Streamlining Image Editing with Layered Diffusion Brushes

Denoising diffusion models have emerged as powerful tools for image manipulation, yet interactive, localized editing workflows remain underdeveloped. We introduce Layered Diffusion Brushes (LDB), a novel framework that facilitates real-time and iterative image editing with fine-grained, region-specific control. LDB leverages a unique approach that caches intermediate latent states within the diffusion process, enabling users to apply prompt-guided edits via masks in a non-destructive, layered manner. Key innovations include latent caching for significant speed enhancements (achieving edits in under 140ms on consumer GPUs) and redefining layering for diffusion models with an order-agnostic system that allows for independent manipulation and stacking of edits, even in overlapping regions. An editor implementing LDB, incorporating familiar layer concepts, was evaluated through user study and quantitative metrics. Results demonstrate LDB's superior speed alongside comparable or improved image quality, background preservation, and edit fidelity relative to existing state-of-the-art techniques across various sequential image manipulation tasks. The findings highlight LDB's potential to significantly enhance creative workflows by providing an intuitive and efficient approach to diffusion-based image editing and its potential for expansion into related subdomains, such as video editing.