This paper provides a dedicated case study of the stylegan2-ada implementation on fashion images generation with limited data. We tested the capability of the model using the ZEN dataset to produce high-quality and diverse fashion images with a comparatively small training corpus. Compared to the baseline with StyleGAN2, it is shown that the model is unstable in the absence of ADA and fails to produce high-quality images (FID \( > 80\) ), whereas the StyleGAN2-ADA baseline converges steadily with significantly better results (FID = 10.8). We also point out instances of failure that are common like poor fabric textures and color bleeding and we also recognize the weaknesses of the model together with its strength. Presenting this work as a case study, we will offer useful information as to the usefulness of StyleGAN2-ADA in fashion synthesis tasks that are conducted using limited data, and critically assess this to inform future research and advancements.