So now it's time to explore one of the coolest parts of AI.
And that's image generation.
So we're going to unpack how these models create images, what types of models are out there.
And by the end, you're going to be able to explain it and create images like a pro.
So what's really going on under the hood here?
Well, in simple terms it's all about pattern recognition.
These AI models are trained on millions of images and different pieces of content that help them to
learn shapes, colors, textures, and different styles.
Now, when you give them a prompt, they tap into that training to create something totally new.
So you can think of text generation like writing a sentence one word at a time.
And image generation, on the other hand, is like painting an entire picture one pixel at a time.
It does that by predicting the color, the light, and the form of the overall image from scratch.
And the crazy part about this is that most models start with just pure noise, similar to static on
a TV, and then they gradually sculpt that noise into a clear and vivid looking image.
So first up with this is Gans, which are generative adversarial networks.
Now, that might sound complicated, but this is actually pretty simple and pretty genius.
So just imagine an art contest between two rivals the artist aka the generator, which is creating realistic
looking images, and then the critic, aka the discriminator that's trying to spot the fake images.
Now the generator will start with rough and synthetic images, and the discriminator examines them and
calls out what's fake with each image.
Now, every time it catches a fake image, the generator learns and tweaks its next attempt to be even
better than the previous.
Now this cycle repeats again and again, until the generator gets so good that it can fool even the
sharpest critic.
So on the left side, we have here the generator building the images from scratch and on the right side
the discriminator, judging if they're real or not, and at the bottom the adversarial process, which
is the feedback loop where they both continue to level up their skills.
Now, the arrows in this process are the key because it's a never ending loop of creation and critique
that sharpens the skill set of both sides.
So in a nutshell, Gans are like a relentless art competition.
So they're great at producing super realistic images, but the training itself can get a little bit
tricky.
Now, sometimes the generator gets stuck repeating the same types of images, which is a glitch that
we call mode collapse.
Next up is something that a lot of people overlook, but it plays a huge role in creative AI and this
is called variational Autoencoders, or for short VAE.
Now these models don't compete like Gans or work through de-noising like diffusion models.
Instead, they focus on understanding and reimagining the data that they're trained on.
So here's the idea.
Vaes.
Take complex images and compress them into a simple and abstract form, which is called latent space.
From there, the model can either rebuild the image or sample new points from that space to create fresh
new variants.
It's kind of similar to capturing the essence of something and then remixing it.
Now, these are especially handy when you want smooth and realistic variations of images, whether it's
tweaking faces, generating slightly different handwritten numbers, or even coming up with new product
designs so they don't just copy.
They explore possibilities within learned patterns.
So let's walk through what we're seeing here.
On the left we have our input data, in this case digits.
Now that data gets fed into the encoder which acts like a smart compressor.
And instead of keeping every single detail, it distills the image down to its essential features and
rolls it up into what's called latent space, which in this case is that glowing scroll in the center
of the image.
Now, inside this space, you'll notice floating numbers, and these represent compressed versions of
your input mapped into a space of different possibilities.
And the magic wand that you can see tapping on the glowing points.
That's what's called sampling.
This is where the VAE takes a specific spot in the latent space and says something like, okay, let's
see what happens if I build from here.
This sampled point moves through the decoder, kind of like unrolling the scroll, and out comes your
reconstructed output, which might be a familiar digit or a fresh twist on the original.
What's cool is that the V isn't just memorizing and spitting things back, it's actually imagining and
being guided by what it's learned.
And this whole process compressing into latent space, sampling and reconstructing is why Vaes are so
powerful.
When you want controlled creativity, so you end up getting new variations that feel unique and authentic
and aren't just clones of the input.
Now, the real game changer here is what's called diffusion models.
These have completely flipped the script on AI art, powering tools like Stable Diffusion and Midjourney.
So picture this.
You've got a sharp image and you slowly blur it out and add noise until that original image is pure
static.
The AI model then learns how to reverse that process, cleaning it up bit by bit until the original
image completely is regenerated.
Now, when generating something completely new, it starts with pure static, pure noise and slowly
sculpts together a fresh, detailed image that matches your input prompt.
So here's a visual breakdown.
At the top left we have x0, which is the original image.
The model then adds noise in steps which is x1 becoming slightly noisy.
And after more steps we hit XT which is pure noise.
That's the forward process learning how to break down an image.
Now when we reverse it from the bottom right, starting with XT, the AI denoising step by step, then
we have x2.
It gets less noisy and eventually you land at x0.
The reconstructed image.
Now this method is insanely good at producing high quality, diverse images with tons of detail, but
it's also slower and demands more computing power than something like a Gan, for example.
So this visual hits the point that diffusion models master the art of deconstructing and rebuilding.
And they're really brilliant at creating brand new images from scratch.
So now let's talk about how text to image generation actually works.
When you type in a prompt, like, for example, a minimalist watercolor painting of a sunrise over
a calm sea, the AI isn't just searching a database for a match, it's interpreting your words, breaking
them down into visual elements such as the colors, the mood, the style, and even the lighting.
After that, it starts building the image from scratch, step by step, pulling from everything it's
learned by studying millions and millions of different images.
So the best way to picture this is to imagine you're giving a recipe to a chef who's never seen a specific
dish before.
Now, the more detailed and specific you are, the closer the final result will match what you had in
mind.
If you just say sunrise, on the other hand, you'll get something pretty basic.
But if you say and describe the vibe such as soft brushstrokes, pastel tones, calm.
See you then.
Give the AI a much clearer direction to work from.
Now here's where the different models that we talked about really come into play.
Gans are kind of like a speed painter working fast or create something convincing, even if it's a bit
more rough around the edges.
Vaes, on the other hand, are more like an architect.
They sketch out a blueprint from the image and then they fill in the details to bring it to life.
And then we have diffusion models, which are more like the sculptor.
They start from a block of noise and they start carving it away little by little until your vision actually
appears fully formed.
So whether you're after quick creative bursts or super detailed masterpieces, it all still starts with
you in the end.
So your prompt is going to be always setting the direction, and then the AI is going to be handling
the heavy lifting, predicting, refining, and shaping your idea into something brand new.
Which is pretty crazy to think about because with this technology, a simple sentence turns into a full
blown visual.
Thanks to these models working behind the scenes.
With that understanding, let's now check out the biggest names in the AI image generation space.
These are tools that are completely reshaping how we create art with just words.
First, we have Midjourney, which leans into the artistic, stylized images.
You can think dream like paintings and high concept art.
Then we have image generation with ChatGPT, which is the most conversational way to create visuals
you can describe exactly what you want in the chat, and the model will generate it in seconds, right
in the same window.
It's pretty intuitive, pretty precise, and super easy to use, especially if you're already working
within ChatGPT.
Lastly, we have stable diffusion, which is your go to for overall flexibility.
It's open source, customizable, and has a massive community behind it so you can run it locally if
you've got the hardware or you can use it online.
And to compare these side by side, here's a quick table.
A few highlights here.
Midjourney is fantastic for stylized high concept art like I mentioned, and the image generation on
ChatGPT is of course a lot more conversational and stable.
Diffusion gives you full control, is free to use locally, but overall does take a little bit more
setup.
So each tool kind of has its own sweet spot.
Whether you want fast creative outputs or deep customization, this should help you pick the right tool
for your workflow.
And that's your crash course in AI image generation.
We've covered how these models work, why they're different from text based models, and how you can
start thinking like an AI artist.