LightDraft: Accelerating Masked Diffusion Image Editing with Look-Ahead Guidance

Masked diffusion models support instruction-guided image editing through parallel token prediction. However, their reliance on bidirectional attention requires repeated full-sequence computation throughout sampling, while a fixed sampling schedule assigns the same refinement budget to edits with very different spatial extent and convergence behavior. To address this problem, we investigate whether intermediate predictions can reveal where computation is needed and how long refinement should continue. We observe that the spatial support of an edit often stabilizes before its visual appearance is fully refined. Based on this observation, we propose LightDraft, a training-free acceleration method that adapts both feature computation and sampling length to each edit. LightDraft decodes intermediate complete token predictions into look-ahead images, exposing the developing edit before sampling is complete. Differences from the reference image are used to progressively localize the edit region and guide a two-stage regional caching strategy, allowing most features outside the region to be reused. Meanwhile, changes between successive look-ahead images guide the sampling budget, reducing unnecessary refinement when predictions have largely stabilized. Experiments on ImgEdit and PIE-Bench show that LightDraft achieves an 8.14× speedup while maintaining instruction-following capability and editing quality.