can produce. A model trained mostly on certain styles or subjects will struggle outside that comfort zone.

Here's the practical payoff for you as a prompt writer:

• The model converts text into visual patterns, so detailed prompts beat vague ones — adding specifics like "realistic looking" measurably improved accuracy in hands-on tests.
• Style, lighting, and composition words ("cinematic lighting," "wide-angle shot") give the model concrete patterns to aim for.
• Iterating works: in one comparative test, Stable Diffusion went from 1 of 4 correct images to 3 of 4 after a refined prompt.

Think of prompting as steering a very fast, very literal artist. It won't guess your intent — it responds to what you actually describe. The same principle applies when you hand tasks to AI agents: the quality of the instructions determines the quality of the output. That's why instruction packages and agent skills are worth vetting carefully — SkillzBench is building a free directory with security scanning and benchmarking so you can see which ones genuinely lift performance before you rely on them.

With that mental model in place, the hands-on part gets much easier.

Choosing Your Tool: DALL-E 3, Midjourney, Stable Diffusion, and More

The right AI image generator depends less on which one makes the "best" art and more on which one matches how you work. Hands-on testing of six major tools shows clear patterns: beginners thrive with conversational interfaces, marketers get the most from parameter-driven control, and developers want direct API access.

If you're non-technical, start with DALL-E 3 via ChatGPT. You describe what you want, then refine it through plain back-and-forth conversation until the image works. It's free through Microsoft Bing Chat, or $20/month via ChatGPT Plus, which allows 40 prompts per 3 hours. The main weakness: it struggles with rendering text inside images, so botched logo taglines are common. Microsoft Designer offers a similar path — it's essentially a different interface for the same OpenAI model, with 15 free credits per Microsoft account.

For marketers, CMSWire's hands-on comparison named Midjourney its number one pick, praising photorealistic output suited to email, web pages, and social content. Version 6, released in February 2024, focused specifically on photorealism and lighting accuracy. You control results through parameters like --ar 16:9 for aspect ratio and --stylize 1000 for a more artistic look. It generates four images in roughly 50 seconds, with basic plans starting around $10/month.

Developers should look at Stable Diffusion. It's free and open source for individuals, requires an API key plus a Google Colab or Hugging Face setup, and was the fastest tool in testing — under 10 seconds per image — while producing some of the most realistic results.

Other options worth knowing:

• Flux — fast and simple, but no conversational refinement; free-tier generation takes 1–2+ minutes, with paid plans from $9.99/month.
• Adobe Firefly — stands out for editing features like adding text and shapes, built for users without graphic design skills; $4.99/month.
• Microsoft Designer — a free-ish entry point for DALL-E results through a Microsoft account.

Before committing, check image ownership terms, because they differ sharply. OpenAI grants users outright ownership — you can reprint, sell, or merchandise images without permission. Midjourney users own their images too, but companies with more than $1M in gross annual revenue must buy Pro or Mega plans to own assets. Stable Diffusion claims no rights on what you generate, though AI-image copyright varies by jurisdiction, so verify local laws before using images in branded content.

For developer readers, Stable Diffusion's API-and-Colab setup is exactly the kind of workflow that benefits from automation — agent skills can chain image generation into broader content pipelines, and you can browse and test free ones on SkillzBench, which is building a free skills directory with security scanning and benchmarking.

Prompting Like a Pro: The Formula That Works Across Every Tool

The difference between a mediocre AI image and a great one usually comes down to a single variable: how you write the prompt. Hands-on testing across six major tools shows that the same core formula works everywhere, whether you're using DALL-E 3, Midjourney, Flux, Stable Diffusion, Adobe Firefly, or Microsoft Designer.

The formula is simple: subject + style descriptor + lighting + composition. Style descriptors like "hyper-realistic," "oil painting," "cyberpunk," or "watercolor" tell the model what aesthetic to aim for. Lighting phrases such as "cinematic lighting" or "sunset backlight" shape mood and depth, while composition cues like "wide-angle shot" or "macro shot" control framing. A hands-on review of six AI image generators found that layering these elements works across every tool, even though each platform interprets them slightly differently.

