The fundamental requirement for text-to-image generation is aligning the generated images with the provided text. With large-scale data, pre-trained Stable Diffusion (SD) models have achieved remarkable performance in this task. These models process an input prompt as text control, guiding a vision model to perform denoising operations that recover a clean image from pure noise. However, we observe that when there is correlation among text tokens, SD’s generated images fail to accurately represent the semantics of the input prompt: simple yet crucial objects may be omitted, thereby disrupting text-image alignment. We refer to this problem as "object omission". Without additional external knowledge, previous methods have been ineffective at addressing this issue. To investigate this problem, we analyze the attention maps in SD and find that biased text representations mislead the visual denoising process when handling correlated tokens, impeding object generation. Moreover, we observe that even when two prompts share the same semantics, slight variations in token sequence significantly alter attention scores, consequently affecting the final generated images. Based on these findings, we propose a simple yet effective fine-tuning method that applies decorrelation to the self-attention maps in the text module, thus reducing dependencies between tokens. Our approach requires no external prior knowledge, is straightforward to implement, and operates solely on the text module of the SD model. Extensive experiments confirm that our method effectively alleviates the object omission problem under text correlations, thereby enhancing text-image alignment.