Trade-Offs in Image Generation: How Do Different Dimensions Interact?

Model performance in text-to-image (T2I) and image-toimage (I2I) generation often depends on multiple aspects, including quality, alignment, diversity, and robustness. However, models' complex trade-offs among these dimensions have been rarely explored due to (1) the lack of datasets that allow fine-grained quantification of these trade-offs, and (2) using a single metric for multiple dimensions. To address this gap, we introduce TRIG-Bench (Trade-offs in Image Generation), which spans 10 dimensions (Realism, Originality, Aesthetics, Content, Relation, Style, Knowledge, Ambiguity, Toxicity and Bias), contains 40,200 samples, and covers 132 Pairwise Dimensional Subsets. Furthermore, we develop TRIGScore, a VLM-as-judge metric that automatically adapts to various dimensions. Based on TRIG-Bench and TRIGScore, we evaluate 14 cuttingedge models across T2I and I2I tasks. In addition, we propose the Relation Recognition System and generate the Dimension Trade-off Map (DTM), which visualizes modelspecific capability trade-offs. Our experiments demonstrate that DTM consistently provides a comprehensive under-standing of the trade-offs between dimensions for each type of generation model. Notably, after fine-tuning on DTM, the model's dimension-specific impact is mitigated, and overall performance is enhanced. Code is available at: https://github.com/fesvhtr/TRIG.