How is synthetic data generated using diffusion models?

27 Haz 07:25

I tried setting up something called a diffusion model for one of my smaller projects to generate new variations from a small number of images I had. Basically, you take the images, add noise to them (like a pixelated version of the image), and then the model is supposed to "clean up" that noise to produce something new that resembles the original. The results were disappointing at first, but after a few tries, it even recreated simple hand drawings quite accurately—I remember thinking, "Ah, so that’s how it works!" The biggest advantage was that even with just 50 images, the model could generate a lot of variety. The downside, though, was that training took forever and required a ton of GPU power.