Content-Adaptive Aesthetic Alignment for Text-to-Image Generation

Aesthetic evaluation for text-to-image (T2I) generation must distinguish an inap- plicable criterion from a poorly realized one. To assess diverse visual content, we define a taxonomy of six aesthetic criteria, *i.e.*, realism, lighting, anatomy, creativ- ity, color harmony, and typography, and construct an aesthetic preference corpus of approximately 240K distinct images and 840K pairwise comparisons from hu- man and GPT-assisted criterion-specific rankings. Based on this corpus, we train **A**pplicability-Aware **W**eighted **A**esthetic **RE**ward (AWARE), which jointly pre- dicts quality and applicability for all six dimensions in one model. We then use AWARE to design an applicability-aware reinforcement-learning algorithm for text-to-image post-training, dynamically selecting and weighting only the crite- ria relevant to each sample. To evaluate both aesthetic judgment and post-trained generation, we introduce AWARE-RBench for criterion-conditioned preference and applicability evaluation, and AWARE-GenBench for role-conditional T2I aes- thetic quality. AWARE improves over the baseline by 60.92% on the aesthetic- model evaluation. Across Z-Image and SenseNova-U1.5, post-trained T2I models achieve average relative gains of 19.01%, 21.72%, and 2.39% on Photography, Artwork, and Infographic, respectively. On general-purpose benchmarks such as Qwen-Image-Bench, the aligned models remain competitive with existing open- source systems. Side-by-side visual comparisons further confirm improvements in fidelity, composition, natural lighting, and text rendering.