Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities. However, applying these methods to real images necessitates the inversion of the images into the domain of the pretrained diffusion model. Achieving faithful inversion remains a challenge, particularly for more recent models trained to generate images with a small number of denoising steps. In this work, we introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. Building on reversing the diffusion sampling process, our method employs an iterative renoising mechanism at each inversion sampling step. This mechanism refines the approximation of a predicted point along the forward diffusion trajectory, by iteratively applying the pretrained diffusion model, and averaging these predictions. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models. Through comprehensive evaluations and comparisons, we show its effectiveness in terms of both accuracy and speed. Furthermore, we confirm that our method preserves editability by demonstrating text-driven image editing on real images.

翻译：近期，文本引导扩散模型的进展解锁了强大的图像编辑能力。然而，将这些方法应用于真实图像时，需先将图像反演至预训练扩散模型的域中。实现高保真反演仍是一项挑战，尤其是对于近期训练生成少量去噪步的图像模型。本文提出一种具有高质量-操作比的反演方法，在不增加操作数量的前提下提升重建精度。该方法基于逆向扩散采样过程，在每次反演采样步中引入迭代重加噪机制——通过反复应用预训练扩散模型并平均其预测结果，逐步优化沿前向扩散轨迹的预测点近似值。我们采用多种采样算法和模型（包括近期加速扩散模型）评估所提ReNoise技术的性能。通过全面的评估与比较，证明了该方法在精度与速度两方面的有效性。此外，通过真实图像上的文本驱动图像编辑实验，验证了该方法保留可编辑性的能力。