Controllable image generation is fundamental to the success of modern generative AI, yet it faces a critical trade-off between semantic fidelity and inference speed. The RemEdit diffusion-based framework addresses this trade-off with two synergistic innovations. First, for editing fidelity, we navigate the latent space as a Riemannian manifold. A mamba-based module efficiently learns the manifold's structure, enabling direct and accurate geodesic path computation for smooth semantic edits. This control is further refined by a dual-SLERP blending technique and a goal-aware prompt enrichment pass from a Vision-Language Model. Second, for additional acceleration, we introduce a novel task-specific attention pruning mechanism. A lightweight pruning head learns to retain tokens essential to the edit, enabling effective optimization without the semantic degradation common in content-agnostic approaches. RemEdit surpasses prior state-of-the-art editing frameworks while maintaining real-time performance under 50% pruning. Consequently, RemEdit establishes a new benchmark for practical and powerful image editing. Source code: https://www.github.com/eashanadhikarla/RemEdit.

翻译：可控图像生成是现代生成式人工智能成功的关键，然而其在语义保真度与推理速度之间面临关键的权衡。RemEdit扩散框架通过两项协同创新解决了这一权衡。首先，在编辑保真度方面，我们将潜在空间视为黎曼流形进行导航。一个基于Mamba的模块高效学习流形结构，从而能够为平滑语义编辑直接且准确地计算测地线路径。这种控制通过双SLERP混合技术以及来自视觉语言模型的目标感知提示增强步骤得到进一步优化。其次，为获得额外加速，我们引入了一种新颖的任务特定注意力剪枝机制。一个轻量级剪枝头学习保留对编辑至关重要的标记，使得在无需内容无关方法常见的语义退化情况下实现有效优化。RemEdit在保持50%剪枝下实时性能的同时，超越了先前最先进的编辑框架。因此，RemEdit为实用且强大的图像编辑确立了新基准。源代码：https://www.github.com/eashanadhikarla/RemEdit。