Existing Text-to-Image (T2I) benchmarks remain anchored in foundational criteria such as image quality and text-image alignment, failing to capture the nuanced capabilities that professional creators demand in real-world workflows. Meanwhile, evaluation pipelines that rely on an untrained multimodal model as a judge diverge from human standards. We introduce Qwen-Image-Bench, a creator-centric benchmark that extends evaluation with two application-driven dimensions (Real-world Fidelity and Creative Generation), alongside the conventional pillars of Quality, Aesthetics, and Alignment. The benchmark organizes these into a three-level taxonomy (5 pillars, 23 sub-capabilities, 56 verifiable rubrics) and provides 1000 stratified bilingual prompts. We train a unified Judge Model (Q-Judger) on Qwen3.6-27B, supervised by 80 professional annotators under blind labeling and triple-review protocols, that scores every image across all 56 facets with fine-grained, rubric-grounded diagnostics. Empirically, Qwen-Image-Bench reliably distinguishes leading T2I models, achieving the greatest separation on the application-driven dimensions where existing benchmarks provide limited insight.

Recent advances in image generation and editing have been driven by powerful pretrained unified models, yet most existing approaches remain fundamentally raster-centric: they represent images as opaque pixels without natively modeling transparency. This limitation restricts their applicability to a broad range of real-world applications. In this work, we present Qwen-Image-Layered 1.5, a pretrained unified image generation and editing model with native transparency awareness. By jointly modeling visual appearance and transparency, our framework supports a wide range of both raster-centric and transparency-aware tasks within a single model. Notably, we observe that the model can perform transparent image editing in a zero-shot manner. To systematically evaluate this new task, we introduce TransEdit-Bench, the first benchmark specifically designed for transparent image editing. Extensive experiments across various tasks demonstrate strong performance and highlight the great potential of transparency-native image modeling as a unified paradigm for future image synthesis and editing.