Text-to-image (T2I) generation has been actively studied using Diffusion
Models and Autoregressive Models. Recently, Masked Generative Transformers have
gained attention as an alternative to Autoregressive Models to overcome the
inherent limitations of causal attention and autoregressive decoding through
bidirectional attention and parallel decoding, enabling efficient and
high-quality image generation. However, compositional T2I generation remains
challenging, as even state-of-the-art Diffusion Models often fail to accurately
bind attributes and achieve proper text-image alignment. While Diffusion Models
have been extensively studied for this issue, Masked Generative Transformers
exhibit similar limitations but have not been explored in this context. To
address this, we propose Unmasking with Contrastive Attention Guidance
(UNCAGE), a novel training-free method that improves compositional fidelity by
leveraging attention maps to prioritize the unmasking of tokens that clearly
represent individual objects. UNCAGE consistently improves performance in both
quantitative and qualitative evaluations across multiple benchmarks and
metrics, with negligible inference overhead. Our code is available at
https://github.com/furiosa-ai/uncage.