Image-to-Image

Image-to-image translation covers a vast family of tasks — super-resolution, style transfer, inpainting, colorization, denoising — unified by the idea of learning a mapping between image domains. Pix2Pix (2017) and CycleGAN showed paired and unpaired translation were both learnable, but diffusion models rewrote the playbook entirely. ControlNet (2023) demonstrated that conditioning Stable Diffusion on edges, depth, or poses gives surgical control over generation, while models like SUPIR push restoration quality beyond what was thought possible. The Swiss army knife of visual AI — nearly every creative and restoration workflow runs through some form of image-to-image.