  Denoising diffusion models have recently gained prominence as powerful tools
for a variety of image generation and manipulation tasks. Building on this, we
propose a novel tool for real-time editing of images that provides users with
fine-grained region-targeted supervision in addition to existing prompt-based
controls. Our novel editing technique, termed Layered Diffusion Brushes,
leverages prompt-guided and region-targeted alteration of intermediate
denoising steps, enabling precise modifications while maintaining the integrity
and context of the input image. We provide an editor based on Layered Diffusion
Brushes modifications, which incorporates well-known image editing concepts
such as layer masks, visibility toggles, and independent manipulation of
layers; regardless of their order. Our system renders a single edit on a
512x512 image within 140 ms using a high-end consumer GPU, enabling real-time
feedback and rapid exploration of candidate edits. We validated our method and
editing system through a user study involving both natural images (using
inversion) and generated images, showcasing its usability and effectiveness
compared to existing techniques such as InstructPix2Pix and Stable Diffusion
Inpainting for refining images. Our approach demonstrates efficacy across a
range of tasks, including object attribute adjustments, error correction, and
sequential prompt-based object placement and manipulation, demonstrating its
versatility and potential for enhancing creative workflows.
