How does AI image generation work?

Type "astronaut riding a horse on Mars, oil painting" and get... exactly that. How?

The answer involves noise, diffusion, and teaching AI to be an artist by showing it how to destroy art.

The core idea: learn to remove noise

Here's the trick that makes modern image generation work:

1. Take a real image
2. Gradually add random noise until it's pure static
3. Train an AI to reverse this process, to remove the noise step by step

Once trained, you can start with pure noise and ask the AI to "denoise" it into a coherent image.

This is called diffusion: the image emerges from chaos like a photograph developing in a darkroom.

Where the text comes in

But how does "astronaut on Mars" become that specific image?

The AI doesn't just remove noise randomly. It's guided by your text prompt.

During training, the model sees millions of images paired with descriptions. It learns the relationship between words and visual concepts:

• "Red" → certain pixel patterns
• "Cat" → certain shapes and textures
• "Sunset" → certain colors and compositions

When you give it a prompt, the model uses these learned associations to guide the denoising process. At each step, it asks: "What would make this noisy image more like 'astronaut on Mars'?"

The actual process

When you generate an image:

1. Start with noise. Pure random static, like TV snow.

2. Encode your prompt. Your text gets converted to numbers that capture its meaning. "Astronaut" and "cosmonaut" end up close together, "astronaut" and "banana" far apart.

3. Denoise step by step. The model makes small adjustments, guided by both "what makes a good image" and "what matches this prompt." Typically 20-50 steps.

4. Image emerges. From total chaos, structure appears. First rough shapes, then details, then fine textures.

The magic is that the same process can generate infinite variations. Different starting noise = different image.

Why the images are so good (and so weird)

The model learned from millions of real images. It knows what things look like: light, shadow, texture, composition.

But it also learned from the internet's weirdness. Ask for "two cats" and sometimes you get a cat with extra legs. Why? Because the model learned patterns, not rules. It doesn't "know" cats have four legs. It just knows what cat-like pixels look like.

This is why AI art has that distinctive look:

• Incredible at vibes and composition
• Shaky on counting and text
• Hands? Forget it

DALL-E vs Midjourney vs Stable Diffusion

DALL-E (OpenAI): The pioneer. Clean, follows prompts literally, good at text in images.

Midjourney: Optimized for aesthetics. Everything looks like concept art. Less literal, more "artistic interpretation."

Stable Diffusion: Open source. You can run it yourself, modify it, train it on your own images. Huge community of fine-tuned models.

They all use diffusion. The differences are training data, model size, and how they interpret prompts.

Why this matters

AI image generation is:

• Destroying stock photography. Why pay for generic images when you can generate exactly what you need?
• Changing art. Artists use it as a tool, argue about it as a threat, or both.
• Raising questions. If AI trained on human art, who owns the output? We're still figuring this out.
• Getting video. Sora and others use the same principles to generate video. The implications are enormous.

The weird philosophical bit

Here's what trips people up: the AI doesn't store images and remix them. It learned patterns, statistical relationships between pixels.

When you ask for "astronaut on Mars," it's not finding an astronaut image and a Mars image and combining them. It's generating pixels that fit the pattern of what astronaut-on-Mars images look like, based on everything it learned.

It's more like dreaming than collaging.

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Image generation is just the beginning. The same diffusion approach now generates video, 3D models, and music. Next: Why are GPUs so expensive?, the hardware bottleneck behind all of this.