Understanding How AI Image Generation works in Simple terms

Every time you generate an image with a prompt, you can usually watch it snap into place. A blurry mess turns into a rough shape, then a clearer shape, and a few seconds later you have something like “cat with sunglasses.” The model isn’t drawing it in one shot. It’s running the same cleanup loop again and again, starting from random noise and slowly removing this noise until an image shows up. This is the basic idea behind diffusion models like DDPM. 

Noise here just means a screen full of speckles with no meaning. The loop works because the model learned one job really well, to remove the right amount of noise at the right time. During training, the model looked at millions of noisy images and practiced predicting the exact "denoising step" needed to fix them. That’s why results improve step by step. This is why you shouldn’t expect a perfect image instantly, because the process is literally designed to get better with each pass.

With a prompt like “cat with sunglasses,” these words stay in the loop at every step. Early passes keep pushing the shape toward “cat,” and later passes keep pushing details toward “sunglasses.” A simple way to picture it is an artist refining a sketch while the same brief keeps getting repeated. This text-to-image linking is implemented with Cross-Attention method.

But those many loops can slow the process down and raise compute costs, especially at higher resolution. Latent Diffusion Models solve this by running the loop on a compressed version of the image, then decoding back to full pixels only at the end. This makes the whole pipeline far more efficient.

And once this basic loop makes sense, you can go one layer deeper into why the same prompt sometimes comes out cleaner with tiny tweaks or a rerun. 

A lot of it comes down to two controls that change how the loop behaves:

Guidanceis how strictly the model follows your prompt. Turn it up and it sticks closer to your words, but it can start to look forced or overdone. Turn it down and you get more variety, but it can drift away from what you asked for.

Steps are those cleanup loops, just counted. More steps means the model gets more passes to refine the image, but after a point you’re paying for small gains. DDIM is a sampler that can reach good results in fewer steps by taking a more efficient sampling path. 

If an output looks weird, it’s often because the process was pushed too hard or cut too short, not because the model ignored the prompt.