Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling

• URL: http://arxiv.org/abs/2506.21045v1
• Date: Thu, 26 Jun 2025 06:46:03 GMT
• Title: Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling
• Authors: Hansam Cho, Seoung Bum Kim,
• Abstract summary: In image editing, two crucial aspects are editability, which determines the extent of modification, and faithfulness, which reflects how well unaltered elements are preserved.<n>We propose Faithfulness Guidance and Scheduling (FGS), which enhances faithfulness with minimal impact on editability.<n> Experimental results demonstrate that FGS achieves superior faithfulness while maintaining editability.
• Score: 1.8876415010297893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial aspects are editability, which determines the extent of modification, and faithfulness, which reflects how well unaltered elements are preserved. However, achieving optimal results is challenging because of the inherent trade-off between editability and faithfulness. To address this, we propose Faithfulness Guidance and Scheduling (FGS), which enhances faithfulness with minimal impact on editability. FGS incorporates faithfulness guidance to strengthen the preservation of input image information and introduces a scheduling strategy to resolve misalignment between editability and faithfulness. Experimental results demonstrate that FGS achieves superior faithfulness while maintaining editability. Moreover, its compatibility with various editing methods enables precise, high-quality image edits across diverse tasks.

Related papers

• 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)
• Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model [60.82962950960996]
  We introduce UnifyEdit, a tuning-free method that performs diffusion latent optimization.<n>We develop two attention-based constraints: a self-attention (SA) preservation constraint for structural fidelity, and a cross-attention (CA) alignment constraint to enhance text alignment.<n>Our approach achieves a robust balance between structure preservation and text alignment across various editing tasks, outperforming other state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-08T01:02:50Z)
• Lost in Edits? A $λ$-Compass for AIGC Provenance [119.95562081325552]
  We propose a novel latent-space attribution method that robustly identifies and differentiates authentic outputs from manipulated ones.<n>LambdaTracer is effective across diverse iterative editing processes, whether automated through text-guided editing tools such as InstructPix2Pix or performed manually with editing software such as Adobe Photoshop.
  arXiv  Detail & Related papers  (2025-02-05T06:24:25Z)
• Edicho: Consistent Image Editing in the Wild [90.42395533938915]
  Edicho steps in with a training-free solution based on diffusion models.<n>It features a fundamental design principle of using explicit image correspondence to direct editing.
  arXiv  Detail & Related papers  (2024-12-30T16:56:44Z)
• Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing [42.45138713525929]
  Effective editing requires inverting the source image into a latent space, a process often hindered by prediction errors inherent in DDIM inversion. We introduce the Logistic Schedule, a novel noise schedule designed to eliminate singularities, improve inversion stability, and provide a better noise space for image editing. Our approach requires no additional retraining and is compatible with various existing editing methods.
  arXiv  Detail & Related papers  (2024-10-24T14:07:02Z)
• Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing [60.730661748555214]
  We introduce textbfTask-textbfOriented textbfDiffusion textbfInversion (textbfTODInv), a novel framework that inverts and edits real images tailored to specific editing tasks. ToDInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability.
  arXiv  Detail & Related papers  (2024-08-23T22:16:34Z)
• FlexiEdit: Frequency-Aware Latent Refinement for Enhanced Non-Rigid Editing [22.308638156328968]
  DDIM latent, crucial for retaining the original image's key features and layout, significantly contribute to limitations. We introduce FlexiEdit, which enhances fidelity to input text prompts by refining DDIM latent. Our approach represents notable progress in image editing, particularly in performing complex non-rigid edits.
  arXiv  Detail & Related papers  (2024-07-25T08:07:40Z)
• DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing [66.43179841884098]
  Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. We propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing. Our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks.
  arXiv  Detail & Related papers  (2024-02-04T18:50:29Z)
• Optimisation-Based Multi-Modal Semantic Image Editing [58.496064583110694]
  We propose an inference-time editing optimisation to accommodate multiple editing instruction types. By allowing to adjust the influence of each loss function, we build a flexible editing solution that can be adjusted to user preferences. We evaluate our method using text, pose and scribble edit conditions, and highlight our ability to achieve complex edits.
  arXiv  Detail & Related papers  (2023-11-28T15:31:11Z)

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.