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AI Image Generation | danielball.com

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AI Image Generation | danielball.com

About Me 🧐Portfolio 📚Journey so far 🚲Skills 🛠️Notes 📝

This is my general guide to AI image generation. It covers how the models work at a high level, the different types, a
bit of history, what you actually get from ChatGPT and how to build image generation into your own code and agents.

It's based on my own experiments calling a handful of image providers from Python. I've generated pixel art sprites,
photorealistic card table backgrounds and a series of restaurant posters, trying to make one that doesn't look AI
generated. Every image here came from those experiments.

A short history
Six fingers and gibberish text

How it works
Diffusion models

Are multimodal models diffusion under the hood?

Types of image generation

What you get with ChatGPT

What it costs
You pay for the canvas

Four ways you get billed

The landscape in October 2026

Cost per usable image

Prompting that works
Describe the camera, not the view

Marketing is where AI images get the most criticism

Making a poster that doesn't look AI generated

Prompting rules of thumb

Pixel art
Pixel art is a style, not a format

Many sprites on one canvas

Doing pixel art properly

Where the line falls

Integrating image generation into a coding project
Put a thin interface in front of every provider

Expect the ground to move

Treat generation as a slow, failing job

Do the exact work in code

Test without spending money

Integrating with an agent
Claude doesn't generate images

Three ways to wire it up

How the experiments in this guide were run

Tips for agent-driven generation

Most models start from noise and clean it up. Text steers the clean-up. Newer models also plan the image the way a
chat model plans a sentence.

You pay for the canvas, not the content. A plain wooden table costs the same as a detailed oil painting of the
same size.

ChatGPT is the easiest way in and the hardest to control. Great for one-off images and conversational edits. For
anything repeatable you want the API.

Prices vary more than 100 times over. Anything from a fraction of a cent to 15 cents or more per image. Running a
model locally costs nothing. The cheapest model isn't always the cheapest result.

The APIs move under you. Models get retired with weeks of notice and providers quietly ignore parameters. Keep the
model in config and log what you actually got back.

Describe how it was made, not how it should look. Camera, film, print process and layout get better results than
"stunning" and "hyper-detailed".

Spelling and fingers are no longer the giveaway. The tell now is taste. Too much glow, too much drama and nothing
allowed to be plain.

Agents are good at this. A multimodal agent can write the prompt, call the API, look at the result and try again.
You still need to supply the taste.

The best pipelines are hybrids. Let AI do the creative part. Let ordinary code do the exact parts like sizing,
transparency, palettes and layout.

2014 - GANs. Ian Goodfellow's generative adversarial networks pit two networks
against each other, one making images and one spotting fakes. They go on to produce convincing synthetic faces but are
hard to steer with text.

2015 - DeepDream. Google's
Inceptionism experiments turn image
recognition inside out. The results are full of
psychedelic dog faces,
because the network was trained on a dataset with a lot of dogs in it.

2018 - AI art goes to auction. A blurry GAN portrait called Edmond de Belamy
sells at Christie's for $432,500,
more than 40 times its top estimate.

2019 - Faces that don't exist. NVIDIA's StyleGAN powers
thispersondoesnotexist.com, a new photorealistic fake face on every refresh.

2021 - DALL-E. OpenAI links text and images in one model. You can type a sentence and get a picture, even if it's
a small, blurry one.

2022 - The breakout year. DALL-E 2, Midjourney and
Stable Diffusion all arrive within a few months. Stable
Diffusion is open source and runs on a gaming PC with about 7GB of video memory, which kicks off a huge community of
fine-tunes and tools.

2023 - Photorealism and readable text. A Midjourney image of the
Pope in a white puffer jacket fools much of the
internet. Ideogram launches with text you can
actually read. SDXL raises the bar for open models and DALL-E 3 moves into ChatGPT.

2024 - Flux and the backlash. Former Stable Diffusion researchers found Black Forest Labs and
release Flux, which becomes the default open-weight model. Meanwhile AI
posters become a byword for cutting corners (more on that
below).

2025 - Native image models. OpenAI builds image generation into GPT-4o itself and the "Studio Ghibli" trend takes
over social media. Google's
Gemini 2.5 Flash Image, nicknamed Nano
Banana, makes conversational editing cheap at $0.039 an image.

2026 - Abundance and churn. OpenAI
switches off DALL-E
in its API. OpenRouter alone lists over 60 image models from a dozen companies, and older models are retired every few
months.

Six fingers and gibberish text

For a few years the easiest way to spot an AI image was to count the fingers or try to read the sign in the background.
Both problems are now mostly fixed, and why they happened says a lot about how these models work.

Hands are complicated, flexible and usually partly hidden in photos. Behind a cup, in a pocket, half out of frame.
The models learned what hands look like as a texture, not how many fingers they have.
Midjourney v5 in March 2023 was widely
reported as the turning point. Experts suspected better curated training data with clearer pictures of hands, plus
bigger models and higher resolutions. It wasn't perfect. People in the background still had
alien anatomy for a while.

Text had a more specific cause. Most models read your prompt through a tokeniser that splits it into chunks of words
rather than letters. The model never sees that "chocolate" is spelled c-h-o-c-o-l-a-t-e, so it can't draw it letter by
letter. A 2022 paper, Character-Aware Models Improve Visual Text Rendering, showed
that giving the model access to individual characters fixed much of the problem. Ideogram, whose team included former
Google Imagen researchers, made text a selling point in 2023. By 2025 the multimodal models from OpenAI and Google,
which understand language as well as any chatbot, had made readable text normal.

The fix arrived in stages, and the old problem leaked into the real world in between. The
Willy's Chocolate Experience in Glasgow in February 2024
promised "a pasadise of sweet teats" on its AI-generated posters. Long text is still not completely solved. In December
2025 Nano Banana Pro turned "Lived experience" into
Liver experience on an otherwise good
infographic.

You don't need the maths to use these models well. You do need a rough picture of what's going on, because it explains
most of the surprises.

Most image models are diffusion models. During training they learn to remove noise from images. Take a photo, add a bit
of static and teach the model to predict what the clean version looked like. Repeat billions of times at every level of
noise.

To generate, you run that in reverse. Start from pure static and remove a little noise at a time over a number of steps.
A text encoder turns your prompt into numbers that steer each step towards an image that matches it.

Two details matter in practice.

They work in a compressed space. Latent diffusion, the approach behind Stable
Diffusion, paints on an internal canvas about 8 times smaller on each side than the final image, then decodes it.
That's why you can't ask for a 32 x 32 image. The internal canvas would be 4 x 4.

Steps cost time. More steps is slower and usually better, up to a point. "Distilled" models like Flux Schnell and
SDXL Turbo are trained to get a good image in 4 steps instead of 30.

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