SpurFlow: Spatially-Adaptive Residual Steering for Few-Step Image Editing

Recent diffusion distillation methods enable high-quality image generation with only a few sampling steps, yet extending them to image editing remains challenging. We argue that this difficulty arises from a mismatch between conventional distillation objectives and the asymmetric nature of image editing. Unlike generation, most editing tasks preserve the majority of the reference image while modifying only sparse instruction-specified regions. Under full-field matching, preserved tokens dominate both spatially and in gradient contribution, diluting sparse yet critical edit signals. To address this, we propose SpurFlow, an editing-oriented distillation framework that separates reference-preserving content from edit-specific updates. SpurFlow learns token-wise reference coefficients to adaptively control reference preservation across spatial locations while steering residual learning toward regions requiring edits. These coefficients further guide an edit-aware adversarial objective that concentrates perceptual supervision on edited regions. Extensive experiments across multiple benchmarks and foundation models demonstrate strong few-step editing performance. With only two sampling steps, SpurFlow closely approaches the full-step FLUX.1-Kontext teacher, surpasses the Qwen-Image-Edit-2511 teacher on both benchmark overall scores, and approaches the performance of the officially distilled 4-step FLUX.2-Klein model.