Focusing on conditioning with energy above the hull, the MP-20 training dataset includes materials with energy above hull values ranging from 0 to 0.08 eV/atom, representing a spectrum of stable materials. For generation, we uniformly sampled 1,000 instances from this range as conditioning criteria to guide the model in generating crystal structures conditioned on energy above the hull values. Among these generated samples, 983 were confirmed to be both compositionally and structurally valid. Our study compares the distribution of lattice parameters a, b, c, a, b, g and space group number between the training and generated samples. To assess the alignment and variation of these distributions, we calculated the KL divergence and Earth Mover's Distance (EMD). We evaluated the diversity and novelty of the generated crystal structures relative to the training data using compositional and structural features. Compositional features were extracted using the ElementProperty featurizer from Matminer , which encodes elemental properties based on the Magpie set of descriptors . Structural features were generated using the CrystalNNFingerprint featurizer , producing site-level fingerprints that were averaged to create structure-level representations. Both feature types were standardized using the training data statistics to ensure uniform scaling and fair comparison across datasets. Diversity was calculated as the average pairwise Euclidean distance within the training and generated datasets, providing a measure of how varied the samples are. Novelty was assessed as the minimum distance between each generated sample and the training data, offering insights into the uniqueness of the generated structures compared to known materials.