First, the AI needs to under­stand what you wrote. If you type "a red apple on a wood­en table," the machine doesn't know what "apple," "red," or "table" means. It just sees text. This is where a com­po­nent, often a mod­el like CLIP (Con­trastive Lan­guage-Image Pre-train­ing), comes in. CLIP is trained on a mas­sive amount of images and the text that goes with them, scraped from the inter­net. Think of it like a giant dig­i­tal library of pic­ture books. By look­ing at hun­dreds of mil­lions of image-cap­­tion pairs, it learns to con­nect words with visu­al con­cepts.

It doesn't learn to define "apple." Instead, it learns that the word "apple" is sta­tis­ti­cal­ly very like­ly to appear along­side images that have cer­tain shapes, col­ors, and tex­tures. It cre­ates a math­e­mat­i­cal representation—a bunch of num­bers called a vector—for both the text and the image. For "a red apple on a wood­en table," it gen­er­ates a spe­cif­ic text vec­tor. For a pic­ture of that same scene, it gen­er­ates a sim­i­lar image vec­tor. The goal of the train­ing is to make the vec­tors for a match­ing text and image as close as pos­si­ble in this math­e­mat­i­cal space. So, CLIP acts as the bridge, trans­lat­ing your words into a for­mat the image-mak­ing part of the AI can under­stand.

Next comes the image gen­er­a­tion itself. Most mod­ern text-to-image mod­els use a tech­nique called dif­fu­sion. This is the part that feels a bit like sculpt­ing. The AI starts with a can­vas full of com­plete ran­dom­ness, like a TV screen show­ing pure sta­t­ic. It's just ran­dom noise. The dif­fu­sion process then refines this noise step by step, grad­u­al­ly shap­ing it into a rec­og­niz­able image.

Here’s a way to think about it: Imag­ine you have a clear pho­to. The "for­ward" part of the dif­fu­sion process is like adding a lit­tle bit of noise to it over and over again, in many small steps, until the orig­i­nal pho­to is com­plete­ly lost in sta­t­ic. The AI is trained on how to do this in reverse. It learns to look at a noisy image and pre­dict what a slight­ly less noisy ver­sion of that image would look like. It essen­tial­ly learns to denoise an image.

So, when you give it a prompt, the process starts with a ran­dom field of noise. Then, guid­ed by the text vec­tor from CLIP, a denois­ing mod­el (often a U‑Net archi­tec­ture) begins its work. At each step, it looks at the noisy image and, using the prompt's infor­ma­tion as a guide, removes a lit­tle bit of noise. The CLIP vec­tor tells the denois­er, "What­ev­er you do, make sure the final result is some­thing that match­es the con­cept of 'a red apple on a wood­en table'."

This hap­pens over and over, maybe 20 to 100 times. With each step, the sta­t­ic gets a lit­tle more orga­nized. Blobs of col­or appear. Shapes start to form. An apple-like object might emerge, then a flat sur­face under­neath it. The AI is con­tin­u­ous­ly check­ing its work against the prompt's mean­ing. Is it red? Is it on some­thing that looks like wood? Slow­ly, the chaos of the ini­tial noise is replaced by the coher­ent image you asked for.

To get more spe­cif­ic, some mod­els like Sta­ble Dif­fu­sion don't do this in the high-res­o­lu­­tion pix­el space you see. That would require too much com­put­er pow­er. Instead, they first com­press a high-res­o­lu­­tion image into a much small­er, "latent" space. Think of it like a high-qual­i­­ty ZIP file for an image. The AI does all the noisy work—the dif­fu­sion and denois­ing steps—in this com­pressed space, which is much faster and more effi­cient. Once the denois­ing process is fin­ished in this latent space, a final com­po­nent called a decoder con­verts that small, abstract rep­re­sen­ta­tion back into the full-sized, detailed pix­el image you see at the end. This is why it's called a "latent dif­fu­sion mod­el."

The spe­cif­ic words you use in your prompt are very impor­tant. The AI isn't think­ing; it's match­ing pat­terns it learned from its train­ing data. If you write a clear, descrip­tive prompt, you guide the AI more effec­tive­ly. For exam­ple, adding details like "pho­to­re­al­is­tic," "in the style of Van Gogh," "cin­e­mat­ic light­ing," or "wide-angle shot" push­es the AI to pull from dif­fer­ent parts of its vast train­ing data to match those con­cepts. The struc­ture of the prompt can also mat­ter. Start­ing with the main sub­ject and then adding descrip­tive details and style infor­ma­tion can lead to bet­ter results. Some sys­tems even allow you to give neg­a­tive prompts—things you don't want to see in the image—to help refine the out­put fur­ther.

This entire process is built on mas­sive datasets. The LAION-5B dataset, for exam­ple, which was used to train ear­ly ver­sions of Sta­ble Dif­fu­sion, con­tains near­ly 6 bil­lion image-text pairs scraped from the web. This is both a strength and a weak­ness. It's why the AI can gen­er­ate such a wide vari­ety of images, from pho­tos to illus­tra­tions. But it also means the AI inher­its all the bias­es, strange con­tent, and copy­rights present in that data. If the data has more pic­tures of doc­tors who are men, the AI will like­ly gen­er­ate images of male doc­tors when prompt­ed.

So, when you type a sen­tence and get an image, what's hap­pen­ing is a chain of com­plex steps. First, a lan­guage mod­el trans­lates your text into a mean­ing­ful numer­i­cal instruc­tion. Then, a gen­er­a­tive mod­el takes that instruc­tion and uses it to guide a denois­ing process, start­ing from pure sta­t­ic and pro­gres­sive­ly refin­ing it into a coher­ent image. It often does this in a com­pressed latent space to be more effi­cient. It's not cre­at­ing from a place of under­stand­ing, but from an incred­i­bly sophis­ti­cat­ed abil­i­ty to rec­og­nize and recon­struct pat­terns based on the bil­lions of exam­ples it has been shown.