DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing

• URL: http://arxiv.org/abs/2506.02560v1
• Date: Tue, 03 Jun 2025 07:46:44 GMT
• Title: DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing
• Authors: Zixiang Li, Haoyu Wang, Wei Wang, Chuangchuang Tan, Yunchao Wei, Yao Zhao,
• Abstract summary: Inversion within Diffusion models aims to recover the latent noise representation for a real or generated image.<n>Most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility.<n>We introduce Dual-Conditional Inversion (DCI), a novel framework that jointly conditions on the source prompt and reference image.
• Score: 73.12011187146481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, we introduce Dual-Conditional Inversion (DCI), a novel framework that jointly conditions on the source prompt and reference image to guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading to more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-art performance across multiple editing tasks, significantly improving both reconstruction quality and editing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process.

Related papers

• MambaStyle: Efficient StyleGAN Inversion for Real Image Editing with State-Space Models [60.110274007388135]
  MambaStyle is an efficient single-stage encoder-based approach for GAN inversion and editing.<n>We show that MambaStyle achieves a superior balance among inversion accuracy, editing quality, and computational efficiency.
  arXiv  Detail & Related papers  (2025-05-06T20:03:47Z)
• Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing [66.48853049746123]
  We analyze reconstruction from a structural perspective and propose a novel approach that replaces traditional cross-attention with uniform attention maps.<n>Our method effectively minimizes distortions caused by varying text conditions during noise prediction.<n> Experimental results demonstrate that our approach not only excels in achieving high-fidelity image reconstruction but also performs robustly in real image composition and editing scenarios.
  arXiv  Detail & Related papers  (2024-11-29T12:11:28Z)
• ERDDCI: Exact Reversible Diffusion via Dual-Chain Inversion for High-Quality Image Editing [20.46262679357339]
  Diffusion models (DMs) have been successfully applied to real image editing. Recent popular DMs often rely on the assumption of local linearization. ERDDCI uses the new Dual-Chain Inversion (DCI) for joint inference to derive an exact reversible diffusion process.
  arXiv  Detail & Related papers  (2024-10-18T07:52:03Z)
• 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)
• Tuning-Free Inversion-Enhanced Control for Consistent Image Editing [44.311286151669464]
  We present a novel approach called Tuning-free Inversion-enhanced Control (TIC) TIC correlates features from the inversion process with those from the sampling process to mitigate the inconsistency in DDIM reconstruction. We also propose a mask-guided attention concatenation strategy that combines contents from both the inversion and the naive DDIM editing processes.
  arXiv  Detail & Related papers  (2023-12-22T11:13:22Z)
• Spatial-Contextual Discrepancy Information Compensation for GAN Inversion [67.21442893265973]
  We introduce a novel spatial-contextual discrepancy information compensationbased GAN-inversion method (SDIC) SDIC bridges the gap in image details between the original image and the reconstructed/edited image. Our proposed method achieves the excellent distortion-editability trade-off at a fast inference speed for both image inversion and editing tasks.
  arXiv  Detail & Related papers  (2023-12-12T08:58:56Z)
• Effective Real Image Editing with Accelerated Iterative Diffusion Inversion [6.335245465042035]
  It is still challenging to edit and manipulate natural images with modern generative models. Existing approaches that have tackled the problem of inversion stability often incur in significant trade-offs in computational efficiency. We propose an Accelerated Iterative Diffusion Inversion method, dubbed AIDI, that significantly improves reconstruction accuracy with minimal additional overhead in space and time complexity.
  arXiv  Detail & Related papers  (2023-09-10T01:23:05Z)
• ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing [20.39792009151017]
  StyleGAN allows for flexible and plausible editing of generated images by manipulating the semantic-rich latent style space. Projecting a real image into its latent space encounters an inherent trade-off between inversion quality and editability. We propose a novel two-phase framework by designating two separate networks to tackle editing and reconstruction respectively.
  arXiv  Detail & Related papers  (2023-01-31T04:38:42Z)
• Editing Out-of-domain GAN Inversion via Differential Activations [56.62964029959131]
  We propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. With the aid of the generated Diff-CAM mask, a coarse reconstruction can intuitively be composited by the paired original and edited images. In the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction.
  arXiv  Detail & Related papers  (2022-07-17T10:34:58Z)
• High-Fidelity GAN Inversion for Image Attribute Editing [61.966946442222735]
  We present a novel high-fidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved. With a low bit-rate latent code, previous works have difficulties in preserving high-fidelity details in reconstructed and edited images. We propose a distortion consultation approach that employs a distortion map as a reference for high-fidelity reconstruction.
  arXiv  Detail & Related papers  (2021-09-14T11:23:48Z)

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.