Learning an Image Editing Model without Image Editing Pairs

ArXi:2510.14978v2 Announce Type: replace-cross Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of input-target pairs. This is a critical bottleneck, as such naturally occurring pairs are hard to curate at scale. Current workarounds use synthetic