Diffusion models have attained remarkable success in the domains of image
generation and editing. It is widely recognized that employing larger inversion
and denoising steps in diffusion model leads to improved image reconstruction
quality. However, the editing performance of diffusion models tends to be no
more satisfactory even with increasing denoising steps. The deficiency in
editing could be attributed to the conditional Markovian property of the
editing process, where errors accumulate throughout denoising steps. To tackle
this challenge, we first propose an innovative framework where a rectifier
module is incorporated to modulate diffusion model weights with residual
features, thereby providing compensatory information to bridge the fidelity
gap. Furthermore, we introduce a novel learning paradigm aimed at minimizing
error propagation during the editing process, which trains the editing
procedure in a manner similar to denoising score-matching. Extensive
experiments demonstrate that our proposed framework and training strategy
achieve high-fidelity reconstruction and editing results across various levels
of denoising steps, meanwhile exhibits exceptional performance in terms of both
quantitative metric and qualitative assessments. Moreover, we explore our
model's generalization through several applications like image-to-image
translation and out-of-domain image editing.

扩散模型在图像生成和编辑领域取得了显著的成功。我们提出了一种创新的框架，其中包含一个修正模块，用残差特征调节扩散模型权重，以填补编辑过程中准确性的差距。此外，我们引入了一种新的学习范式，旨在在编辑过程中最小化错误传播。通过大量实验证明，我们的提议框架和训练策略在各种去噪步骤下实现了高保真的重建和编辑结果，并在定量指标和质量评估方面表现出色。此外，我们还通过图像到图像的转换和跨领域图像编辑等多个应用探索了模型的泛化能力。