Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images. However, their application to editing real images often encounters hurdles primarily due to the text condition deteriorating the reconstruction quality and subsequently affecting editing fidelity. Null-text Inversion (NTI) has made strides in this area, but it fails to capture spatial context and requires computationally intensive per-timestep optimization. Addressing these challenges, we present Noise Map Guidance (NMG), an inversion method rich in a spatial context, tailored for real-image editing. Significantly, NMG achieves this without necessitating optimization, yet preserves the editing quality. Our empirical investigations highlight NMG's adaptability across various editing techniques and its robustness to variants of DDIM inversions.

翻译：文本引导的扩散模型已成为图像合成领域的常用工具，因其能生成高质量且多样化的图像而闻名。然而，这类模型在应用于真实图像编辑时常面临障碍，主要由于文本条件会降低重建质量，进而影响编辑保真度。空文本反演（NTI）在该领域取得了进展，但该方法无法捕捉空间上下文，且需要计算密集的逐时间步优化。针对这些挑战，我们提出噪声图引导（NMG）——一种富含空间上下文的反演方法，专为真实图像编辑设计。显著的是，NMG在无需优化的情况下实现了这一目标，同时保持了编辑质量。我们的实证研究凸显了NMG对各种编辑技术的适应性及其对DDIM反演变体的鲁棒性。