# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 5 Experiment
### Comparisons  
Qualitative Comparisons: Figure 6 illustrates the qualitative comparison results with Edit Friendly DDPM Inv [8], InstructPix2 Pix [6], and Prompt-to-Prompt [7]. Specifically, while Edit Friendly DDPM Inv provides quick editing results, it often leads to unnatural \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **CLIP Score \(\uparrow\)** & **LPIPS \(\downarrow\)** & **FID \(\downarrow\)** \\ \hline InstructPix2 Pix & 0.328 & 0.400 & 26.7 \\ Prompt-to-Prompt & 0.325 & 0.389 & 23.8 \\ Edit Friendly DDPM inv. & 0.329 & 0.270 & 21.4 \\ Ours & 0.343 & 0.294 & 20.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of our method with the state-of-the-art methods on CLIP Score, FID, LPIPS. Figure 6: Qualitative comparison of our method with the state-of-the-art methods. Our results are shown in the last column, and it is clear that our method performs well in preserving the details of the original image while accurately understanding and executing the editing instructions. edits and, in some cases, fails to comprehend the editing instructions fully, causing the edits to be ineffective (Figure 6 column 2). In contrast, InstructPix2 Pix tends to lose detail in complex scenes, and the uncertainty in the editing instructions makes it challenging to preserve the overall style of the input image (Figure 6 column 3). Finally, Prompt-to-Prompt's reliance on text prompts affects the fidelity of the edited images, leading to significant changes in details (Figure 6 column 4). These limitations make it difficult for these methods to achieve high precision and subtle image edits. In contrast, our method produces more natural and smooth edits while maintaining the overall structure and style of the input image. It retains the key features of the input image and adheres to the editing instructions, ensuring high precision and detailed control in the edited output. Quantitative Comparisons: Table 1 compares our method with several existing techniques, including An Edit Friendly DDPM Inv [8], InstructPix2 Pix [6], and Prompt-to-Prompt [7], based on quantitative metrics. We evaluated 1000 test images using two key metrics: LPIPS (measuring fidelity to the source image, with lower values indicating higher fidelity), CLIP score (measuring the consistency between the generated image and the target text, with higher values indicating better alignment), and FID (a measure of distance between two distributions of image sets, where a lower FID score implies that the distribution of generated images is closer to that of real images). According to the data presented in the table, our method achieves state-of-the-art FID and CLIP scores while maintaining a relatively low LPIPS score, indicating that our editing results maintain high fidelity to the source image and are highly consistent with the editing instructions. This balance highlights the effectiveness of our approach in providing precise editing with detail preservation. The CLIP score is significant in tasks requiring high precision and detail in image editing [35], as it reflects the model's ability to understand and execute the editing instructions. A higher CLIP score indicates that the system better captures the user's intent and applies it effectively to the image editing process. The CLIP (Contrastive Language-Image Pretraining) score is commonly used to evaluate the quality of image generation or editing models. CLIP is a pretrained model that learns representations of both text and images simultaneously from a large number of text-image pairs through contrastive learning. The CLIP score is obtained by calculating the similarity between generated images and the text that describes them. This score measures how well the generated image matches the target description. A higher score indicates greater relevance between the image and the text. LPIPS (Learned Perceptual Image Patch Similarity) is a metric for assessing perceptual differences between two images.