The performance of deep learning models in medical image analysis critically depends on the access to large and high-quality datasets. However, ethical, legal, and privacy constraints often limit data availability. Generative models offer a promising solution by producing synthetic training data, yet their resource-efficient fine-tuning remains an open challenge. This study investigates whether stable diffusion (SD) can be adapted using low-rank adaptation (LoRA) with minimal data and computational resources to generate synthetic hand radiographs (X-rays). The aim is not perfect anatomical fidelity but the reproduction of key X-ray characteristics under restrictive conditions. Quantitative and qualitative comparisons of a generic and a medically pre-trained SD 1.4 model showthat both can produce visually plausibleX-rays despite anatomical imperfections. These findings demonstrate the potential of lightweight fine-tuning methods for medical imaging and underscore the need for systematic research on training efficiency, quality assessment of synthetic data, and integration of synthetic and real datasets in medical AI.