Deep generative models have shown promising potential in the medical field by providing synthetic data to help address data scarcity caused by privacy concerns or high annotation costs. Anatomy-conforming images can be synthesized using semantically conditioned image synthesis models. Recent state-of-the-art models perform the synthesis process in a compressed latent space to enable the generation of high-resolution 3D images. However, synthesizing fine-grained tubular structures such as vessels remains a significant challenge. In this paper, we propose a 3D latent generative model with semantic and tubular-aware conditioning. Our tubular-aware conditioning module leverages a custom cross-attention-based vessel encoding scheme to incorporate fine-grained structural information. We assess its performance on 3D \(256 \times 256 \times 128\) coronary CTA images. Experimental evaluation demonstrates its superiority over conventional conditioning methods regarding the preservation of vessel structures. These results highlight the potential of our method and suggest that more advanced conditioning strategies, such as explicit modeling of tubular-structure-specific anomalies or fine details, could be explored in future work. Code available at: https://github.com/pshavela/tubular-aware-lfm .