用黎曼几何加速图像编辑，兼顾精准控制与实时性能。
RemEdit: Efficient Diffusion Editing with Riemannian Geometry
- 将潜在空间视为黎曼流形，通过测地线实现平滑语义编辑。
- 在50%注意力剪枝下仍保持语义保真，实现实时推理。
- 适合需要高效高精度图像编辑的研究与应用开发者。
可控图像生成是现代生成式AI成功的关键，但面临语义保真度与推理速度之间的核心权衡。RemEdit基于扩散模型的框架通过两项协同创新解决该问题。首先，在编辑保真度方面，将潜在空间建模为黎曼流形，采用基于Mamba的模块高效学习流形结构，实现直接且精确的测地线路径计算，从而支持平滑的语义编辑；进一步通过双SLERP混合技术与视觉-语言模型引导的目标感知提示增强，提升控制精度。其次，在加速方面，提出一种新型任务特定注意力剪枝机制，轻量级剪枝头学习保留编辑关键的注意力令牌，避免内容无关剪枝导致的语义退化。在50%剪枝率下，RemEdit超越现有最先进编辑框架，同时保持实时性能。该工作建立了实用且强大的图像编辑新基准。源码：https://www.github.com/eashanadhikarla/RemEdit。
原文摘要 · Abstract (English)
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.
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