# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 1 Introduction  
Diffusion Models (DMs) [1, 2, 3] have achieved remarkable breakthroughs and widespread recognition in the generative artificial intelligence community as a cutting-edge deep learning generative technology. DMs approach the real-world data distribution through iterative denoising of random Gaussian noise, thereby enabling generative tasks. Their applications include image synthesis [4, 5], image editing [6, 7, 8, 9], image restoration [10, 11], video generation [12, 13], and more [14, 15, 16, 17, 18]. Notable examples are OpenAI's DALL-E series [19, 20, 21], Google's Imagen [22], and the recently famous Sora [23]. These systems built on DMs demonstrate astonishing creative capabilities. Although the synthesis performance of DMs has surpassed many other generative models [24], such as Generative Adversarial Networks (GANs) [25], DMs still have highcomputational requirements. This is reflected in two aspects: on the one hand, the DMs have a huge model architecture. On the other hand, the iterative denoising method of DMs requires multi-step inference. This makes Stable Diffusion (SD) [26] stand out among numerous generative models by reducing the computational cost of DMs. The SD method converts the image into a latent space and then performs the diffusion process on this more efficient latent space. Another area for improvement with DMs is that they cannot control the generated results. Although SD supports text input to control the generated results, it still cannot provide precise control over the generated results. The emergence of ControlNet [27] addresses the need for precise control over generated results. ControlNet is an innovative solution designed to enhance the controllability of SD's output. By introducing additional conditional control mechanisms, ControlNet provides users with more excellent control capabilities during the generation process in a plug-and-play manner. These additional conditions include edge maps, human pose skeletons, segmentation masks, depth maps, and normals. This advancement not only enhances the practicality of SD but also indicates that DMs with precise controllability have broad application prospects in various fields, such as image processing, personalized content creation, advertising design, and filmmaking [28; 29; 30; 31]. In image processing applications with precisely controllable DMs [32; 33; 34], high-precision image editing is exciting. High-precision image editing typically refers to keeping parts of the image other than the modified areas unchanged during the editing process. Specifically, details such as background, edges, and textures should remain intact, and the edited image must be consistent with the internal structure of the original image, including object positions, proportions, and lighting. At the same time, the editing results should align closely with the editing instructions [35]. Despite being praised for their boundless creativity and deep semantic capabilities, large-scale text-to-image DMs reveal limitations when directly intervening in images and achieving high-precision edits. Specifically, these models show insufficient flexibility when handling tasks that require delicate edits in specific regions of an image, primarily due to their extreme sensitivity to textual instructions. Even minor variations in input prompts can lead to significant changes in the output image, creating obstacles for implementing high-precision image editing tasks. Some technical approaches have recently enhanced image editing quality by introducing additional control modules or fine-tuning existing models [6; 27; 36; 37]. For instance, certain methods enhance specific control over editing results by incorporating dedicated control modules during training, improving overall image editing quality. Other methods include fine-tuning models for specific editing tasks to enhance the quality of edits.