## 2 Related Work
[27] were purely theoretical and devoid of practical application; therefore, our research transcends qualitative examination by incorporating the practical implementation of AIGC with cutting-edge models such as SD. [PERSON] [28] examined image synthesis (image production) from an ethical perspective. Elements of creation can also facilitate destruction, which is why [PERSON] [28] advocated the ethical application of generative AI in image synthesis. The significant potential of image synthesis models entails a considerable duty to guarantee ethical utilization. [PERSON] [28] used the Midjourney, SD, and DALL-E2 models to analyze the topic from the point of view of risk and opportunity. In a comparable context, [PERSON] et al. [28] addressed the meticulous supervision required to realize the potential of generative AI in precise medical diagnostics and neurointerventional surgery while mitigating the risk of encountering pitfalls. Both works lack practical application and are purely theoretical. However, ethical considerations were implemented during the construction and testing of the models in our research. [PERSON] et al. [29] conducted a comparative examination of SD, Midjourney, and DALL-E, advocating for their implementation due to their educational use. The study demonstrated that neural network applications can improve the teaching of students in design-related fields. [PERSON] et al. [PERSON] [23] developed a depth estimator utilizing an extensive dataset of synthetic images produced through a precise forward model of an endoscope and an anatomically accurate colon. Their studies revealed the following enhancements in porcine colon data: an 88% improvement in Structural Similarity Index Measure (SSIM) for depth estimation, a 48% improvement in SSIM for phantom colon data depth estimation, and approximately a 125% improvement compared to the prior dictionary learning method. Additional validation of various medical imaging tasks and modalities would enhance the broader application. [PERSON] et al. [16] developed a synthetic image generation model using Conditional GAN for single-sample face images. The research achieved an initial accuracy of 76% on individual neutral images and a final accuracy of 99% after the fine-tuning of synthetic expressions, representing a 23% improvement over the original accuracy recorded. This method presents possibilities to address the difficulty of Single Sample Per Person (SSPP) in facial recognition systems. However, it is limited solely to variations in expression and can necessitate the integration of additional motions to capture other forms of facial alterations for applicability in different contexts. [PERSON] et al. [14] used the Adaptive refinement semantic diffusion model for the synthesis of colonoscopy images. Polyp segmentation exhibited the greatest enhancement with PraNet (6.0% mDice, 5.7% mIoU), succeeded by SANet and Polyp-PVT. CenterNet demonstrated the most significant gain in average precision (9.1%) and the greatest improvement in the F1 score (6.1%) for the diagnosis of polyps. The research indicates that the use of synthetic images generated by ArSDM for training significantly enhances the results in both polyp segmentation and detection tasks. The methodology effectively addresses weakly annotated medical imaging data. [PERSON] et al. [15] present a novel methodology for text-to-image synthesis that improves image quality and adherence to textual descriptions compared to previous methods. Compared to the Caltech-UCSD Birds-200-2011 (CUB) dataset, the model improved the Inception score by 17.6% and decreased the Frechet Inception Distance by 36.6% relative to the GAN-INT-CLS model based on cGAN. The model is limited to bird images at a relatively low resolution (128 \(\times\) 128) and was assessed only on a single dataset, CUB. [PERSON] et al. [9] employed three methodologies for medical image synthesis: DB + LoRA, fine-tuned SD, and CLIP.