## 4 Results
### Image Generation Results  
The synthesized images were measured using the inception score and the Frechet Inception Distance. Among the three models, CLIP has the highest Frechet FID score: 0.114 for 0.128 and 0.124 for three (3) datasets [9]. These higher ratings indicate poorer image quality and realism as CLIP-generated images seem less like real photos than other models. Figure 4 shows the final image. The higher FID score for the multicenter dataset indicates that CLIP struggles to create realistic images when trained on a diverse dataset from numerous medical facilities. High FID scores indicate that in this case, CLIP is not the best approach to obtain high-quality medical images. The images in Figure 4 demonstrate \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Model** & **FID** & **IS avg** & **IS std** & **IS med** \\ \hline single & CLIP & 0.11 & 1.57 & 0.03 & 1.56 \\ multi & CLIP & 0.11 & 1.57 & 0.03 & 1.56 \\ both & CLIP & 0.12 & 1.57 & 0.03 & 1.56 \\ single & SD & 0.06 & 2.33 & 0.07 & 2.34 \\ multi & SD & 0.07 & 2.33 & 0.07 & 2.34 \\ single & DB+LoRa & 0.11 & 2.36 & 0.05 & 2.36 \\ multi & DB+LoRa & 0.07 & 2.36 & 0.05 & 2.36 \\ both & DB+LoRa & 0.08 & 2.36 & 0.05 & 2.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different models across datasets. The evaluation metrics include FID score (lower is better) and Inception score (IS) measurements, including average (avg), standard deviation (std), and median (med) values. \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline
**Dataset** & **Model** & **G1** & **G2** & **G3** & **G4** & **G5** & **G6** & **G7** & **G8** & **G9** & **G10** \\ \hline single & CLIP & 1.56 & 1.55 & 1.55 & 1.57 & 1.55 & 1.55 & 1.54 & 1.54 & 1. CLIP's capability to generate synthetic colonoscopy images from textual descriptions, though with notable quality limitations when trained on diverse medical facility datasets. The implementation combines Stable Diffusion pre-trained, DreamBooth, and LoRA for the synthesis of colonoscopy images, optimized with a resolution of 512 pixels, a learning rate of \(1\times e^{-4}\), and specialized medical prompts. This architecture efficiently handles medical imaging complexity while minimizing computational resources through LoRA's targeted modifications and DreamBooth's instance-specific adaptations. Indicating better image quality and realism, the lowest FID scores for fine-tuned SD are \"0.099\" (single center), \"0.064\" (multicenter), and \"0.067\" (combined datasets) [9]. The reduced score of the multicenter dataset implies better performance of healthcare facilities. Although somewhat inferior to fine-tuned DB + LoRa, SD offers a high amount of visual diversity and quality, averaging \"2.33\" in all datasets. The consistency of SD across datasets shows its ability to function regardless of the data source. In general, fine-tuned SD produces high-quality, diversified medical images. Figure 5 shows the synthetic images that demonstrate SD's ability to generate diverse high-quality medical images with FID scores of 0.064-0.099 across different datasets, showing consistent performance regardless of data source. The FID scores of \"0.11\" (single center), \"0.073\" (multicenter), and \"0.076\" (combined datasets) obtained by fine-tuning DB + LoRa show a high level of image quality and accuracy. With a wider range of training data, the multicenter score is more favorable. However, scores are higher than for SD, which has been fine-tuned, indicating a good level of quality overall. Regardless of the data source, the Inception score of \"2.36\" remains constant across all datasets, indicating strong image diversity and quality [9]. Figure 6 shows the result of the text-to-image synthesis using DB and LoRa. The synthetic images demonstrate the model's ability to generate colonoscopy images in response to specific text prompts, with each pair showing the relationship between real patient images and their synthetic counterparts.