TwinEdit: One-Step Image Editing as Single-State Latent Transport

One-step text-to-image models have brought image synthesis to real-time speeds, creating a natural foundation for equally fast image editing. Turning this speed into reliable editing remains challenging because a single update must introduce substantial target changes while retaining the structure and content of the source image. Recent one-step methods address this challenge through low-energy transport, post-hoc candidate selection, or localized adaptive editing. However, steering the edit and representing the edit are different problems. To address this gap, we propose TwinEdit, which formulates one-step editing as the transport of a single source-anchored latent state. Source- and target-conditioned latent responses estimate a target-directed path, while only the encoded source is transported along it. The transported state is refined by one target-conditioned denoising step and decoded into a target image. The final edit is then formed along the displacement from the source to this decoded target. Experiments on PIE-Bench show that TwinEdit produces clean, source-faithful edits with strong target alignment while retaining one-step inference efficiency and the lowest measured GPU-memory use in our comparison.