Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompts-particularly those involving multiple subjects with distinct attributes. Inspired by the human drawing process, which first outlines the composition and then incrementally adds details, we propose Detail++, a training-free framework that introduces a novel Progressive Detail Injection (PDI) strategy to address this limitation. Specifically, we decompose a complex prompt into a sequence of simplified sub-prompts, guiding the generation process in stages. This staged generation leverages the inherent layout-controlling capacity of self-attention to first ensure global composition, followed by precise refinement. To achieve accurate binding between attributes and corresponding subjects, we exploit cross-attention mechanisms and further introduce a Centroid Alignment Loss at test time to reduce binding noise and enhance attribute consistency. Extensive experiments on T2I-CompBench and a newly constructed style composition benchmark demonstrate that Detail++ significantly outperforms existing methods, particularly in scenarios involving multiple objects and complex stylistic conditions.

With the increasing demand for personalized health monitoring through wearable devices, there is a growing need for non-prescription ECG diagnosing, especially during physical activities. However, existing abnormal ECG data are typically measured in clinical settings, reflecting heart waveforms in a resting state. Abnormality classification models based on such data often struggle to maintain high performance during physical activities, leading to increased false alarms and a higher incidence of missed detections. Due to the potential risks associated with having patients engage in physical activity, abnormal ECG data captured during exercise is not readily available, further complicating the development of reliable models for active scenarios. To address this issue, we propose GAEA-Net in this study. Our goal is to utilize exercise ECGs from healthy individuals, which are more easily accessible, combined with resting-state abnormal ECGs, to generate activity-enriched ECGs through synthesis. We conduct abnormal classification on five widely used datasets, achieving average improvements of 1.3% in Accuracy, 1.3% in F1-score, 0.9% in AUROC, 1.6% in MCC, and 1.4% in Cohen's Kappa. Furthermore, a clinical Turing test involving seven experienced cardiologists confirms that our synthesized ECGs exhibit high fidelity. In the diagnostic task, the cardiologists achieved comparable accuracy on synthetic and real ECGs (55.7% vs. 54.9%, p = 0.76).