InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow
This work addresses the need for efficient and high-quality image editing for users in creative and design fields, representing an incremental improvement over existing methods.
The authors tackled the problem of fast text-guided image editing by proposing InstantEdit, a method that achieves better qualitative and quantitative results on the PIE dataset compared to state-of-the-art few-step editing methods.
We propose a fast text-guided image editing method called InstantEdit based on the RectifiedFlow framework, which is structured as a few-step editing process that preserves critical content while following closely to textual instructions. Our approach leverages the straight sampling trajectories of RectifiedFlow by introducing a specialized inversion strategy called PerRFI. To maintain consistent while editable results for RectifiedFlow model, we further propose a novel regeneration method, Inversion Latent Injection, which effectively reuses latent information obtained during inversion to facilitate more coherent and detailed regeneration. Additionally, we propose a Disentangled Prompt Guidance technique to balance editability with detail preservation, and integrate a Canny-conditioned ControlNet to incorporate structural cues and suppress artifacts. Evaluation on the PIE image editing dataset demonstrates that InstantEdit is not only fast but also achieves better qualitative and quantitative results compared to state-of-the-art few-step editing methods.