InstaEdit: Instant Image Editing via Optimized Noise Prediction

This paper proposes InstaEdit, an acceleration frameworkfor image editing models that reduces the inference steps while preserv-ing, or even improving, generation quality. Unlike existing approaches,the proposed method employed a noise prediction strategy that incorpo-rates both textual instructions and visual conditions into the noise initial-ization process, producing latent variables that are closer to intermediatestates along the generative trajectory. These optimized latent variableswere then used as the starting point for the sampling process. The keycomponent of the framework is a deep noise predictor that estimates amore suitable initial noise state for the generation procedure, therebyreducing redundant denoising steps and improving inference efficiency.InstaEdit can be seamlessly integrated into existing mainstream FlowMatching inference pipelines. We applied InstaEdit to several state-of-the-art image editing models, including Step1X-Edit, FLUX.1-Kontext,and FLUX.2-klein-base-9B. Experimental results demonstrated that In-staEdit achieved acceleration factors of 4.13×, 3.88×, and 4.22×, respec-tively, while maintaining stable and high-quality generative performanceacross multiple standard image editing benchmarks.