RemEdit: Efficient Diffusion Editing with Riemannian Geometry

• URL: http://arxiv.org/abs/2601.17927v1
• Date: Sun, 25 Jan 2026 17:58:57 GMT
• Title: RemEdit: Efficient Diffusion Editing with Riemannian Geometry
• Authors: Eashan Adhikarla, Brian D. Davison,
• Abstract summary: RemEdit is a diffusion-based framework for image editing.<n>For editing fidelity, we use a mamba-based module and a goal-aware prompt enrichment pass from a Vision-Language Model.<n>For additional acceleration, we introduce a novel task-specific attention pruning mechanism.<n>RemEdit surpasses prior state-of-the-art editing frameworks while maintaining real-time performance under 50% pruning.
• Score: 1.8594036119086927
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• EditInfinity: Image Editing with Binary-Quantized Generative Models [64.05135380710749]
  We investigate the parameter-efficient adaptation of binary-quantized generative models for image editing.<n>Specifically, we propose EditInfinity, which adapts emphInfinity, a binary-quantized generative model, for image editing.<n>We propose an efficient yet effective image inversion mechanism that integrates text prompting rectification and image style preservation.
  arXiv  Detail & Related papers  (2025-10-23T05:06:24Z)
• FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing [75.29825659756351]
  FlashEdit is a novel framework designed to enable high-fidelity, real-time image editing.<n>Its efficiency stems from three key innovations: (1) a One-Step Inversion-and-Editing (OSIE) pipeline that bypasses costly iterative processes; (2) a Background Shield (BG-Shield) technique that guarantees background preservation by selectively modifying features only within the edit region; and (3) a Sparsified Spatial Cross-Attention (SSCA) mechanism that ensures precise, localized edits by suppressing semantic leakage to the background.
  arXiv  Detail & Related papers  (2025-09-26T11:59:30Z)
• Visual Autoregressive Modeling for Instruction-Guided Image Editing [97.04821896251681]
  We present a visual autoregressive framework that reframes image editing as a next-scale prediction problem.<n>VarEdit generates multi-scale target features to achieve precise edits.<n>It completes a $512times512$ editing in 1.2 seconds, making it 2.2$times$ faster than the similarly sized UltraEdit.
  arXiv  Detail & Related papers  (2025-08-21T17:59:32Z)
• UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying [64.5307229755533]
  We introduce a novel training-free framework named UniEdit-I to enable the unified VLM with image editing capability.<n>We implement our method based on the latest BLIP3-o and achieved state-of-the-art (SOTA) performance on the GEdit-Bench benchmark.
  arXiv  Detail & Related papers  (2025-08-05T06:42:09Z)
• Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency Models [1.9389881806157316]
  In this work, we propose a novel framework that enhances image inversion using consistency models.<n>Our method introduces a cycle-consistency optimization strategy that significantly improves reconstruction accuracy.<n>We achieve state-of-the-art performance across various image editing tasks and datasets.
  arXiv  Detail & Related papers  (2025-06-23T20:34:43Z)
• FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model [54.693572837423226]
  FireEdit is an innovative Fine-grained Instruction-based image editing framework that exploits a REgion-aware VLM.<n>FireEdit is designed to accurately comprehend user instructions and ensure effective control over the editing process.<n>Our approach surpasses the state-of-the-art instruction-based image editing methods.
  arXiv  Detail & Related papers  (2025-03-25T16:59:42Z)
• Latent Space Editing in Transformer-Based Flow Matching [53.75073756305241]
  Flow Matching with a transformer backbone offers the potential for scalable and high-quality generative modeling. We introduce an editing space, $u$-space, that can be manipulated in a controllable, accumulative, and composable manner. Lastly, we put forth a straightforward yet powerful method for achieving fine-grained and nuanced editing using text prompts.
  arXiv  Detail & Related papers  (2023-12-17T21:49:59Z)
• LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance [0.0]
  LEDITS is a combined lightweight approach for real-image editing, incorporating the Edit Friendly DDPM inversion technique with Semantic Guidance. This approach achieves versatile edits, both subtle and extensive as well as alterations in composition and style, while requiring no optimization nor extensions to the architecture.
  arXiv  Detail & Related papers  (2023-07-02T09:11:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.