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.

基于层次扩散刷子的实时图像编辑技术结合了图像编辑概念，通过精细的区域导向修改中间去噪步骤，保持输入图像的完整性和上下文，并在高端消费级GPU上在140毫秒内呈现512x512图像的单个编辑，从而实现实时反馈和候选编辑的快速探索。通过用户研究验证了方法和编辑系统的可行性和有效性，对比了现有技术如InstructPix2Pix和Stable Diffusion Inpainting在图像优化方面的表现。该方法在对象属性调整、错误修正和顺序提示式对象放置和操作等各种任务上表现出有效性，展示了它提升创作工作流程的多功能性和潜力。

使用分层扩散笔简化图像编辑