Achieving compositional alignment between textual descriptions and generated images - covering objects, attributes, and spatial relationships - remains a core challenge for modern text-to-image (T2I) models. Although diffusion-based architectures have been widely studied, the compositional behavior of emerging Visual Autoregressive (VAR) models is still largely unexamined. We benchmark six diverse T2I systems - SDXL, PixArt-α, Flux-Dev, Flux-Schnell, Infinity-2B, and Infinity-8B - across the full T2I-CompBench++ and GenEval suites, evaluating alignment in color and attribute binding, spatial relations, numeracy, and complex multi-object prompts. Across both benchmarks, Infinity-8B achieves the strongest overall compositional alignment, while Infinity-2B also matches or exceeds larger diffusion models in several categories, highlighting favorable efficiency-performance trade-offs. In contrast, SDXL and PixArt-α show persistent weaknesses in attribute-sensitive and spatial tasks. These results provide the first systematic comparison of VAR and diffusion approaches to compositional alignment and establish unified baselines for the future development of the T2I model.
@article{arxiv.2512.11542,
title = {Infinity and Beyond: Compositional Alignment in VAR and Diffusion T2I Models},
author = {Hossein Shahabadi and Niki Sepasian and Arash Marioriyad and Ali Sharifi-Zarchi and Mahdieh Soleymani Baghshah},
journal= {arXiv preprint arXiv:2512.11542},
year = {2026}
}
Comments
Accepted at the ICLR 2026 Workshop on Multimodal Intelligence: Next Token Prediction and Beyond