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 inversion features
from the key and value features in the self-attention layers, and enhances the
sampling process by these inversion features, thus achieving accurate
reconstruction and content-consistent editing. To extend the applicability of
our method to general editing scenarios, we also propose a mask-guided
attention concatenation strategy that combines contents from both the inversion
and the naive DDIM editing processes. Experiments show that the proposed method
outperforms previous works in reconstruction and consistent editing, and
produces impressive results in various settings.

提出了一种名为 Tuning-free Inversion-enhanced Control (TIC) 的新方法，通过直接关联反演过程和采样过程的特征来缓解 DDIM 重建中的不一致性，实现准确的重建和内容一致性编辑。通过实验证明，该方法在重建和一致性编辑方面优于以前的工作，在各种设置中产生了令人印象深刻的结果。

基于无需调优的增强逆控制的一致图像编辑

Tuning-Free Inversion-Enhanced Control for Consistent Image Editing

Image editing approaches with diffusion models have been rapidly developed,
yet their applicability are subject to requirements such as specific editing
types (e.g., foreground or background object editing, style transfer), multiple
conditions (e.g., mask, sketch, caption), and time consuming fine-tuning of
diffusion models. For alleviating these limitations and realizing efficient
real image editing, we propose a novel editing technique that only requires an
input image and target text for various editing types including non-rigid edits
without fine-tuning diffusion model. Our method contains three novelties:(I)
Target-text Inversion Schedule (TTIS) is designed to fine-tune the input target
text embedding to achieve fast image reconstruction without image caption and
acceleration of convergence.(II) Progressive Transition Scheme applies
progressive linear interpolation between target text embedding and its
fine-tuned version to generate transition embedding for maintaining non-rigid
editing capability.(III) Balanced Attention Module (BAM) balances the tradeoff
between textual description and image semantics.By the means of combining
self-attention map from reconstruction process and cross-attention map from
transition process, the guidance of target text embeddings in diffusion process
is optimized.In order to demonstrate editing capability, effectiveness and
efficiency of the proposed BARET, we have conducted extensive qualitative and
quantitative experiments. Moreover, results derived from user study and
ablation study further prove the superiority over other methods.

通过目标文本翻译方案、渐进式过渡方案和平衡关注模块，我们提出了一种新的图像编辑技术，实现了包括非刚性编辑在内的各种编辑类型，并提高了图像编辑的效率和准确性。