Detail pays off measurably. In identical test prompts, adding specificity — for example, starting with "A realistic looking image of..." — significantly improved accuracy for both DALL-E and Stable Diffusion, according to CMSWire's comparative testing. The numbers prove iteration matters: when given the same prompt (an alien planet with two moons), Midjourney got 1 of 4 elements correct initially and DALL-E 3 got 3 of 4. Stable Diffusion started at 1 of 4 — but with a refined prompt, it jumped to 3 of 4. That improvement didn't come from switching tools; it came from rewriting the prompt.

A few practical habits will sharpen your results:

• Be specific and descriptive rather than vague — name the subject, action, and setting explicitly.
• Experiment with parameters where available, such as Midjourney's –ar 16:9 for aspect ratio or –stylize 1000 for a more artistic look.
• Refine iteratively: generate, examine what the model missed, and adjust the prompt rather than starting over.
• Practice regularly — prompt crafting is a skill that improves with repetition.

Two cautions deserve attention before you put generated images to work. First, text inside images remains a known weak point — DALL-E 3, for instance, has been observed botching logo taglines and rendered words, so keep in-image text minimal or add it afterward in an editor. Second, legal treatment of AI-generated images is unsettled: copyright rules vary by jurisdiction, and even tool-specific terms differ (Stable Diffusion claims no rights on outputs, while Midjourney requires Pro or Mega plans for companies with more than $1M in annual revenue to own their assets). As CMSWire's guidance puts it, laws are constantly changing — check the rules in your specific country and state before using images in branded content.

For teams building image generation into larger workflows, prompt patterns like these can be packaged into repeatable instructions for AI agents. SkillzBench is building a free skills directory and MCP connector that will let you browse, test, and connect vetted skills to your agents — worth a look as your prompting matures into automation.

Go Further: Automating Your Image Workflow with Agent Skills

One image is easy. Thirty on-brand images a week is a job — and that's where automation starts to matter. With roughly 34 million new AI images generated daily, according to market research, the teams winning at visual content aren't prompting harder; they're wiring image generation into their workflows.

For developers, the entry point is already familiar. Stable Diffusion is aimed squarely at coders: it requires an API key and a Google Colab or Hugging Face setup, and it was the fastest generator in hands-on testing, producing images in under 10 seconds. That API-first design makes it a natural building block for scripted pipelines — batch-generating product shots, resizing for social formats, or feeding images into downstream publishing steps without touching a UI.

This is exactly where agent skills come in. Skills are instruction packages (SKILL.md files) that agents like Claude Code, Codex, Cursor, and Gemini CLI can use to perform multi-step tasks — including the kind of image-generation and content-pipeline work described above. The catch? Skills are everywhere, but quality is not. Published research found that roughly 26% of skills carry at least one vulnerability, so grabbing the first skill you find on GitHub is a genuine risk.

That's the problem SkillzBench (skillzbench.com) is being built to solve. It's a free, independent directory of AI agent skills with a few core pillars:

• Discover and verify: skills are continuously crawled and deduped from GitHub, with every version pinned to a commit SHA, license-checked, and security-scanned.
• Benchmark in a sandbox: each skill is tested with the same model, with and without the skill, so you can see its Lift in percentage points before you trust it.
• Equip per agent: one free MCP connector lets you toggle exactly which skills and tools each agent can see — nothing more.
• Stay current: upstream changes trigger re-scans, re-benchmarks, and alerts.

Everything on the platform — the directory, the MCP connector, benchmarks, and alerts — is free, funded by blog advertising rather than user fees, and money never touches scores. Each skill carries a 0–100 trust score built from security, quality, license, maintenance, and community signals, with Lift always shown separately.

If you're ready to move from one-off prompts to an automated image workflow, join the SkillzBench waitlist to be first in line when the directory and MCP connector go live.