PersistEdit: Steering Persistent Noise for Inversion-Free Image Editing

Recent inversion-free flow-based methods enable text-guided image editing without explicitly inverting source images into noise. They typically exploit averaging across timesteps through independent Gaussian noise sampling to approximate the noise-marginalized editing direction. However, finite sampling leaves realization-specific deviations that can favor different ways of realizing target semantics, shaping path-specific editing tendencies. Particularly, independent resampling repeatedly shifts these tendencies, disrupting the coherent accumulation of their effects over successive editing steps and leading to incomplete edits with residual source content or visible artifacts. To address this issue, we introduce PersistEdit, a training-free and inversion-free method to organize and selectively exploit realization-specific effects through noise control, which involves two key components, i.e., Noise Persistence and Semantic Steering. Specifically, Noise Persistence models noise evolution as a Gaussian-Markov process that combines inherited memory with newly sampled innovation, promoting coherent editing tendencies across updates. Semantic Steering selects target-favorable innovations using semantic cues within the estimated editing region while retaining inherited memory, allowing selected semantic preferences to persist across updates. Together, these designs produce a temporally coherent and semantically directed editing trajectory, improving target-semantic alignment while maintaining source-image fidelity. Extensive experiments on PIE-Bench show that PersistEdit outperforms existing methods in overall performance. Compared with FlowEdit, it improves background PSNR by 4.37 dB and edited-region CLIP similarity by 2.32%.