How Are Images Rendered in AI?

When AI generates images, it's actually working backwards. Instead of starting with a blank canvas and adding details, most modern image generation systems start with pure noise (think TV static) and gradually refine it into a coherent image.

Imagine you're looking at a blurry, noisy photo and trying to guess what it's supposed to be. That's essentially what AI image generators do, but they're really good at it because they've been trained on millions of images.

Enter the Latent Space: Where Images Live in AI's Mind

The secret sauce here is something called "latent space." Think of latent space as a massive multidimensional warehouse where all possible images exist. Every point in this space represents a different image.

Here's the cool part: similar images are close together in latent space. All the images of dogs are in one neighborhood, cats in another, landscapes somewhere else. The AI learns the map of this space during training.

When you type a prompt like "a corgi wearing a bow tie," the AI navigates to the right neighborhood in latent space—somewhere between "corgi" and "bow tie."

The Denoising Process: Like Sculpting in Reverse

Let's get into how it actually works:

Start with noise: The AI begins with random noise—completely random pixel values.

Predict the denoising direction: The model looks at this noisy image and, based on your text prompt, predicts "if this is supposed to be a corgi with a bow tie, how should I change these pixels?"

Take a step: The AI moves the image a bit in that direction, removing some noise.

Repeat: The AI keeps looking at the increasingly less noisy image, making new predictions, and taking more steps.

Each of these iterations is what we call a "step" in the generation process. More steps generally mean more refined images, but also take longer to generate.

Controlling the Journey: Samplers and Steps

Steps: Quality vs. Speed

When you set the number of steps for image generation, you're deciding how many iterations of denoising to perform:

• Low steps (15-20): Faster generation but potentially less detail
• High steps (50+): More refined results but takes longer

But there's a catch—more isn't always better! Past a certain point (usually around 30-50 steps), you hit diminishing returns. The changes become so subtle that human eyes can barely notice the difference.

Samplers: Different Paths to the Same Destination

Samplers are algorithms that control how the AI navigates from noise to image. Think of them as different driving styles:

• DDIM: The highway route—fast and direct, but might miss some scenic details
• Euler a: The balanced path—generally good quality with reasonable speed
• DPM++ 2M Karras: The scenic route—slower but often with better fine details

Each sampler has its own mathematical approach to deciding how big each denoising step should be and exactly how to move through latent space.

Behind the Curtain: Diffusion Models

The technical name for the most popular image generation approach is "diffusion models." Here's a slightly more technical explanation:

During training, the AI learns the process of gradually adding noise to real images until they become pure noise.

Then it learns to reverse this process—to predict what an image with less noise would look like, given a noisy image and a description.

When generating, it applies this denoising knowledge step by step until a clear image emerges.

This approach is used by models like Stable Diffusion, Midjourney, and DALL-E.

The Magic of Controlled Randomness

One fascinating aspect is that starting with different random noise will produce different interpretations of the same prompt. This is why you can generate the same prompt multiple times and get varied results.

The latent space is so vast that there are countless valid ways to visualize "a corgi wearing a bow tie"—different angles, lighting, styles, and corgi personalities.

Why This Matters

Understanding how image generation works helps you get better results:

• Knowing about steps helps you balance quality and generation time
• Understanding samplers lets you choose the right one for your specific needs
• Recognizing the role of randomness explains why results vary

Conclusion

AI image generation is a remarkable process of navigating through latent space and gradually denoising random noise into coherent, often beautiful images. The next time you type a prompt and watch an image materialize before your eyes, you'll know there's a sophisticated mathematical journey happening behind the scenes—from noise to art, one denoising step at a time.