SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking

• URL: http://arxiv.org/abs/2605.02152v1
• Date: Mon, 04 May 2026 02:30:24 GMT
• Title: SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking
• Abstract summary: SpecEdit is a training-free dynamic-resolution framework tailored for diffusion-based image editing.<n> Experiments on Qwen-Image-Edit and FLUX.1-Kontext-dev demonstrate up to 10x and 7x acceleration, while maintaining strong quality.
• Score: 12.078069478646057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based image editing offers strong semantic controllability, but remains computationally expensive due to iterative high-resolution denoising over all spatial tokens. Dynamic-resolution sampling reduces this cost by performing early steps at reduced resolution. However, existing approaches prioritize upsampling using low-level heuristics such as edge detection or channel variance, which are weakly aligned with editing semantics and may lead to structural inconsistency. Moreover, spatial regions are often upsampled without verifying whether semantic modification is actually required, resulting in redundant high-resolution computation and accumulated errors. Therefore, we propose SpecEdit, a training-free dynamic-resolution framework tailored for diffusion-based image editing. SpecEdit follows a draft-and-verify scheme: a low-resolution draft first estimates the semantic outcome, after which token-level discrepancies are used to identify edit-relevant tokens for high-resolution denoising, while the remaining tokens stay at a coarse resolution. Experiments on Qwen-Image-Edit and FLUX.1-Kontext-dev demonstrate up to 10x and 7x acceleration, while maintaining strong quality. SpecEdit is complementary to step distillation and other acceleration techniques, achieving up to 13x speedup when combined with existing methods. Our code is in supplementary material and will be released on GitHub.

Related papers

• AViTS: Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation [10.734099999989127]
  Atemporal token selection framework for dynamic-resolution DiTs.<n>ATS models semantic importance via latent-text attention and temporal importance via token-level feature variation across diffusion timesteps.
  arXiv  Detail & Related papers  (2026-08-18T16:42:09Z)
• Adaptive Inference-Time Scaling via Early-Step Latent Verification for Image Editing [49.40982704974727]
  An unsuitable initial noise may lead to unsatisfactory editing results.<n>Recent inference-time scaling methods address this issue by sampling multiple initial noises and selecting better candidates.<n>We propose VeriLatent, a plug-and-play adaptive inference-time scaling framework with early-step latent verification for image editing.
  arXiv  Detail & Related papers  (2026-06-13T08:30:33Z)
• HierEdit: Region-Aware Hierarchical Diffusion for Efficient High-Resolution Editing [83.1290629939693]
  High-resolution image editing is essential for professional and creative applications.<n>Current approaches redundantly process the entire image canvas or rely on large-scale high-resolution datasets.<n>We introduce HierEdit, a region-aware hierarchical diffusion framework for efficient and scalable high-resolution image editing.
  arXiv  Detail & Related papers  (2026-05-17T07:14:15Z)
• SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing [15.234877788378563]
  Rectified flow models have become a de facto standard in image generation due to their stable sampling trajectories and high-fidelity outputs.<n>Despite their strong generative capabilities, they face critical limitations in image editing tasks.<n>Recent efforts have attempted to directly map source and target distributions via ODE-based approaches without inversion.<n>We propose a flow decomposition-and-aggregation framework built upon an inversion-free formulation to address these limitations.
  arXiv  Detail & Related papers  (2025-10-29T21:12:58Z)
• 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)
• LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing [0.276240219662896]
  We introduce LORE, a training-free and efficient image editing method.<n>LORE directly optimize the inverted noise, addressing the core limitations in generalization and controllability of existing approaches.<n> Experimental results show that LORE significantly outperforms strong baselines in terms of semantic alignment, image quality, and background fidelity.
  arXiv  Detail & Related papers  (2025-08-05T06:45:04Z)
• Training-Free Text-Guided Image Editing with Visual Autoregressive Model [46.201510044410995]
  We propose a novel text-guided image editing framework based on Visual AutoRegressive modeling.<n>Our method eliminates the need for explicit inversion while ensuring precise and controlled modifications.<n>Our framework operates in a training-free manner and achieves high-fidelity editing with faster inference speeds.
  arXiv  Detail & Related papers  (2025-03-31T09:46:56Z)
• PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing [63.38854614997581]
  We introduce PostEdit, a method that incorporates a posterior scheme to govern the diffusion sampling process.<n>The proposed PostEdit achieves state-of-the-art editing performance while accurately preserving unedited regions.<n>The method is both inversion- and training-free, necessitating approximately 1.5 seconds and 18 GB of GPU memory to generate high-quality results.
  arXiv  Detail & Related papers  (2024-10-07T09:04:50Z)
• TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models [53.757752110493215]
  We focus on a popular line of text-based editing frameworks - the edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts.
  arXiv  Detail & Related papers  (2024-08-01T17:27:28Z)
• Object-Centric Diffusion for Efficient Video Editing [64.71639719352636]
  Diffusion-based video editing has reached impressive quality. Such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames. We propose modifications that allow significant speed-ups whilst maintaining quality.
  arXiv  Detail & Related papers  (2024-01-11T08:36:15Z)
• Inversion-Free Image Editing with Natural Language [18.373145158518135]
  We present inversion-free editing (InfEdit), which allows for consistent and faithful editing for both rigid and non-rigid semantic changes. InfEdit shows strong performance in various editing tasks and also maintains a seamless workflow (less than 3 seconds on one single A40), demonstrating the potential for real-time applications.
  arXiv  Detail & Related papers  (2023-12-07T18:58:27Z)

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.