Image-to-image translation transforms images from one visual domain to another while preserving structure. Examples: Sketch to photo, day to night, summer to winter, horse to zebra, photo to painting, map to satellite. Approaches: Paired training: pix2pix requires aligned source/target pairs, learns direct mapping. Unpaired training: CycleGAN learns from unpaired examples using cycle consistency loss. Modern diffusion: SDEdit, img2img add noise then denoise toward target domain. Key architectures: Conditional GANs, encoder-decoder networks, cycle-consistent adversarial training. Diffusion img2img: Start from encoded input image + noise, denoise with text conditioning toward new domain. Denoising strength controls how much original is preserved. Applications: Photo editing, artistic stylization, domain adaptation, synthetic data, virtual try-on, face aging. Style-specific models: GFPGAN (face restoration), CodeFormer, specialized checkpoints. Challenges: Preserving identity/structure across transformation, handling diverse inputs, artifacts. Foundational technique enabling countless creative and practical applications.