How are AI images generated?

Posted by Einsty
Horse Riding on marse
  • What is AI image generation?
    • AI image generators are like magical artists powered by computers. They use trained neural networks (think of them as digital brains) to create pictures from scratch.
    • Imagine telling these AI artists what you want in plain language, and they whip up original, realistic images based on your description.
    • What’s fascinating is that they can blend different styles, ideas, and features to make beautiful and contextually relevant art. This magic happens thanks to a branch of AI called generative AI.
  • How Do They Do It?
    • These AI artists learn their craft by studying a massive amount of data—huge collections of images.
    • During their training, they pick up all sorts of tricks and details from those images.
    • Then, armed with this knowledge, they can create new pictures that share similarities with what they’ve seen before.
  • Variety of AI Artists:
    • There’s a whole gallery of AI artists out there, each with its own special abilities.
    • Let me introduce you to a few:
      1. Neural Style Transfer: It’s like taking the style of one picture and applying it to another.
      2. Generative Adversarial Networks (GANs): These use a clever duo of neural networks to create realistic images that look like the ones they’ve studied.
      3. Diffusion Models: These artists transform random noise into structured images, like turning chaos into art.
dog image

Neural Style Transfer

Neural Style Transfer (NST) resembles a mystical art mixer! Allow me to explain:

  1. Components:
    • We have two pictures:
      • Content Image: This is our default image, which is a cute dog picture.
      • Style Reference Image: Imagine a famous painting by Wassily Kandinsky.
  2. The Recipe for Art:
    • NST blends these images together. It takes the content from the dog photo and adds Kandinsky’s style to it.
    • The result? An output image that looks like the dog but is “painted” in Kandinsky’s unique way.
  3. How It Works:
    • NST uses fancy math (optimization) to adjust the output image.
    • It tweaks the pixels to match the content details from the dog and the artistic flair from Kandinsky.
    • Think of it as a digital brushstroke dance!

Generative Adversarial Networks (GANs)

  1. What is a GAN?
    • Generative Adversarial Networks (GANs) are a type of neural network.
    • They’re used for unsupervised learning, which means they learn from data without explicit labels or supervision.
    • GANs consist of two main parts: the discriminator and the generator.
  2. How do GANs work?
    • Imagine a creative duo: the generator and the discriminator.
    • The generator creates fake data (like images or text) from random noise.
    • The discriminator checks if the data is real or fake.
    • They play a game of “catch me if you can”!
    • The generator tries to make its fake data look real, while the discriminator gets better at spotting fakes.
  3. Why are GANs cool?
    • They’re like artistic forgers! GANs can create super-realistic stuff.
    • Think of them as Picasso meets AI.
    • GANs are used for:
      • Making fake images (like creating realistic faces from scratch).
      • Style transfer (turning a photo into a painting).
      • Text-to-image magic (making pictures from descriptions).
  4. Types of GANs:
    • Vanilla GAN: The basic version. It’s like a simple math problem solver.
    • Conditional GAN (CGAN): Adds extra info to the game. Imagine the generator saying, “Paint me a cat, but with stripes!”

Generative Adversarial Networks (GANs) and their different flavors:

  1. Vanilla GAN:
    • The simplest type of GAN.
    • Imagine it as a basic math problem solver.
    • It uses two players: the generator and the discriminator.
    • The generator creates fake stuff (like images) from scratch.
    • The discriminator checks if it’s real or fake.
    • They play a game of “spot the impostor”!
    • Vanilla GAN’s secret sauce: stochastic gradient descent (fancy math optimization).
  2. Conditional GAN (CGAN):
    • Adds a twist to the game.
    • The generator gets an extra hint (let’s call it ‘y’).
    • It uses this hint to create more specific stuff.
    • The discriminator now has to tell real from fake, plus guess the hint.
    • It’s like saying, “Paint me a cat, but with stripes!”
  3. Deep Convolutional GAN (DCGAN):
    • The popular kid in town.
    • Instead of basic math, it uses ConvNets (fancy image filters).
    • No max pooling—just fancy convolutional strides.
    • And the layers aren’t fully connected (they’re like distant cousins).
  4. Laplacian Pyramid GAN (LAPGAN):
    • The artist’s dream.
    • Uses multiple generators and discriminators.
    • Picture a pyramid of images (like a fancy art gallery).
    • It starts with a tiny image, adds layers, and ends up with a masterpiece.
    • It’s like zooming in on a painting until you see every brushstroke.
  5. Super Resolution GAN (SRGAN):
    • The magician.
    • It combines a deep neural network with an adversarial network.
    • She takes low-res pictures and whispers, “Abracadabra!”
    • Voilà! High-res images with more details.
    • Perfect for turning pixelated cats into crystal-clear feline portraits! 

Diffusion Models

  1. Diffusion Models: These are new AI models that work differently from the older ones called GANs. Instead of modifying existing images, they create entirely new ones.
  2. Training Process: Diffusion models learn by looking at millions of images and reading captions that describe those images. This helps them understand how text and images relate.
  3. Creating Images: When you give them a text prompt (like “draw a dog”), they start with a rough image and gradually add more details until they have a complete picture.
  4. No Internet References: Unlike searching the internet for existing images, diffusion models make everything from scratch. So, if you ask for a dog, they’ll create one based on what they know about dogs.

In simple terms, these models are like artists who can imagine and draw things without copying from anyone else! 

Conclusion:

AI-generated images have countless benefits. Imagine being able to take pictures that are on par with those of professional photographers with just a few clicks. Plus, you can invent entirely new things and people that don’t even exist in reality!

Here are some perks of using an AI image generator:

  1. Boosted Productivity: Forget complex editing software. AI lets you whip up stunning visuals in seconds. No more endless stock image searches for your blog!
  2. Fine-Tuned Control: Adjust textures, lighting, and shadows effortlessly to achieve the perfect look.
  3. Built-in Uniqueness: Each image is born from scratch, making it inherently different.
  4. Infinite Imagination: Whether it’s a dragon in space or a rainbow-colored unicorn, AI can bring your wildest ideas to life.
  5. Pure Joy: Creating art just for fun? AI makes it a delightful experience.

Fun fact: AI Dall E created the thumbnail for this article, which is “Horse riding on Mars in a chariot.”

Here’s another image of a woman eating food with a tiger.

References:

https://www.altexsoft.com/blog/ai-image-generation/

https://www.tensorflow.org/tutorials/generative/style_transfer

https://www.geeksforgeeks.org/generative-adversarial-network-gan/

https://www.hypotenuse.ai/blog/how-do-ai-image-generators-work