Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
Xiaoyue Duan, Shuhao Cui, Guoliang Kang, Baochang Zhang, Zhengcong, Fei, Mingyuan Fan, Junshi Huang

TL;DR
This paper introduces TIC, a tuning-free method that enhances real image editing by improving reconstruction accuracy and content consistency through inversion features and mask-guided attention, outperforming previous approaches.
Contribution
The paper proposes a novel tuning-free inversion-enhanced control method that correlates inversion and sampling features for consistent image editing without fine-tuning.
Findings
Outperforms previous methods in reconstruction quality
Achieves more content-consistent editing results
Effective in various real-world editing scenarios
Abstract
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e.g., changing postures) to the main objects in the input image without changing their identity or attributes. To guarantee consistent attributes, some existing methods fine-tune the entire model or the textual embedding for structural consistency, but they are time-consuming and fail to perform non-rigid edits. Other works are tuning-free, but their performances are weakened by the quality of Denoising Diffusion Implicit Model (DDIM) reconstruction, which often fails in real-world scenarios. In this paper, we present a novel approach called Tuning-free Inversion-enhanced Control (TIC), which directly correlates features from the inversion process with those from the sampling process to mitigate the inconsistency in DDIM reconstruction. Specifically, our method effectively obtains…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Vision and Imaging · Advanced Image and Video Retrieval Techniques
MethodsDiffusion
