Through systematic quantitative and qualitative analysis, the paper comprehensively validates the outstanding performance of its large-scale training paradigm in the zero-shot setting. In quantitative evaluation, the model, without using any training labels from benchmark datasets such as MS-COCO, achieves generation quality (measured by FID/IS metrics) comparable to or even better than domain-specific models trained specifically on those datasets. Crucially, human evaluation shows that human raters prefer DALL·E's results in approximately 90% of cases. The paper also validates the fairness of the evaluation through analyses such as Gaussian blur and thoroughly explores the model's emergent advanced capabilities in concept composition, attribute binding, and more. These experiments rigorously confirm the paper's core thesis: when model and data scale are sufficiently large, a simple two-stage architecture can achieve powerful generalization and creative capabilities without domain-specific fine-tuning.