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.

翻译：扩散模型在图像生成与编辑领域已取得显著成功。广泛认为，在扩散模型中采用更大的反演与去噪步长有助于提升图像重建质量。然而，即使增加去噪步数，扩散模型的编辑性能仍难以令人满意。这种编辑缺陷可能归因于编辑过程中的条件马尔可夫性质，即误差会随去噪步骤不断累积。为解决该挑战，我们首先提出一种创新框架，其中引入整流模块，利用残差特征调节扩散模型权重，从而提供补偿信息以弥合保真度差距。此外，我们提出一种新型学习范式，旨在最小化编辑过程中的误差传播——该范式以类似去噪分数匹配的方式训练编辑流程。大量实验表明，我们提出的框架与训练策略能够在不同去噪步数下实现高保真度的重建与编辑结果，同时在定量指标与定性评估中均展现出卓越性能。最后，我们通过图像到图像翻译、域外图像编辑等多种应用探索了模型的泛化能力。