Image to image translation

We draw messy sketches. She turns them into photos. That’s image-to-image translation.

From doodle to detail

The idea is simple. We give AI (artificial intelligence) a rough outline, and she fills in the rest. A circle with triangles becomes a cat’s face. A few bent lines grow into a tree. The point isn’t to perfect our drawing. The point is to get her to imagine what we meant.

Pix2Pix

Pix2Pix was one of the first models here. We pair each sketch with its photo. She studies the pairs, pixel by pixel. When we hand her a new sketch, she predicts the matching photo. It works well when the training set is tight, like edges-to-buildings or labels-to-faces. Less so when we stray. She’s literal.

CycleGAN

CycleGAN lets her dream bigger. No pairs needed. Instead she learns two worlds—sketches and photos—and cycles between them. A sketch becomes a photo, then back to a sketch. If she can return to the start, she’s on the right track. It’s fuzzier but more flexible. She can swap horses for zebras, apples for oranges, or our doodles for pictures.

Why it matters

We don’t just save time. We get to test ideas faster. Draw three layouts, see three photo-level results, toss two. The gap between rough thinking and polished output gets thinner. That makes her less of a tool and more of a partner.

We keep telling ourselves not to rely on her too much. Yet we reach for her again, because it’s easier to sketch badly than code a renderer. She forgives our bad lines. We forgive her odd cats. And we move on.