Noise Map Guidance (NMG) is an inversion method designed for real-image editing that incorporates spatial context. The paper introduces NMG as an optimization-free approach that preserves the spatial context of input images during editing. Unlike Null-text Inversion (NTI), which requires per-timestep optimization, NMG conditions the reverse process using noise maps derived from DDIM inversion. These noise maps, which are noisy representations of the input image, inherently capture spatial context, allowing NMG to maintain the original image's spatial characteristics during editing. NMG is combined with various editing techniques, including Prompt-to-Prompt, MasaCtrl, and pix2pix-zero, demonstrating its versatility and effectiveness in real-image editing. The method is also robust to variations of DDIM inversion, as shown in experiments comparing NMG with other inversion methods like NTI, NPI, and ProxNPI. Quantitative evaluations using metrics such as CLIPScore and TIFA, as well as user studies, confirm that NMG consistently outperforms other methods in preserving spatial context and maintaining editing quality. The paper also includes ablation studies showing that NMG's performance is influenced by guidance scales and that it achieves superior results compared to optimization-based methods. Overall, NMG offers a significant advancement in real-image editing by effectively preserving spatial context without requiring optimization.Noise Map Guidance (NMG) is an inversion method designed for real-image editing that incorporates spatial context. The paper introduces NMG as an optimization-free approach that preserves the spatial context of input images during editing. Unlike Null-text Inversion (NTI), which requires per-timestep optimization, NMG conditions the reverse process using noise maps derived from DDIM inversion. These noise maps, which are noisy representations of the input image, inherently capture spatial context, allowing NMG to maintain the original image's spatial characteristics during editing. NMG is combined with various editing techniques, including Prompt-to-Prompt, MasaCtrl, and pix2pix-zero, demonstrating its versatility and effectiveness in real-image editing. The method is also robust to variations of DDIM inversion, as shown in experiments comparing NMG with other inversion methods like NTI, NPI, and ProxNPI. Quantitative evaluations using metrics such as CLIPScore and TIFA, as well as user studies, confirm that NMG consistently outperforms other methods in preserving spatial context and maintaining editing quality. The paper also includes ablation studies showing that NMG's performance is influenced by guidance scales and that it achieves superior results compared to optimization-based methods. Overall, NMG offers a significant advancement in real-image editing by effectively preserving spatial context without requiring optimization.